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Sensors, Volume 17, Issue 3 (March 2017) – 233 articles

Cover Story (view full-size image): Sensing selectivity (data on the foreground) of a concentric-electrode organic electrochemical transistor output (OECT device on the background), for different diluted analytes. A sketch of the 3-electrode device is depicted to illustrate the coupling between the selective oxidation at the gate and the dedoping of the transistor channel, responsible for an effective detection-transduction in the OECT sensor. View this paper
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10936 KiB  
Article
Accurate Determination of the Frequency Response Function of Submerged and Confined Structures by Using PZT-Patches†
by Alexandre Presas, David Valentin, Eduard Egusquiza, Carme Valero, Mònica Egusquiza and Matias Bossio
Sensors 2017, 17(3), 660; https://doi.org/10.3390/s17030660 - 22 Mar 2017
Cited by 50 | Viewed by 8911
Abstract
To accurately determine the dynamic response of a structure is of relevant interest in many engineering applications. Particularly, it is of paramount importance to determine the Frequency Response Function (FRF) for structures subjected to dynamic loads in order to avoid resonance and fatigue [...] Read more.
To accurately determine the dynamic response of a structure is of relevant interest in many engineering applications. Particularly, it is of paramount importance to determine the Frequency Response Function (FRF) for structures subjected to dynamic loads in order to avoid resonance and fatigue problems that can drastically reduce their useful life. One challenging case is the experimental determination of the FRF of submerged and confined structures, such as hydraulic turbines, which are greatly affected by dynamic problems as reported in many cases in the past. The utilization of classical and calibrated exciters such as instrumented hammers or shakers to determine the FRF in such structures can be very complex due to the confinement of the structure and because their use can disturb the boundary conditions affecting the experimental results. For such cases, Piezoelectric Patches (PZTs), which are very light, thin and small, could be a very good option. Nevertheless, the main drawback of these exciters is that the calibration as dynamic force transducers (relationship voltage/force) has not been successfully obtained in the past. Therefore, in this paper, a method to accurately determine the FRF of submerged and confined structures by using PZTs is developed and validated. The method consists of experimentally determining some characteristic parameters that define the FRF, with an uncalibrated PZT exciting the structure. These parameters, which have been experimentally determined, are then introduced in a validated numerical model of the tested structure. In this way, the FRF of the structure can be estimated with good accuracy. With respect to previous studies, where only the natural frequencies and mode shapes were considered, this paper discuss and experimentally proves the best excitation characteristic to obtain also the damping ratios and proposes a procedure to fully determine the FRF. The method proposed here has been validated for the structure vibrating in air comparing the FRF experimentally obtained with a calibrated exciter (impact Hammer) and the FRF obtained with the described method. Finally, the same methodology has been applied for the structure submerged and close to a rigid wall, where it is extremely important to not modify the boundary conditions for an accurate determination of the FRF. As experimentally shown in this paper, in such cases, the use of PZTs combined with the proposed methodology gives much more accurate estimations of the FRF than other calibrated exciters typically used for the same purpose. Therefore, the validated methodology proposed in this paper can be used to obtain the FRF of a generic submerged and confined structure, without a previous calibration of the PZT. Full article
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<p>(<b>a</b>) Tested structure with installed PZT; and (<b>b</b>) installed Accelerometer (back side of the disk).</p>
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<p>Equipment used.</p>
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<p>Time signals: (<b>a</b>) chirp excitation (PZT) and response; and (<b>b</b>) hammer excitation and response.</p>
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<p>Sweep excitation (PZT): excitation and response.</p>
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<p>Tested Structure (disk) submerged in water with a nearby rigid wall.</p>
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<p>Obtaining the FRF: (<b>a</b>) One average of the frequency signals of Accelerometer and Hammer after applying the FFT; and (<b>b</b>) FRF estimated with 5 averages (impacts) and coherence.</p>
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<p>Comparison of the FRF obtained with the Hammer and the transfer function ((m/s<sup>2</sup>)/V) obtained with the PZT (chirp excitation).</p>
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<p>(<b>a</b>) First Mode Shape of the analyzed structure with measured points; (<b>b</b>) Representation of <math display="inline"> <semantics> <mrow> <mi>H</mi> <mrow> <mo>(</mo> <mrow> <mi>j</mi> <msub> <mi>ω</mi> <mi>r</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> for the points with maximal deformation (Hammer and PZT).</p>
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<p>Damping Ratio estimation with the half power method for the FRF and for the transfer function ((m/s<sup>2</sup>)/V) obtained with the PZT (chirp excitation).</p>
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<p>(<b>a</b>) Comparison PZT-Transfer function within Sweep and Chirp; and (<b>b</b>) detailed Time–Frequency representation of the peak hold method analysis for the sweep excitation.</p>
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<p>Flowchart of the proposed method to estimate the FRF using PZTs and a numerical simulation model.</p>
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<p>(<b>a</b>) Simulation model including the position of the force and the measurement point; and (<b>b</b>) application of the harmonic response compared to experimental result for the 3rd mode.</p>
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<p>Comparison FRF obtained with Hammer and FRF estimated with the PZT combined with the proposed method. Structure suspended in air.</p>
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<p>Simulation model used: (<b>a</b>) Disk with “infinite” water medium; and (<b>b</b>) disk close to rigid wall (25 mm).</p>
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<p>(<b>a</b>) Comparison PZT transfer function with FRF; and (<b>b</b>) comparison FRF estimated with the PZT and FRF obtained with the Hammer. Structure with infinite water medium.</p>
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<p>Natural Frequency and Damping estimation of the fourth mode of the disk submerged in water close to a rigid wall.</p>
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<p>(<b>a</b>) Comparison PZT transfer function with FRF; and (<b>b</b>) comparison FRF estimated with the PZT and FRF obtained with the Hammer. The Structure is close to a rigid wall.</p>
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<p>Comparison modal parameters for the four analyzed modes. Ratio modal parameter estimated with the Hammer against modal parameter estimated with the proposed method (using PZTs: (<b>a</b>) Natural Frequency; (<b>b</b>) Damping Factor; and (<b>c</b>) Amplitude FRF.</p>
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<p>Example of a submerged-confined structure (reversible Francis turbine).</p>
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19818 KiB  
Article
Shadow Detection Based on Regions of Light Sources for Object Extraction in Nighttime Video
by Gil-beom Lee, Myeong-jin Lee, Woo-Kyung Lee, Joo-heon Park and Tae-Hwan Kim
Sensors 2017, 17(3), 659; https://doi.org/10.3390/s17030659 - 22 Mar 2017
Cited by 10 | Viewed by 7081
Abstract
Intelligent video surveillance systems detect pre-configured surveillance events through background modeling, foreground and object extraction, object tracking, and event detection. Shadow regions inside video frames sometimes appear as foreground objects, interfere with ensuing processes, and finally degrade the event detection performance of the [...] Read more.
Intelligent video surveillance systems detect pre-configured surveillance events through background modeling, foreground and object extraction, object tracking, and event detection. Shadow regions inside video frames sometimes appear as foreground objects, interfere with ensuing processes, and finally degrade the event detection performance of the systems. Conventional studies have mostly used intensity, color, texture, and geometric information to perform shadow detection in daytime video, but these methods lack the capability of removing shadows in nighttime video. In this paper, a novel shadow detection algorithm for nighttime video is proposed; this algorithm partitions each foreground object based on the object’s vertical histogram and screens out shadow objects by validating their orientations heading toward regions of light sources. From the experimental results, it can be seen that the proposed algorithm shows more than 93.8% shadow removal and 89.9% object extraction rates for nighttime video sequences, and the algorithm outperforms conventional shadow removal algorithms designed for daytime videos. Full article
(This article belongs to the Section Physical Sensors)
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<p>Video content analytics algorithm based on background subtraction.</p>
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<p>Foreground region extracted in a video content analytics algorithm: (<b>a</b>) Input video frame; (<b>b</b>) Foreground region extracted.</p>
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<p>Shapes of objects and their shadow regions in nighttime video sequences.</p>
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<p>Video content analytics with the proposed shadow detection algorithm for nighttime video sequences.</p>
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<p>Vertical histogram of a foreground region: (<b>a</b>) A foreground region; (<b>b</b>) vertical histogram.</p>
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<p>Partitioning of a foreground region by scanning the vertical histogram: (<b>a</b>) Foreground regions; (<b>b</b>) Foreground pixel histograms and the partitioned results for three scanning methods.</p>
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<p>Foreground region partitioned into two smaller regions: (<b>a</b>) A foreground region extracted; (<b>b</b>) Partitioned regions after the double column scan partitioning.</p>
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<p>Finding the direction of the major axis of a matched ellipse.</p>
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<p>Calculation of the major axis of each partitioned object after vertical histogram analysis: (<b>a</b>) Partitioned objects; (<b>b</b>) Major axes of the virtual matched ellipses.</p>
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<p>Possible regions of light sources for the proposed shadow detection algorithm.</p>
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<p>Results in each step in the proposed shadow detection algorithm: (<b>a</b>) Sequence S5, frame 1280; (<b>b</b>) Sequence S3, frame 826.</p>
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<p>Shadow removal result 1 for a foreground object (120 × 90 pixels from sequence S3): (<b>a</b>) A foreground object; (<b>b</b>) Partitioned objects; (<b>c</b>) A foreground object after shadow removal.</p>
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<p>Shadow removal result 2 for a foreground object (87 × 84 pixels from sequence S3): (<b>a</b>) A foreground object; (<b>b</b>) Partitioned objects; (<b>c</b>) A foreground object after shadow removal.</p>
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<p>Shadow removal result 3 for multiple foreground objects (140 × 105 pixels from sequence S4): (<b>a</b>) Foreground objects; (<b>b</b>) Partitioned objects; (<b>c</b>) Foreground objects after shadow removal.</p>
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<p>Shadow removal result 4 for multiple foreground objects (140 × 105 pixels from sequence S4): (<b>a</b>) Foreground objects; (<b>b</b>) Partitioned objects; (<b>c</b>) Foreground objects after shadow removal.</p>
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<p>Shadow removal result 5 for a foreground object (240 × 180 pixels from sequence S2): (<b>a</b>) Foreground objects; (<b>b</b>) Partitioned objects (<b>c</b>) Foreground object after shadow removal.</p>
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<p>Shadow removal results for various nighttime and one daytime video sequences.</p>
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2907 KiB  
Article
Technical Aspects and Validation of a New Biofeedback System for Measuring Lower Limb Loading in the Dynamic Situation
by Marco Raaben, Herman R. Holtslag, Robin Augustine, Rutger O. Van Merkerk, Bart F. J. M. Koopman and Taco J. Blokhuis
Sensors 2017, 17(3), 658; https://doi.org/10.3390/s17030658 - 22 Mar 2017
Cited by 8 | Viewed by 5241
Abstract
Background: A variety of techniques for measuring lower limb loading exists, each with their own limitations. A new ambulatory biofeedback system was developed to overcome these limitations. In this study, we described the technical aspects and validated the accuracy of this system. Methods: [...] Read more.
Background: A variety of techniques for measuring lower limb loading exists, each with their own limitations. A new ambulatory biofeedback system was developed to overcome these limitations. In this study, we described the technical aspects and validated the accuracy of this system. Methods: A bench press was used to validate the system in the static situation. Ten healthy volunteers were measured by the new biofeedback system and a dual-belt instrumented treadmill to validate the system in the dynamic situation. Results: Bench press results showed that the sensor accurately measured peak loads up to 1000 N in the static situation. In the healthy volunteers, the load curves measured by the biofeedback system were similar to the treadmill. However, the peak loads and loading rates were lower in the biofeedback system in all participants at all speeds. Conclusions: Advanced sensor technologies used in the new biofeedback system resulted in highly accurate measurements in the static situation. The position of the sensor and the design of the biofeedback system should be optimized to improve results in the dynamic situation. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic representation of the biofeedback system with the five components: the insole sensor sends load measurements wireless and in real-time to a wrist device (Sensi), which acts as feedback instrument for the patient. These load measurements are then sent wireless and in real-time to a tablet, which acts as feedback instrument for the healthcare professional. The healthcare professional sets the desired target load and margins in the tablet (StepApp). Finally, all sessions are saved on a secured Web Portal.</p>
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<p>Schematic representation of the sensor. The sensor consists of six different parts: (1) the cover and (2) bottom plate consist of high quality non-ferromagnetic aluminum 7075 (AlZn5,5MgCu), (3) the laser-cut flat spring is made of strong and non-corrosive stainless steel (RVS AISI 301), (4) the N45 magnets are made of NdFeB (Neodymium–Iron–Boron), (5) the Hall sensors (Honeywell SS49E), and (6) a U3-HV USB data acquisition module (LabJack, Lakewood, CO, USA, not shown in figure). A force exerted on the cover plate (1) will be transferred via the beads to the four flat springs (3). Depending on the applied force, these springs will bend more or less, which results in movement of the magnets (4) and thus a change in the magnetic field at each of the Hall sensors (5). This creates changes in the output signal of the Hall sensors, which makes it possible to calculate the total force exerted on the spring by using the correct signal processing and algorithm.</p>
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<p>Validation of the sensor in the static situation using a bench press. The sensor (3–5) was placed between a solid underground (6) and a solid plate (2) to increase the area of applied force by the bench press (1). Incremental forces from 0 to 1000 N were applied to the sensor. For this experimental set-up a cable (7) was connected from the sensor to a laptop to analyze the data in the static situation.</p>
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<p>Validation of the sensor in the static situation. (<b>a,b</b>) Tests at the University Medical Center Utrecht (n = 2) and Technical University Delft (n = 2). The black dots indicate the applied force by the bench press versus the measured force by the sensor; (<b>c</b>) Sensor validation test at a broad range of temperatures (0–55 °C) at constant force (750 N). The black dots indicate the force measured by the sensor. The intermittent line represents the applied force by the bench press (750 N) with the 10% error range marked by dotted line. In the 20–37 °C range, the deviation was 1% at maximum. At extreme temperatures, the error fluctuates between +8% and −9%, but remains within the range of 10%; (<b>d</b>) Cyclic loading at 20 °C. Cyclic fluctuating forces between 70 N and 860 N, highlighted by the intermittent line, were applied to the sensor. The error between applied force and measured force was negligible (&lt;1%).</p>
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<p>Validation of the biofeedback system in the dynamic situation at different speeds. <b>Left</b>: peak load (N) measured by the R-Mill vs. peak load (N) measured by the biofeedback system. Middle: loading rate (N/ms) measured by the R-Mill vs. loading rate (N/ms) measured by the biofeedback system. <b>Right</b>: overlay of the load curves (n = 10) measured by the R-Mill (red) and the biofeedback system (green). Legend: - - - Linear curve, <span class="html-fig-inline" id="sensors-17-00658-i001"> <img alt="Sensors 17 00658 i001" src="/sensors/sensors-17-00658/article_deploy/html/images/sensors-17-00658-i001.png"/></span> Observed curve, ● R-Mill vs. biofeedback system.</p>
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4806 KiB  
Article
Monitoring Concrete Deterioration Due to Reinforcement Corrosion by Integrating Acoustic Emission and FBG Strain Measurements
by Weijie Li, Changhang Xu, Siu Chun Michael Ho, Bo Wang and Gangbing Song
Sensors 2017, 17(3), 657; https://doi.org/10.3390/s17030657 - 22 Mar 2017
Cited by 129 | Viewed by 8173
Abstract
Corrosion of concrete reinforcement members has been recognized as a predominant structural deterioration mechanism for steel reinforced concrete structures. Many corrosion detection techniques have been developed for reinforced concrete structures, but a dependable one is more than desired. Acoustic emission technique and fiber [...] Read more.
Corrosion of concrete reinforcement members has been recognized as a predominant structural deterioration mechanism for steel reinforced concrete structures. Many corrosion detection techniques have been developed for reinforced concrete structures, but a dependable one is more than desired. Acoustic emission technique and fiber optic sensing have emerged as new tools in the field of structural health monitoring. In this paper, we present the results of an experimental investigation on corrosion monitoring of a steel reinforced mortar block through combined acoustic emission and fiber Bragg grating strain measurement. Constant current was applied to the mortar block in order to induce accelerated corrosion. The monitoring process has two aspects: corrosion initiation and crack propagation. Propagation of cracks can be captured through corresponding acoustic emission whereas the mortar expansion due to the generation of corrosion products will be monitored by fiber Bragg grating strain sensors. The results demonstrate that the acoustic emission sources comes from three different types, namely, evolution of hydrogen bubbles, generation of corrosion products and crack propagation. Their corresponding properties are also discussed. The results also show a good correlation between acoustic emission activity and expansive strain measured on the specimen surface. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic diagram of corrosion-induced concrete cracking process: (<b>a</b>) initial unrestrained reinforced concrete; (<b>b</b>) internal expansive pressure for concrete in restrained condition; (<b>c</b>) corrosion products deposited within open cracks.</p>
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<p>Reinforced mortar specimen and sensor installation.</p>
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<p>Reinforced mortar specimen and sensor installation.</p>
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<p>Amplitude from both transducers at different locations.</p>
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<p>Waveforms from pencil lead break tests for both transducers.</p>
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<p>AE hits during the accelerated corrosion test.</p>
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<p>Peak frequency content from Transducer A.</p>
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<p>Typical waveforms of different types and their fast Fourier transform (FFT).</p>
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<p>3D cross-plot of duration, energy and signal strength and its perspective plots.</p>
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<p>Strain history from FBG strain measurement.</p>
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<p>Illustration of cracking of the specimen.</p>
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<p>Simultaneous evaluation of acoustic emission activity and strain measurement.</p>
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6756 KiB  
Article
Flexible Piezoresistive Sensors Embedded in 3D Printed Tires
by Md Omar Faruk Emon and Jae-Won Choi
Sensors 2017, 17(3), 656; https://doi.org/10.3390/s17030656 - 22 Mar 2017
Cited by 46 | Viewed by 9851
Abstract
In this article, we report the development of a flexible, 3D printable piezoresistive pressure sensor capable of measuring force and detecting the location of the force. The multilayer sensor comprises of an ionic liquid-based piezoresistive intermediate layer in between carbon nanotube (CNT)-based stretchable [...] Read more.
In this article, we report the development of a flexible, 3D printable piezoresistive pressure sensor capable of measuring force and detecting the location of the force. The multilayer sensor comprises of an ionic liquid-based piezoresistive intermediate layer in between carbon nanotube (CNT)-based stretchable electrodes. A sensor containing an array of different sensing units was embedded on the inner liner surface of a 3D printed tire to provide with force information at different points of contact between the tire and road. Four scaled tires, as well as wheels, were 3D printed using a flexible and a rigid material, respectively, which were later assembled with a 3D-printed chassis. Only one tire was equipped with a sensor and the chassis was driven through a motorized linear stage at different speeds and load conditions to evaluate the sensor performance. The sensor was fabricated via molding and screen printing processes using a commercially available 3D-printable photopolymer as 3D printing is our target manufacturing technique to fabricate the entire tire assembly with the sensor. Results show that the proposed sensors, inserted in the 3D printed tire assembly, could detect forces, as well as their locations, properly. Full article
(This article belongs to the Special Issue 3D Printed Sensors)
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<p>(<b>a</b>) A single taxel sensor connected to a half-Wheatstone bridge circuit supplied with the DC voltage; (<b>b</b>) detailed view of the cross-section of a taxel with different layers; and (<b>c</b>) a simplified equivalent circuit showing resistance of each layer.</p>
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<p>Sensor fabrication steps: (<b>a</b>) TangoPlus is poured into mold to create bottom insulation layer; (<b>b</b>) UV curing process for the bottom insulation layer; (<b>c</b>) screen printing of MWNT/polymer paste to create the first electrode layer; (<b>d</b>) thermal curing of MWNT/polymer electrodes; (<b>e</b>) IL/polymer composite poured into mold to create a piezoresistive intermediate layer; (<b>f</b>) UV curing of the IL/polymer layer; (<b>g</b>) screen printing of the MWNT/polymer paste to create a second electrode layer; (<b>h</b>) thermal curing of MWNT electrodes; (<b>i</b>) TangoPlus poured to create the top insulation layer; (<b>j</b>) UV curing of the top layer; and (<b>k</b>) an exploded view of sensor.</p>
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<p>(<b>a</b>) Tire with slot for sensor; (<b>b</b>) wheel with hole for wiring; (<b>c</b>) tire-wheel assembly; (<b>d</b>) sectional view of the assembly; and (<b>e</b>) tire, wheel, and chassis assembled.</p>
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<p>Wiring diagram of a twelve (2 × 6) taxel sensor where each taxel is connected to a half-Wheatstone bridge circuit. For the operational amplifier (OPA551PA), the supply voltage range was +24 to −24 V, and the input voltage range was +10 to −10 V.</p>
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<p>(<b>a</b>) Six-taxel (1 × 6) sensor; (<b>b</b>) 6-taxel sensor with bead; (<b>c</b>) 12-taxel (2 × 6) sensor; and (<b>d</b>) bead attached on each taxel.</p>
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<p>3D printed tire assembly. (<b>a</b>) 3D printed tire and wheel; (<b>b</b>) slot for the sensor inside the tire; (<b>c</b>) the assembled tire on the wheel; and (<b>d</b>) the 3D-printed chassis</p>
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<p>(<b>a</b>) Twelve-taxel sensor attached on the wheel; (<b>b</b>) sensor connected and inserted inside the tire; and (<b>c</b>) the fully assembled experimental setup with the motorized linear stage.</p>
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<p>Changes in voltage output versus time for different load conditions: Voltage output with the speed of (<b>a</b>) 5 mm/s; and (<b>b</b>) 10 mm/s; (<b>c</b>) 50 mm/s</p>
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<p>(<b>a</b>) Twelve locations on the tire were marked corresponding to 12 taxels; (<b>b</b>) the tire rotates while taxels or location 7 and 8 hit the ground; and (<b>c</b>) bar plot indicates ∆V at each taxel when locations 7 and 8 hit ground.</p>
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<p>The car was loaded with a 0.38 kg weight and experimented at different speeds: the change in voltage output versus time for 12-taxel sensor embedded in tire while car speed was (<b>a</b>) 5 mm/s; (<b>b</b>) 10 mm/s; and (<b>c</b>) 50 mm/s. (<b>d</b>–<b>f</b>) The locations of force shown in the bar plot at a certain time. (<b>g</b>–<b>i</b>) The speed of the car calculated at each row of taxel and compared with original speed.</p>
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<p>(<b>a</b>) Conventional sensor installation on tire; and (<b>b</b>) proposed direct-print photopolymerization of MWNT-based electrodes on the conformal inner liner surface of the tire.</p>
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4635 KiB  
Article
Error Analysis and Calibration Method of a Multiple Field-of-View Navigation System
by Shuai Shi, Kaichun Zhao, Zheng You, Chenguang Ouyang, Yongkui Cao and Zhenzhou Wang
Sensors 2017, 17(3), 655; https://doi.org/10.3390/s17030655 - 22 Mar 2017
Cited by 5 | Viewed by 3908
Abstract
The Multiple Field-of-view Navigation System (MFNS) is a spacecraft subsystem built to realize the autonomous navigation of the Spacecraft Inside Tiangong Space Station. This paper introduces the basics of the MFNS, including its architecture, mathematical model and analysis, and numerical simulation of system [...] Read more.
The Multiple Field-of-view Navigation System (MFNS) is a spacecraft subsystem built to realize the autonomous navigation of the Spacecraft Inside Tiangong Space Station. This paper introduces the basics of the MFNS, including its architecture, mathematical model and analysis, and numerical simulation of system errors. According to the performance requirement of the MFNS, the calibration of both intrinsic and extrinsic parameters of the system is assumed to be essential and pivotal. Hence, a novel method based on the geometrical constraints in object space, called checkerboard-fixed post-processing calibration (CPC), is proposed to solve the problem of simultaneously obtaining the intrinsic parameters of the cameras integrated in the MFNS and the transformation between the MFNS coordinate and the cameras’ coordinates. This method utilizes a two-axis turntable and a prior alignment of the coordinates is needed. Theoretical derivation and practical operation of the CPC method are introduced. The calibration experiment results of the MFNS indicate that the extrinsic parameter accuracy of the CPC reaches 0.1° for each Euler angle and 0.6 mm for each position vector component (1σ). A navigation experiment verifies the calibration result and the performance of the MFNS. The MFNS is found to work properly, and the accuracy of the position vector components and Euler angle reaches 1.82 mm and 0.17° (1σ) respectively. The basic mechanism of the MFNS may be utilized as a reference for the design and analysis of multiple-camera systems. Moreover, the calibration method proposed has practical value for its convenience for use and potential for integration into a toolkit. Full article
(This article belongs to the Section Physical Sensors)
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<p>(<b>a</b>) 3D model and (<b>b</b>) flying sketch of the SITSS.</p>
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<p>(<b>a</b>) Prototype and (<b>b</b>) navigation sketch of the MFNS.</p>
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<p>Pinhole imaging model.</p>
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<p>MC simulation result of the pixel error caused by the beacon error.</p>
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<p>MC simulation result of pixel error caused by the relative position error.</p>
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<p>MC simulation result of pixel error caused by the relative attitude error.</p>
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<p>Calibration architecture of MFNS.</p>
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<p>Process of MFNS calibration method.</p>
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<p>Images for calibration of camera X (<b>a</b>), camera Y (<b>b</b>), and camera Z (<b>c</b>) using Zhang’s method.</p>
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<p>Architecture of the navigation experiment.</p>
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<p>Results of the navigation experiment: (<b>a</b>) attitude error and (<b>b</b>) position error.</p>
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<p>Laser is installed on the turntable to adjust the orientation of the checkerboard.</p>
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453 KiB  
Article
Combined Pre-Distortion and Censoring for Bandwidth-Efficient and Energy-Efficient Fusion of Spectrum Sensing Information
by Guilherme Pedro Aquino, Dayan Adionel Guimarães, Luciano Leonel Mendes and Tales Cleber Pimenta
Sensors 2017, 17(3), 654; https://doi.org/10.3390/s17030654 - 22 Mar 2017
Cited by 6 | Viewed by 4365
Abstract
This paper describes a novel scheme for the fusion of spectrum sensing information in cooperative spectrum sensing for cognitive radio applications. The scheme combines a spectrum-efficient, pre-distortion-based fusion strategy with an energy-efficient censoring-based fusion strategy to achieve the combined effect of reduction in [...] Read more.
This paper describes a novel scheme for the fusion of spectrum sensing information in cooperative spectrum sensing for cognitive radio applications. The scheme combines a spectrum-efficient, pre-distortion-based fusion strategy with an energy-efficient censoring-based fusion strategy to achieve the combined effect of reduction in bandwidth and power consumption during the transmissions of the local decisions to the fusion center. Expressions for computing the key performance metrics of the spectrum sensing of the proposed scheme are derived and validated by means of computer simulations. An extensive analysis of the overall energy efficiency is made, along with comparisons with reference strategies proposed in the literature. It is demonstrated that the proposed fusion scheme can outperform the energy efficiency attained by these reference strategies. Moreover, it attains approximately the same global decision performance of the best among these strategies. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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<p>Probability density function of the noiseless received signal samples due to a single SU for <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>E</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>. The situation of no clipping is also shown for reference.</p>
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<p>Probability density function of the noiseless received signal samples at the FC, for <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>E</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>.</p>
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<p>ROC curves at the fusion center for <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> (left), <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> (right), <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> (top), <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mo>⌈</mo> <mi>M</mi> <mo>/</mo> <mn>2</mn> <mo>⌉</mo> </mrow> </semantics> </math> (middle) and <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mi>M</mi> </mrow> </semantics> </math> (bottom).</p>
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<p>Energy consumption during the report phase for <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>SU</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics> </math> dB and <math display="inline"> <semantics> <mrow> <mi>E</mi> <mo>=</mo> <mi>ξ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>.</p>
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<p>Error probability (top) and energy consumption (bottom) as a function of the clipping threshold <span class="html-italic">C</span>, for <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>SU</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics> </math> dB, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>FC</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> dB and <math display="inline"> <semantics> <mrow> <mi>E</mi> <mo>=</mo> <mi>ξ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>.</p>
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<p>Performance (left) and energy consumption (right) for the new fusion scheme and for the one proposed in [<a href="#B28-sensors-17-00654" class="html-bibr">28</a>], for <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>SU</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics> </math> dB, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>FC</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> dB and <math display="inline"> <semantics> <mrow> <mi>E</mi> <mo>=</mo> <mi>ξ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>.</p>
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<p>Energy consumption per <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi mathvariant="normal">b</mi> </msub> <msub> <mi>T</mi> <mi mathvariant="normal">t</mi> </msub> </mrow> </semantics> </math> fair opportunistic transmitted bits, for <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> (top-left), <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> (top-right) and <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> (bottom), <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>SU</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics> </math> dB, <math display="inline"> <semantics> <mrow> <msub> <mi>γ</mi> <mi>FC</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> dB and <math display="inline"> <semantics> <mrow> <mi>E</mi> <mo>=</mo> <mi>ξ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>.</p>
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<p>Lowest energy consumption per <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi mathvariant="normal">b</mi> </msub> <msub> <mi>T</mi> <mi mathvariant="normal">t</mi> </msub> </mrow> </semantics> </math> fair opportunistic transmitted bits (highest energy efficiency), for <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics> </math> for the new fusion scheme, <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics> </math> for the scheme of [<a href="#B28-sensors-17-00654" class="html-bibr">28</a>] and <math display="inline"> <semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> for the scheme of [<a href="#B26-sensors-17-00654" class="html-bibr">26</a>].</p>
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1050 KiB  
Article
Theoretical Study of Monolayer and Double-Layer Waveguide Love Wave Sensors for Achieving High Sensitivity
by Shuangming Li, Ying Wan, Chunhai Fan and Yan Su
Sensors 2017, 17(3), 653; https://doi.org/10.3390/s17030653 - 22 Mar 2017
Cited by 18 | Viewed by 4966
Abstract
Love wave sensors have been widely used for sensing applications. In this work, we introduce the theoretical analysis of the monolayer and double-layer waveguide Love wave sensors. The velocity, particle displacement and energy distribution of Love waves were analyzed. Using the variations of [...] Read more.
Love wave sensors have been widely used for sensing applications. In this work, we introduce the theoretical analysis of the monolayer and double-layer waveguide Love wave sensors. The velocity, particle displacement and energy distribution of Love waves were analyzed. Using the variations of the energy repartition, the sensitivity coefficients of Love wave sensors were calculated. To achieve a higher sensitivity coefficient, a thin gold layer was added as the second waveguide on top of the silicon dioxide (SiO2) waveguide–based, 36 degree–rotated, Y-cut, X-propagating lithium tantalate (36° YX LiTaO3) Love wave sensor. The Love wave velocity was significantly reduced by the added gold layer, and the flow of wave energy into the waveguide layer from the substrate was enhanced. By using the double-layer structure, almost a 72-fold enhancement in the sensitivity coefficient was achieved compared to the monolayer structure. Additionally, the thickness of the SiO2 layer was also reduced with the application of the gold layer, resulting in easier device fabrication. This study allows for the possibility of designing and realizing robust Love wave sensors with high sensitivity and a low limit of detection. Full article
(This article belongs to the Special Issue Acoustic Wave Resonator-Based Sensors)
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<p>Diagram of the Love wave sensor: (<b>a</b>) monolayer; (<b>b</b>) double-layer.</p>
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<p>Velocity of Love wave in (<b>a</b>) LiTaO<sub>3</sub>/SiO<sub>2</sub> device; (<b>b</b>) LiTaO<sub>3</sub>/SiO<sub>2</sub>/Au device.</p>
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<p>(<b>a</b>) Partial energy of Love wave in LiTaO<sub>3</sub>/SiO<sub>2</sub> device; (<b>b</b>) Partial energy of substrate in LiTaO<sub>3</sub>/SiO<sub>2</sub>/Au device.</p>
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<p>Sensitivity coefficient of Love wave devices: (<b>a</b>) LiTaO<sub>3</sub>/SiO<sub>2</sub> structure; (<b>b</b>) LiTaO<sub>3</sub>/SiO<sub>2</sub>/Au structure.</p>
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2856 KiB  
Article
A Fast Measuring Method for the Inner Diameter of Coaxial Holes
by Lei Wang, Fangyun Yang, Luhua Fu, Zhong Wang, Tongyu Yang and Changjie Liu
Sensors 2017, 17(3), 652; https://doi.org/10.3390/s17030652 - 22 Mar 2017
Cited by 5 | Viewed by 6890
Abstract
A new method for fast diameter measurement of coaxial holes is studied. The paper describes a multi-layer measuring rod that installs a single laser displacement sensor (LDS) on each layer. This method is easy to implement by rotating the measuring rod, and immune [...] Read more.
A new method for fast diameter measurement of coaxial holes is studied. The paper describes a multi-layer measuring rod that installs a single laser displacement sensor (LDS) on each layer. This method is easy to implement by rotating the measuring rod, and immune from detecting the measuring rod’s rotation angles, so all diameters of coaxial holes can be calculated by sensors’ values. While revolving, the changing angles of each sensor’s laser beams are approximately equal in the rod’s radial direction so that the over-determined nonlinear equations of multi-layer holes for fitting circles can be established. The mathematical model of the measuring rod is established, all parameters that affect the accuracy of measurement are analyzed and simulated. In the experiment, the validity of the method is verified, the inner diameter measuring precision of 28 μm is achieved by 20 μm linearity LDS. The measuring rod has advantages of convenient operation and easy manufacture, according to the actual diameters of coaxial holes, and also the varying number of holes, LDS’s mounting location can be adjusted for different parts. It is convenient for rapid diameter measurement in industrial use. Full article
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<p>Instrument configuration. (<b>1</b>) Measuring rod; (<b>2</b>) coaxial hole part; (<b>3</b>) LDS; (<b>4</b>) baffle; (<b>5</b>) vee block; (<b>6</b>) platform.</p>
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<p>The ideal measurement model.</p>
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<p>The Global Coordinate System and the Measuring Rod Coordinate System.</p>
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<p>The Distance between Laser Beam and Rotary Axis of the Measuring Rod.</p>
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<p>Angle between the laser beam and rotary axis.</p>
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<p>Spot trajectory formed by laser beams.</p>
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<p>The position relationship between <span class="html-italic">O</span><sub>s</sub>-<span class="html-italic">Z</span> and <span class="html-italic">O</span><sub>w</sub>-<span class="html-italic">Z</span>.</p>
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<p>Transformation of spatial circle.</p>
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<p>The radius error coursed by the relative position of the rod.</p>
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<p>The diameter measurement system for coaxial holes.</p>
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<p>The measurement results for different rotation times of the measuring rod.</p>
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4023 KiB  
Article
Indoor Positioning System Using Magnetic Field Map Navigation and an Encoder System
by Han-Sol Kim, Woojin Seo and Kwang-Ryul Baek
Sensors 2017, 17(3), 651; https://doi.org/10.3390/s17030651 - 22 Mar 2017
Cited by 56 | Viewed by 9657
Abstract
In the indoor environment, variation of the magnetic field is caused by building structures, and magnetic field map navigation is based on this feature. In order to estimate position using this navigation, a three-axis magnetic field must be measured at every point to [...] Read more.
In the indoor environment, variation of the magnetic field is caused by building structures, and magnetic field map navigation is based on this feature. In order to estimate position using this navigation, a three-axis magnetic field must be measured at every point to build a magnetic field map. After the magnetic field map is obtained, the position of the mobile robot can be estimated with a likelihood function whereby the measured magnetic field data and the magnetic field map are used. However, if only magnetic field map navigation is used, the estimated position can have large errors. In order to improve performance, we propose a particle filter system that integrates magnetic field map navigation and an encoder system. In this paper, multiple magnetic sensors and three magnetic field maps (a horizontal intensity map, a vertical intensity map, and a direction information map) are used to update the weights of particles. As a result, the proposed system estimates the position and orientation of a mobile robot more accurately than previous systems. Also, when the number of magnetic sensors increases, this paper shows that system performance improves. Finally, experiment results are shown from the proposed system that was implemented and evaluated. Full article
(This article belongs to the Section Physical Sensors)
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<p>The magnetic-field map-building system.</p>
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<p>The test area for magnetic field map navigation: (<b>a</b>) map of the test area divided into four sections: A, B, C, and D; and (<b>b</b>) photos of the test corridor.</p>
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<p>The magnetic field maps. (<b>a</b>) The <span class="html-italic">X</span>-<span class="html-italic">Y</span> magnetic field map, which is the intensity magnetic field map of the horizontal plane; (<b>b</b>) <span class="html-italic">Z</span>-direction magnetic field map; (<b>c</b>) Direction of the magnetic field in the horizontal plane.</p>
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<p>The histograms of the magnetic field maps. (<b>a</b>) The <span class="html-italic">X</span>-<span class="html-italic">Y</span> magnetic field map; (<b>b</b>) <span class="html-italic">Z</span>-direction magnetic field map; (<b>c</b>) Direction magnetic field map in the horizontal plane.</p>
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<p>The various frames.</p>
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<p>The updated importance weights in the measurement update step.</p>
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<p>The proposed system flowchart.</p>
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<p>Block diagram of the proposed system and the reference system.</p>
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<p>The mobile robot system. (<b>a</b>) Section 1 is the measurement and data transmission unit, and Section 2 is the control unit; (<b>b</b>) The magnetic sensor array board.</p>
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<p>The estimated position of the mobile robot.</p>
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<p>The position error of the mobile robot. (<b>a</b>) Comparison of the odometry position errors and the proposed method’s position errors; (<b>b</b>) The distance errors of the proposed method. A, B, C, and D are the sections of the test corridor, as shown in <a href="#sensors-17-00651-f002" class="html-fig">Figure 2</a>.</p>
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<p>The estimated orientation from the proposed method. A, B, C, and D represent the sections of the test corridor, as shown in <a href="#sensors-17-00651-f002" class="html-fig">Figure 2</a>. (<b>a</b>) The estimated orientation and the reference orientation; (<b>b</b>) The errors from the estimated orientation.</p>
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<p>The results depending on the number of sensors used. The upper figure shows the mean of the distance error for the estimated position. The mean of the orientation error is shown in the lower figure.</p>
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<p>The maximum and minimum orientation error results depending on the number of sensors used.</p>
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11382 KiB  
Article
The Optimization and Characterization of an RNA-Cleaving Fluorogenic DNAzyme Probe for MDA-MB-231 Cell Detection
by Pengcheng Xue, Shengnan He, Yu Mao, Long Qu, Feng Liu, Chunyan Tan, Yuyang Jiang and Ying Tan
Sensors 2017, 17(3), 650; https://doi.org/10.3390/s17030650 - 21 Mar 2017
Cited by 7 | Viewed by 5343
Abstract
Breast cancer is one of the most frequently diagnosed cancers in females worldwide and lacks specific biomarkers for early detection. In a previous study, we obtained a selective RNA-cleaving Fluorogenic DNAzyme (RFD) probe against MDA-MB-231 cells, typical breast cancer cells, through the systematic [...] Read more.
Breast cancer is one of the most frequently diagnosed cancers in females worldwide and lacks specific biomarkers for early detection. In a previous study, we obtained a selective RNA-cleaving Fluorogenic DNAzyme (RFD) probe against MDA-MB-231 cells, typical breast cancer cells, through the systematic evolution of ligands by exponential process (SELEX). To improve the performance of this probe for actual application, we carried out a series of optimization experiments on the pH value of a reaction buffer, the type and concentration of cofactor ions, and sequence minimization. The length of the active domain of the probe reduced to 25 nt from 40 nt after optimization, which was synthesized more easily and economically. The detection limit of the optimized assay system was 2000 MDA-MB-231 cells in 30 min, which is more sensitive than the previous one (almost 5000 cells). The DNAzyme probe was also capable of distinguishing MDA-MB-231 cell specifically from 3 normal cells and 10 other tumor cells. This probe with high sensitivity, selectivity, and economic efficiency enhances the feasibility for further clinical application in breast cancer diagnosis. Herein, we developed an optimization system to produce a general strategy to establish an easy-to-use DNAzyme-based assay for other targets. Full article
(This article belongs to the Special Issue Aptasensors 2016)
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<p>Optimization of the reaction buffer. 10% dPAGE analysis of probe incubated with MDA-MB-231 cell lysate for 60 min. (<b>a</b>) The cleavage efficiency of AAI2-5 with different pH environments; (<b>b</b>) The cleavage efficiency of AAI2-5 with different divalent ions in reaction buffer; (<b>c</b>) The cleavage efficiency of different Mg<sup>2+</sup> concentrations. Clv% = cleavage efficiency; Unclv = uncleaved probe; Clv = cleaved probe; C.F = cofactor types; C (mM) = cofactor concentration</p>
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<p>The original sequence of AAI2-5 (A0) which was divided into four parts including primer sequences P1 and P2, substrate, and active domain (italic letters) which was further divided into eight units.</p>
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<p>The cleavage efficiency of the original sequence (A0) and two sequences without primers (AP1 and AP2). 10% dPAGE analysis of each probe incubated with MDA-MB-231 cell lysate for 60 min. A0 = AAI2-5; Clv% = cleavage efficiency; Unclv = uncleaved probe; Clv = cleaved probe.</p>
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<p>The truncated sequences A1–A8 and the activity of each sequence. 10% dPAGE analysis of each probe incubated with MDA-MB-231 cell lysate for 60 min. Clv% = cleavage efficiency; Unclv = uncleaved probe; Clv = cleaved probe.</p>
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<p>The truncated sequences A9–A14 and the activity of each sequence. 10% dPAGE analysis of each probe incubated with MDA-MB-231 cell lysate for 60 min. Clv% = cleavage efficiency; Unclv = uncleaved probe; Clv = cleaved probe.</p>
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<p>The sensitivity of two probes. 10% dPAGE analysis of each probe incubated with MDA-MB-231 cell lysate for 60 min. (<b>a</b>) A13 and (<b>b</b>) A0 were incubated with different MDA-MB-231 cell numbers; (<b>c</b>) A bar graph of the two probes' detection limit. Clv% = cleavage efficiency; Unclv = uncleaved probe; Clv = cleaved probe.</p>
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<p>The specificity of A13. (<b>a</b>,<b>b</b>) A13 was incubated with 60 μg/mL of different breast cell line lysates for 30 min. MCF-10A are normal breast cells, while others are breast cancer cells. (<b>c</b>,<b>d</b>) A13 was incubated with 60 μg/mL of different cell line lysate for 30 min. QSG 7701 is normal human liver cell line, HEB is normal human brain cell line, and other cells are cancer cell lines. Clv% = cleavage efficiency; Unclv = uncleaved probe; Clv = cleaved probe.</p>
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<p>The working principle of the probe.</p>
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5667 KiB  
Article
Activity Recognition and Semantic Description for Indoor Mobile Localization
by Sheng Guo, Hanjiang Xiong, Xianwei Zheng and Yan Zhou
Sensors 2017, 17(3), 649; https://doi.org/10.3390/s17030649 - 21 Mar 2017
Cited by 37 | Viewed by 5628
Abstract
As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is [...] Read more.
As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is significant for trajectory correction and advanced indoor map information extraction. In this paper, an integrated location acquisition method utilizing activity recognition and semantic information extraction is proposed for indoor mobile localization. The location acquisition method combines pedestrian dead reckoning (PDR), human activity recognition (HAR) and landmarks to acquire accurate indoor localization information. Considering the problem of initial position determination, a hidden Markov model (HMM) is utilized to infer the user’s initial position. To provide an improved service for further applications, the landmarks are further assigned semantic descriptions by detecting the user’s activities. The experiments conducted in this study confirm that a high degree of accuracy for a user’s indoor location can be obtained. Furthermore, the semantic information of a user’s trajectories can be extracted, which is extremely useful for further research into indoor location applications. Full article
(This article belongs to the Special Issue Smartphone-based Pedestrian Localization and Navigation)
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<p>The overall architecture. HAR, human activity recognition.</p>
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<p>Step detection. (<b>a</b>) Raw synthetic acceleration data; (<b>b</b>) filtered data and the step detection result.</p>
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<p>Landmark corrections. (<b>a</b>) Go straight through a landmark; (<b>b</b>) Passing a landmark when the user turns.</p>
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<p>The magnetometer changes when a door is opened. (<b>a</b>) Opening a south-facing door, where the door handle is to the right; (<b>b</b>) opening a south-facing door, where the door handle is to the left.</p>
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<p>The trajectory information collection process.</p>
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<p>Semantic landmark and adjacent segments. An adjacent segment consists of four parts: Id, distance, direction and semantic description. Id is the identifier of a segment. Distance represents the distance between the two landmarks that make up the segment. Direction represents the direction of the segment. Semantics indicates the semantic information that can be obtained.</p>
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<p>Semantic landmark construction process.</p>
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<p>The experiment’s overall process.</p>
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<p>Landmarks. (<b>a</b>) Landmarks of the first floor; (<b>b</b>) landmarks of the second floor.</p>
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<p>The direction information obtained by the magnetometer. (<b>a</b>) Distribution of the magnetic differences; (<b>b</b>) direction information.</p>
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<p>The activity sample collection of trajectory Entrance (ET)–R201.</p>
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<p>The trajectory of ET–R108. The raw trajectory is without landmarks, and the corrected trajectory is with landmarks.</p>
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<p>The trajectories on multiple floors.</p>
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<p>Trajectory matching results. (<b>a</b>) Raw PDR trajectory; (<b>b</b>) matching trajectory.</p>
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<p>Localization error. (<b>a</b>) Localization error of trajectory ET–R108; (<b>b</b>) the cumulative error distribution of the 25 test trajectories.</p>
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<p>Landmark matching. The red point is the turn landmark, and the green points are the door landmarks. The yellow points are the PDR position when a turn is detected. The blue points are the PDR position when opening a door is detected. The dashed red line indicates the nearest landmark points.</p>
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<p>Overall score of Zee, UnLoc and the proposed approach.</p>
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<p>Semantic matching of trajectories. (<b>a</b>) The trajectories after semantic matching; (<b>b</b>) the trajectory error.</p>
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3100 KiB  
Article
Optimal Power Allocation of Relay Sensor Node Capable of Energy Harvesting in Cooperative Cognitive Radio Network
by Pham Ngoc Son, Dongsoo Har, Nam Ik Cho and Hyung Yun Kong
Sensors 2017, 17(3), 648; https://doi.org/10.3390/s17030648 - 21 Mar 2017
Cited by 8 | Viewed by 4158
Abstract
A cooperative cognitive radio scheme exploiting primary signals for energy harvesting is proposed. The relay sensor node denoted as the secondary transmitter (ST) harvests energy from the primary signal transmitted from the primary transmitter, and then uses it to transmit power superposed codes [...] Read more.
A cooperative cognitive radio scheme exploiting primary signals for energy harvesting is proposed. The relay sensor node denoted as the secondary transmitter (ST) harvests energy from the primary signal transmitted from the primary transmitter, and then uses it to transmit power superposed codes of the secrecy signal of the secondary network (SN) and of the primary signal of the primary network (PN). The harvested energy is split into two parts according to a power splitting ratio, one for decoding the primary signal and the other for charging the battery. In power superposition coding, the amount of fractional power allocated to the primary signal is determined by another power allocation parameter (e.g., the power sharing coefficient). Our main concern is to investigate the impact of the two power parameters on the performances of the PN and the SN. Analytical or mathematical expressions of the outage probabilities of the PN and the SN are derived in terms of the power parameters, location of the ST, channel gain, and other system related parameters. A jointly optimal power splitting ratio and power sharing coefficient for achieving target outage probabilities of the PN and the SN, are found using these expressions and validated by simulations. Full article
(This article belongs to the Section Sensor Networks)
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<p>System model of the proposed cooperative communication scheme.</p>
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<p>Outage probabilities of the PN and the SN in the CC scheme as a function of <span class="html-italic">ρ</span> when <span class="html-italic">P</span>/<span class="html-italic">N</span><sub>0</sub> = 10 dB, <span class="html-italic">x</span><sub>1</sub> = <span class="html-italic">x</span><sub>2</sub> = 0.5, <span class="html-italic">y</span><sub>1</sub> = 0.1, and <span class="html-italic">y</span><sub>2</sub> = 0.3.</p>
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<p>Outage probabilities of the PN and the SN as functions of <span class="html-italic">ρ</span> and <span class="html-italic">α</span> when <span class="html-italic">P</span>/<span class="html-italic">N</span><sub>0</sub> = 10 dB, <span class="html-italic">x</span><sub>1</sub> = <span class="html-italic">x</span><sub>2</sub> = 0.5, <span class="html-italic">y</span><sub>1</sub> = 0.1, and <span class="html-italic">y</span><sub>2</sub> = 0.3.</p>
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<p>Outage probability of the PN in the CC scheme and in the DT scheme as a function of <span class="html-italic">P</span>/<span class="html-italic">N</span><sub>0</sub> when <span class="html-italic">x</span><sub>1</sub> = <span class="html-italic">x</span><sub>2</sub> = 0.5, <span class="html-italic">y</span><sub>1</sub> = 0.1, <span class="html-italic">y</span><sub>2</sub> = 0.3, and <span class="html-italic">α</span> = 0.1, 0.5, 0.7, 0.8, 0.9.</p>
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<p>Outage probabilities and optimal power splitting ratio <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mrow> <mi>P</mi> <mi>N</mi> <mo>,</mo> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics> </math> according to <span class="html-italic">α</span> when <span class="html-italic">P</span>/<span class="html-italic">N</span><sub>0</sub> = 10 dB, <span class="html-italic">x</span><sub>1</sub> = <span class="html-italic">x</span><sub>2</sub> = 0.5, <span class="html-italic">y</span><sub>1</sub> = 0.1 and <span class="html-italic">y</span><sub>2</sub> = 0.3.</p>
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<p>Outage probabilities of PN and SN as a function of <span class="html-italic">Ψ</span> when <span class="html-italic">P</span>/<span class="html-italic">N</span><sub>0</sub> = 10(dB), <span class="html-italic">α</span> = 0.9, <span class="html-italic">x</span><sub>1</sub> = <span class="html-italic">x</span><sub>2</sub> = 0.5, <span class="html-italic">y</span><sub>1</sub> = 0.1 and <span class="html-italic">y</span><sub>2</sub> = 0.3.</p>
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<p>Outage probabilities of PN and SN as a function of <span class="html-italic">y</span><sub>1</sub> or <span class="html-italic">y</span><sub>2</sub> when <span class="html-italic">P</span>/<span class="html-italic">N</span><sub>0</sub> = 10 dB, <span class="html-italic">α</span> = 0.9, and <span class="html-italic">x</span><sub>1</sub> = <span class="html-italic">x</span><sub>2</sub> = 0.5.</p>
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3319 KiB  
Article
Proof of Concept: Development of Snow Liquid Water Content Profiler Using CS650 Reflectometers at Caribou, ME, USA
by Carlos L. Pérez Díaz, Jonathan Muñoz, Tarendra Lakhankar, Reza Khanbilvardi and Peter Romanov
Sensors 2017, 17(3), 647; https://doi.org/10.3390/s17030647 - 21 Mar 2017
Cited by 7 | Viewed by 7286
Abstract
The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness’s plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task [...] Read more.
The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness’s plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task and a demanding issue to measure the snowpack’s liquid water content (LWC) and its temporal evolution with conventional in situ techniques. We propose an approach based on the use of time-domain reflectometry (TDR) and CS650 soil water content reflectometers to measure the snowpack’s LWC and temperature profiles. For this purpose, we created an easily-applicable, low-cost, automated, and continuous LWC profiling instrument using reflectometers at the Cooperative Remote Sensing Science and Technology Center-Snow Analysis and Field Experiment (CREST-SAFE) in Caribou, ME, USA, and tested it during the snow melt period (February–April) immediately after installation in 2014. Snow Thermal Model (SNTHERM) LWC simulations forced with CREST-SAFE meteorological data were used to evaluate the accuracy of the instrument. Results showed overall good agreement, but clearly indicated inaccuracy under wet snow conditions. For this reason, we present two (for dry and wet snow) statistical relationships between snow LWC and dielectric permittivity similar to Topp’s equation for the LWC of mineral soils. These equations were validated using CREST-SAFE in situ data from winter 2015. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Additionally, the equations seemed to be able to capture the snowpack state (i.e., onset of melt, medium, and maximum saturation). Lastly, field test results show advantages, such as: automated, continuous measurements, the temperature profiling of the snowpack, and the possible categorization of its state. However, future work should focus on improving the instrument’s capability to measure the snowpack’s LWC profile by properly calibrating it with in situ LWC measurements. Acceptable validation agreement indicates that the developed snow LWC, temperature, and wetness profiler offers a promising new tool for snow hydrology research. Full article
(This article belongs to the Section Remote Sensors)
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<p>CREST-SAFE location near the National Weather Service Regional Forecast Office and within the Caribou Municipal Airport premises in Caribou, ME, USA.</p>
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<p>Snow Wetness Profiler at CREST-SAFE.</p>
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<p>Snow Wetness Profiler snowpack dielectric permittivity (<b>a</b>) and temperature (<b>b</b>) measurements at different depths (15, 30, 45, 60 and 75 cm) above the soil surface at CREST-SAFE for winter (6 February–22 April) 2014; The third panel (<b>c</b>) illustrates snow depth (ultrasonic depth sensor) and near-surface air temperature (temperature and relative humidity probe) observations also collected at CREST-SAFE for the same period of time; The bottom panel (<b>d</b>) shows SNTHERM snowpack melt rate and cold content simulations obtained by weather-forcing the model with CREST-SAFE in situ meteorological data for the same time interval. All data are hourly.</p>
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<p>Snow Wetness Profiler (<span class="html-italic">y</span> axis) (estimated using Topp, Denoth, and Tiuri empirical formulas and developed statistical relationships) vs. SNTHERM (<span class="html-italic">x</span> axis) LWC scatter plots for different depths ((<b>a</b>) 15, (<b>b</b>) 30, (<b>c</b>) 45, and (<b>d</b>) 60 cm) above the soil surface at CREST-SAFE for winter (6 February–22 April) 2014.</p>
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<p>SNTHERM simulated LWC (<span class="html-italic">y</span> axis) vs. SWP dielectric permittivity (<span class="html-italic">x</span> axis) scatter plot for all depths (15, 30, 45, and 60 cm) above the soil surface combined at CREST-SAFE for winter (6 February–22 April) 2014. Third-degree polynomial regressions were found to be the best fit for dry (LWC &lt; 2%) and wet (LWC ≥ 2%) snow conditions.</p>
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<p>Frequency distribution for dry, moist, and wet snow conditions at CREST-SAFE for winters (<b>a</b>) 2014 and (<b>b</b>) 2015.</p>
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<p>Confusion matrix (actual-SNTHERM vs. predicted-new statistical relationships) using three snow conditions (dry, moist, wet) as classes for CREST-SAFE 2015 validation data.</p>
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5580 KiB  
Article
Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
by Miguel Garcia-Bosque, Adrián Pérez, Carlos Sánchez-Azqueta and Santiago Celma
Sensors 2017, 17(3), 646; https://doi.org/10.3390/s17030646 - 21 Mar 2017
Cited by 23 | Viewed by 6043
Abstract
In this work, we used a sensor-based True Random Number Generator in order to generate keys for a stream cipher based on a recently published hybrid algorithm mixing Skew Tent Map and a Linear Feedback Shift Register. The stream cipher was implemented and [...] Read more.
In this work, we used a sensor-based True Random Number Generator in order to generate keys for a stream cipher based on a recently published hybrid algorithm mixing Skew Tent Map and a Linear Feedback Shift Register. The stream cipher was implemented and tested in a Field Programmable Gate Array (FPGA) and was able to generate 8-bit width data streams at a clock frequency of 134 MHz, which is fast enough for Gigabit Ethernet applications. An exhaustive cryptanalysis was completed, allowing us to conclude that the system is secure. The stream cipher was compared with other chaotic stream ciphers implemented on similar platforms in terms of area, power consumption, and throughput. Full article
(This article belongs to the Section Physical Sensors)
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<p>Overview of the full cryptosystem, including a chaos-based stream cipher and a sensor-based seed generator.</p>
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<p>This figure represents several consecutive values of a sequence <math display="inline"> <semantics> <mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> </semantics> </math> generated by different values of the chaotic parameter <math display="inline"> <semantics> <mi>γ</mi> </semantics> </math> for: (<b>a</b>) The Logistic Map; (<b>b</b>) The Skew Tent Map.</p>
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<p>Block diagram of the proposed cipher system.</p>
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<p>Block diagram of the proposed 64-bit Skew Tent Map generator. Values <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>/</mo> <mi>γ</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>/</mo> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>−</mo> <mi>γ</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> are precalculated when initializing the chaotic cipher and they are constants for the whole communication session until a new key is requested.</p>
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<p>Sample random data generated by the accelerometer at rest, measured using a sample rate of 25 kSps.</p>
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<p>Fast Fourier Transform of the sampled data.</p>
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<p>(<b>a</b>) Sample data distribution histogram with a normal fit; (<b>b</b>) Empirical Cumulative Distribution Function (CDF) (blue) and CDF of a normal distribution (red).</p>
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<p>Conceptual block diagram of the processing performed.</p>
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<p>(<b>a</b>) NIST test results for bitstream obtained by sampling the sensor at 2.5 kSps; (<b>b</b>) NIST test results after applying the SHA-512 algorithm to that bitstream. The test numeration is the same as in <a href="#sensors-17-00646-t001" class="html-table">Table 1</a>.</p>
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<p>(<b>a</b>) Test image; (<b>b</b>) Encrypted image.</p>
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<p>(<b>a</b>) Histogram of the test image; (<b>b</b>) histogram of the encrypted image.</p>
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<p>NIST test results for a sequence generated by (<b>a</b>) The STM algorithm; (<b>b</b>) The STM-LFSR algorithm. The tests numeration is the same as in <a href="#sensors-17-00646-t001" class="html-table">Table 1</a>.</p>
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2928 KiB  
Article
Ultratrace Detection of Histamine Using a Molecularly-Imprinted Polymer-Based Voltammetric Sensor
by Maedeh Akhoundian, Axel Rüter and Sudhirkumar Shinde
Sensors 2017, 17(3), 645; https://doi.org/10.3390/s17030645 - 21 Mar 2017
Cited by 57 | Viewed by 8806
Abstract
Rapid and cost-effective analysis of histamine, in food, environmental, and diagnostics research has been of interest recently. However, for certain applications, the already-existing biological receptor-based sensing methods have usage limits in terms of stability and costs. As a result, robust and cost-effective imprinted [...] Read more.
Rapid and cost-effective analysis of histamine, in food, environmental, and diagnostics research has been of interest recently. However, for certain applications, the already-existing biological receptor-based sensing methods have usage limits in terms of stability and costs. As a result, robust and cost-effective imprinted polymeric receptors can be the best alternative. In the present work, molecularly-imprinted polymers (MIPs) for histamine were synthesized using methacrylic acid in chloroform and acetonitrile as two different porogens. The binding affinity of the MIPs with histamine was evaluated in aqueous media. MIPs synthesized in chloroform displayed better imprinting properties for histamine. We demonstrate here histamine MIPs incorporated into a carbon paste (CP) electrode as a MIP-CP electrode sensor platforms for detection of histamine. This simple sensor format allows accurate determination of histamine in the sub-nanomolar range using an electrochemical method. The sensor exhibited two distinct linear response ranges of 1 × 10−10–7 × 10−9 M and 7 × 10−9–4 × 10−7 M. The detection limit of the sensor was calculated equal to 7.4 × 10−11 M. The specificity of the proposed electrode for histamine is demonstrated by using the analogous molecules and other neurotransmitters such as serotonin, dopamine, etc. The MIP sensor was investigated with success on spiked serum samples. The easy preparation, simple procedure, and low production cost make the MIP sensor attractive for selective and sensitive detection of analytes, even in less-equipped laboratories with minimal training. Full article
(This article belongs to the Special Issue Biosensors and Molecular Imprinting)
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<p>Rebinding isotherm of histamine (<b>a</b>) MIP1, NIP1; (<b>b</b>) MIP2, NIP2 in 50 mM PBS buffer (pH 7.4). MIP1 and MIP2 (corresponding NIPs) were prepared in CHCl<sub>3</sub> and MeCN, respectively.</p>
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<p>MIP-CP electrode responses to histamine. Cyclic voltammograms in different molar concentrations of histamine: 1 × 10<sup>−10</sup> (<b>a</b>); 2 × 10<sup>−9</sup> (<b>b</b>); 4 × 10<sup>−9</sup> (<b>c</b>); 7 × 10<sup>−9</sup> (<b>d</b>); 2 × 10<sup>−7</sup> (<b>e</b>); and 4 × 10<sup>−7</sup> (<b>f</b>). All of the histamine solutions were made in 0.1 M solution of hexacyanoferrate (III) and KCl as blank.</p>
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<p>Comparison of voltammetric responses to histamine for different electrode composites. All of the histamine solutions were made in the 0.1 M solution of hexacyanoferrate (III) and KCl as a blank.</p>
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<p>Selectivity investigation by cyclic voltammetry; electrochemical response of MIP-CP electrode for histamine and other similar structure compounds. [Histamine and all analytes] = 0.004 µM, scan rate = 50 mV/s, E<sub>step</sub> = 10 mV and equilibration time = 5 s.</p>
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<p>Calibration curve obtained for the developed sensor; (insets are showing two linear ranges of histamine, 1 × 10<sup>−</sup><sup>10</sup>–7 × 10<sup>−9</sup> M and 7 × 10<sup>−9</sup>–4 × 10<sup>−7</sup> M).</p>
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577 KiB  
Article
Efficient and Security Enhanced Anonymous Authentication with Key Agreement Scheme in Wireless Sensor Networks
by Jaewook Jung, Jongho Moon, Donghoon Lee and Dongho Won
Sensors 2017, 17(3), 644; https://doi.org/10.3390/s17030644 - 21 Mar 2017
Cited by 58 | Viewed by 5082
Abstract
At present, users can utilize an authenticated key agreement protocol in a Wireless Sensor Network (WSN) to securely obtain desired information, and numerous studies have investigated authentication techniques to construct efficient, robust WSNs. Chang et al. recently presented an authenticated key agreement mechanism [...] Read more.
At present, users can utilize an authenticated key agreement protocol in a Wireless Sensor Network (WSN) to securely obtain desired information, and numerous studies have investigated authentication techniques to construct efficient, robust WSNs. Chang et al. recently presented an authenticated key agreement mechanism for WSNs and claimed that their authentication mechanism can both prevent various types of attacks, as well as preserve security properties. However, we have discovered that Chang et al’s method possesses some security weaknesses. First, their mechanism cannot guarantee protection against a password guessing attack, user impersonation attack or session key compromise. Second, the mechanism results in a high load on the gateway node because the gateway node should always maintain the verifier tables. Third, there is no session key verification process in the authentication phase. To this end, we describe how the previously-stated weaknesses occur and propose a security-enhanced version for WSNs. We present a detailed analysis of the security and performance of our authenticated key agreement mechanism, which not only enhances security compared to that of related schemes, but also takes efficiency into consideration. Full article
(This article belongs to the Section Sensor Networks)
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<p>WSN system architecture.</p>
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<p>Authentication mechanism using the biohashing approach.</p>
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<p>Registration phase for the proposed scheme.</p>
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<p>Login and authentication phase for the proposed scheme.</p>
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<p>Password change phase for the proposed scheme.</p>
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3211 KiB  
Article
An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy
by Óscar Blanco-Novoa, Tiago M. Fernández-Caramés, Paula Fraga-Lamas and Luis Castedo
Sensors 2017, 17(3), 643; https://doi.org/10.3390/s17030643 - 21 Mar 2017
Cited by 64 | Viewed by 12045
Abstract
The Internet of Energy (IoE) represents a novel paradigm where electrical power systems work cooperatively with smart devices to increase the visibility of energy consumption and create safer, cleaner and sustainable energy systems. The implementation of IoE services involves the use of multiple [...] Read more.
The Internet of Energy (IoE) represents a novel paradigm where electrical power systems work cooperatively with smart devices to increase the visibility of energy consumption and create safer, cleaner and sustainable energy systems. The implementation of IoE services involves the use of multiple components, like embedded systems, power electronics or sensors, which are an essential part of the infrastructure dedicated to the generation and distribution energy and the one required by the final consumer. This article focuses on the latter and presents a smart socket system that collects the information about energy price and makes use of sensors and actuators to optimize home energy consumption according to the user preferences. Specifically, this article provides three main novel contributions. First, what to our knowledge is the first hardware prototype that manages in a practical real-world scenario the price values obtained from a public electricity operator is presented. The second contribution is related to the definition of a novel wireless sensor network communications protocol based on Wi-Fi that allows for creating an easy-to-deploy smart plug system that self-organizes and auto-configures to collect the sensed data, minimizing user intervention. Third, it is provided a thorough description of the design of one of the few open-source smart plug systems, including its communications architecture, the protocols implemented, the main sensing and actuation components and the most relevant pieces of the software. Moreover, with the aim of illustrating the capabilities of the smart plug system, the results of different experiments performed are shown. Such experiments evaluate in real-world scenarios the system’s ease of use, its communications range and its performance when using HTTPS. Finally, the economic savings are estimated for different appliances, concluding that, in the practical situation proposed, the smart plug system allows certain energy-demanding appliances to save almost €70 per year. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2016)
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<p>Subsystems diagram.</p>
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<p>Example of the evolution of the energy prices during a specific time interval.</p>
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<p>General overview of the system.</p>
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<p>Sequence diagram for adding a new node.</p>
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<p>Sequence diagram for adding an operation interval.</p>
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<p>Sequence diagram that illustrates the tasks performed by the proxy server.</p>
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<p>HTTP GET request.</p>
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<p>Optimized request.</p>
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<p>Sequence diagram that illustrates the tasks carried out by the REST service.</p>
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<p>Components of the smart socket system.</p>
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<p>Final assembly and main components of the smart power outlet.</p>
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<p>Original XML verbose format.</p>
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<p>Pre-processed JSON format.</p>
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<p>Main control panel.</p>
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<p>Web application sequence diagram.</p>
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<p>Initial configuration form of a smart socket.</p>
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<p>IoT network state request sent through PostMan.</p>
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<p>Example of scheduling for a one-hour operation interval.</p>
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<p>Menu for scheduling the operation intervals.</p>
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<p>Graph representing the power consumption of an electric heater with two heat levels.</p>
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<p>Graph comparing the power consumption of the device using HTTP and HTTPS.</p>
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3499 KiB  
Article
A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks
by Xu Jing, Hanwen Hu, Huijun Yang, Man Ho Au, Shuqin Li, Naixue Xiong, Muhammad Imran and Athanasios V. Vasilakos
Sensors 2017, 17(3), 642; https://doi.org/10.3390/s17030642 - 21 Mar 2017
Cited by 10 | Viewed by 4827
Abstract
The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may [...] Read more.
The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. Full article
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
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<p>Calculation process of intrusion effort.</p>
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<p>Intrusion effort activity diagram.</p>
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<p>Assessing LoBSs’ risk activity diagram.</p>
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<p>Part of source data.</p>
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<p>Part of source data including attack time, duration and type.</p>
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<p>Relationship between elementary intrusion effort and duration.</p>
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<p>Relationship between intrusion trace effort and time widow.</p>
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<p>Relationship between intrusion trace effort and objective risk.</p>
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<p>Relationship between intrusion trace effort and subjective risk.</p>
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<p>Comprehensive risk under different ratios between objective risk and subjective risk.</p>
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<p>Relationship between initial risk and LoBSs’ quantitative risk.</p>
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<p>Relationship between attack difficulty degree and LoBSs’ quantitative risk.</p>
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<p>Relationship between confidence level and LoBSs’ quantitative risk.</p>
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<p>Relationship between time window and LoBSs’ quantitative risk.</p>
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4163 KiB  
Article
An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
by Qinghua Zeng, Weina Chen, Jianye Liu and Huizhe Wang
Sensors 2017, 17(3), 641; https://doi.org/10.3390/s17030641 - 21 Mar 2017
Cited by 45 | Viewed by 7454
Abstract
An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different [...] Read more.
An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Multi-Sensor Integration and Fusion)
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<p>System hardware structure of the MUAV.</p>
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<p>An example of a factor graph.</p>
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<p>Factor graph representations of the state variable and the measurement.</p>
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<p>The factor graph containing the GPS measurement.</p>
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<p>The factor graph containing GPS and magnetic measurement.</p>
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<p>The multi-sensor fusion framework based on factor graph.</p>
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<p>The factor graph and the associated Jacobian matrix in two moments. (<b>a</b>) The factor graph and the associated Jacobian matrix; (<b>b</b>) The factor graph and the associated Jacobian matrix when adding new factor node.</p>
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<p>Flight track of the MUAV in the simulation.</p>
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<p>Comparison curves of two filter methods. (<b>a</b>) Position error contrast curves; (<b>b</b>) Velocity error contrast curves.</p>
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<p>Square shaped outdoor trajectory in the flight experiment of the MUAV. (<b>a</b>) Test flight experiment of the MUAV; (<b>b</b>) Square shaped trajectory.</p>
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<p>Comparison curves of two filter methods. (<b>a</b>) Position error contrast curves. (<b>b</b>) Velocity error contrast curves.</p>
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2340 KiB  
Article
The Impact of Bending Stress on the Performance of Giant Magneto-Impedance (GMI) Magnetic Sensors
by Julie Nabias, Aktham Asfour and Jean-Paul Yonnet
Sensors 2017, 17(3), 640; https://doi.org/10.3390/s17030640 - 20 Mar 2017
Cited by 28 | Viewed by 5606
Abstract
The flexibility of amorphous Giant Magneto-Impedance (GMI) micro wires makes them easy to use in several magnetic field sensing applications, such as electrical current sensing, where they need to be deformed in order to be aligned with the measured field. The present paper [...] Read more.
The flexibility of amorphous Giant Magneto-Impedance (GMI) micro wires makes them easy to use in several magnetic field sensing applications, such as electrical current sensing, where they need to be deformed in order to be aligned with the measured field. The present paper deals with the bending impact, as a parameter of influence of the sensor, on the GMI effect in 100 µm Co-rich amorphous wires. Changes in the values of key parameters associated with the GMI effect have been investigated under bending stress. These parameters included the GMI ratio, the intrinsic sensitivity, and the offset at a given bias field. The experimental results have shown that bending the wire resulted in a reduction of GMI ratio and sensitivity. The bending also induced a net change in the offset for the considered bending curvature and the set of used excitation parameters (1 MHz, 1 mA). Furthermore, the field of the maximum impedance, which is generally related to the anisotropy field of the wire, was increased. The reversibility and the repeatability of the bending effect were also evaluated by applying repetitive bending stresses. The observations have actually shown that the behavior of the wire under the bending stress was roughly reversible and repetitive. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in France 2016)
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<p>A typical Giant Magneto-Impedance (GMI) curve, <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math>.</p>
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<p>The experimental setup. The GMI wire was 90 mm in length and excited by an AC current with a 1 mA amplitude and a 1 MHz frequency. In this figure, the wire is in a bent position.</p>
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<p>Change of <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> and ∆<span class="html-italic">Z</span>/<span class="html-italic">Z</span> with the bending stress.</p>
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<p>Representation of a bending stress.</p>
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<p>Change of the normalized sensitivity <math display="inline"> <semantics> <mrow> <mfrac> <mn>1</mn> <mrow> <mi>S</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>H</mi> <mi>b</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </mfrac> <mfrac> <mrow> <mo>∂</mo> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> <mrow> <mo>∂</mo> <mi>H</mi> </mrow> </mfrac> </mrow> </semantics> </math>, under bending stress. The sensitivity was normalized with respect to the maximum sensitivity, <math display="inline"> <semantics> <mrow> <mi>S</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>H</mi> <mi>b</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> , obtained in a straight position of the wire.</p>
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<p>Reversibility and repeatability of the impedance curve, <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math>, following two consecutive bending stresses. (<b>a</b>) Change in the modulus of the impedance, <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> , with a 1st bending stress applied; (<b>b</b>) Comparison between the modulus of the impedance after the relaxation of the bending stress (1st bending) and the initial straight position; (<b>c</b>) Change in the modulus of the impedance, <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> , with a 2nd bending stress; (<b>d</b>) Comparison between the modulus of the impedance after the relaxation of the bending stress (2nd bending) and the initial straight position.</p>
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<p>Reversibility and repeatability of the impedance curve, <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math>, following two consecutive bending stresses. (<b>a</b>) Change in the modulus of the impedance, <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> , with a 1st bending stress applied; (<b>b</b>) Comparison between the modulus of the impedance after the relaxation of the bending stress (1st bending) and the initial straight position; (<b>c</b>) Change in the modulus of the impedance, <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>Z</mi> <mrow> <mo>(</mo> <mi>H</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> , with a 2nd bending stress; (<b>d</b>) Comparison between the modulus of the impedance after the relaxation of the bending stress (2nd bending) and the initial straight position.</p>
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<p>Reversibility and repeatability of the sensitivity following two consecutive bending stresses. (<b>a</b>) Change in the normalized sensitivity with a 1st bending stress; (<b>b</b>) Comparison between the normalized sensitivity after the relaxation of the bending stress (1st bending) and the initial straight position; (<b>c</b>) Change in the normalized sensitivity with a 2nd bending stress; (<b>d</b>) Comparison between the normalized sensitivity after the relaxation of the bending stress (2nd bending) and the initial straight position.</p>
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475 KiB  
Article
An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks
by Pengcheng Fu, Yongbo Cheng, Hongying Tang, Baoqing Li, Jun Pei and Xiaobing Yuan
Sensors 2017, 17(3), 639; https://doi.org/10.3390/s17030639 - 20 Mar 2017
Cited by 14 | Viewed by 4471
Abstract
In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of [...] Read more.
In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods. Full article
(This article belongs to the Section Sensor Networks)
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<p>A target tracking scenario in a wireless camera sensor network. The target is viewed by many nodes, but only some of them form the tracking task cluster and the remaining nodes turn into the alert node. In addition, the nodes that could not view the target will turn into the sleep state to save energy.</p>
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<p>The state transition process model.</p>
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<p>Sensing model in WCNs. Some camera sensors are deployed inside the surveillance area and <math display="inline"> <semantics> <msub> <mi>ℏ</mi> <mi>i</mi> </msub> </semantics> </math> is a fan-shaped area with central angle <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>90</mn> <mo>∘</mo> </msup> </mrow> </semantics> </math>. The zones of FOV of camera <math display="inline"> <semantics> <msub> <mi>c</mi> <mi>i</mi> </msub> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>Z</mi> <mi>i</mi> </msub> <mo>=</mo> <mrow> <mo>{</mo> <msubsup> <mi>Z</mi> <mi>i</mi> <mi>n</mi> </msubsup> <mo>;</mo> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>}</mo> </mrow> </mrow> </semantics> </math>, are shown with different sized grey shapes. Target in <math display="inline"> <semantics> <msubsup> <mi>Z</mi> <mi>i</mi> <mn>2</mn> </msubsup> </semantics> </math> can be well viewed by <math display="inline"> <semantics> <msub> <mi>c</mi> <mi>i</mi> </msub> </semantics> </math>, but not well viewed in <math display="inline"> <semantics> <msubsup> <mi>Z</mi> <mi>i</mi> <mn>1</mn> </msubsup> </semantics> </math> and <math display="inline"> <semantics> <msubsup> <mi>Z</mi> <mi>i</mi> <mn>3</mn> </msubsup> </semantics> </math> because their distance is too close or too far.</p>
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<p>Some of the target trajectories which are used to test our methods in our simulated experiment. The trajectories are in solid lines with different colors, denoting different motion states of the target. To simplify the simulation, the spans of all trajectories are set within 100 s.</p>
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<p>Position errors corresponding to different numbers of task cluster nodes in tracking based on different filter methods.</p>
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<p>Ratio of divergence corresponding to different numbers of task cluster nodes in tracking based on different fusion methods.</p>
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<p>Tracking performance in some different trajectories of the target using our proposed node scheduling mechanism (Algorithm 1). The true target trajectories are shown in a solid line with different colors and the estimated trajectories are shown in dashed lines with green.</p>
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<p>Averaged tracking error of different algorithms at different timesteps. The tracking errors are averaged over 1000 independent Monte Carlo runs under the same target trajectories.</p>
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<p>Comparison between Algorithm 1, M1, M2 and M3: the left figure depicts the mean tracking error in one timestep and the right one depicts the mean energy consumption in one tracking action.</p>
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<p>Average number of task cluster nodes of different methods at different timesteps under the same target trajectory.</p>
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<p>Comparison between Algorithm 2, C1 and C2: the left figure depicts the averaged standard deviation of the remaining energy in a task cluster and the right one depicts the averaged ratio of loss of target in a tracking action.</p>
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1573 KiB  
Article
DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays
by Jianfeng Li, Feng Wang and Defu Jiang
Sensors 2017, 17(3), 638; https://doi.org/10.3390/s17030638 - 20 Mar 2017
Cited by 10 | Viewed by 5015
Abstract
A fast direction of arrival (DOA) estimation method using a real-valued cross-correlation matrix (CCM) of coprime subarrays is proposed. Firstly, real-valued CCM with extended aperture is constructed to obtain the signal subspaces corresponding to the two subarrays. By analysing the relationship between the [...] Read more.
A fast direction of arrival (DOA) estimation method using a real-valued cross-correlation matrix (CCM) of coprime subarrays is proposed. Firstly, real-valued CCM with extended aperture is constructed to obtain the signal subspaces corresponding to the two subarrays. By analysing the relationship between the two subspaces, DOA estimations from the two subarrays are simultaneously obtained with automatic pairing. Finally, unique DOA is determined based on the common results from the two subarrays. Compared to partial spectral search (PSS) method and estimation of signal parameter via rotational invariance (ESPRIT) based method for coprime arrays, the proposed algorithm has lower complexity but achieves better DOA estimation performance and handles more sources. Simulation results verify the effectiveness of the approach. Full article
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<p>The structure of coprime arrays.</p>
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<p>DOA estimation results over 100 trials (SNR = 0 dB).</p>
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<p>DOA estimation performance comparison.</p>
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<p>DOA estimation results with <span class="html-italic">K</span> = 5 sources (SNR = 10 dB).</p>
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<p>DOA estimation performance comparison with closely-spaced sources.</p>
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<p>Resolution probability versus angular separation (SNR = 10 dB).</p>
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<p>DOA estimation performance comparison with three sources.</p>
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2572 KiB  
Article
Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction
by Dat Tien Nguyen, Ki Wan Kim, Hyung Gil Hong, Ja Hyung Koo, Min Cheol Kim and Kang Ryoung Park
Sensors 2017, 17(3), 637; https://doi.org/10.3390/s17030637 - 20 Mar 2017
Cited by 41 | Viewed by 8821
Abstract
Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram [...] Read more.
Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. Full article
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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<p>Overview of our proposed method for gender recognition using CNN for image feature extraction.</p>
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<p>Design architecture of our CNN for gender recognition using visible-light or thermal images.</p>
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<p>Feature-level fusion combination method for gender recognition using visible-light and thermal images of the human body.</p>
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<p>Score-level fusion combination method for gender recognition using visible-light and thermal images of the human body.</p>
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<p>Sample images in our self-established collected database: (<b>a</b>) thermal-visible image pairs of male persons; and (<b>b</b>) thermal-visible image pairs of female persons.</p>
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<p>Average ROC curves of recognition systems using single image types with the CNN-based method in <a href="#sensors-17-00637-f002" class="html-fig">Figure 2</a>.</p>
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<p>Average ROC curves of the recognition systems using single image types with the CNN-based method and SVM-based method.</p>
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<p>Average ROC curves of recognition systems using our proposed method.</p>
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<p>Examples of correct recognition results using our proposed method: (<b>a</b>–<b>c</b>) examples of male images correctly recognized as males, and (<b>d</b>–<b>f</b>) examples of female images correctly recognized as females.</p>
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<p>Examples of errors using our proposed method: (<b>a</b>–<b>c</b>) examples of male images incorrectly recognized as females, and (<b>d</b>–<b>f</b>) examples of female images incorrectly recognized as males.</p>
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13091 KiB  
Article
A Networked Sensor System for the Analysis of Plot-Scale Hydrology
by German Villalba, Fernando Plaza, Xiaoyang Zhong, Tyler W. Davis, Miguel Navarro, Yimei Li, Thomas A. Slater, Yao Liang and Xu Liang
Sensors 2017, 17(3), 636; https://doi.org/10.3390/s17030636 - 20 Mar 2017
Cited by 12 | Viewed by 7611
Abstract
This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and [...] Read more.
This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming) maintained high success rates (>96%) and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively) low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments. Full article
(This article belongs to the Special Issue Sensors for Environmental Monitoring 2016)
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<p>Schematics of the environmental sensors deployed at the ASWP network, including the (<b>a</b>) Decagon Devices MPS-1/MPS-2 water potential (WP) sensor; (<b>b</b>) Decagon Devices EC-5 soil moisture (SM) sensor; (<b>c</b>) thermometric sap flow sensor probes; and (<b>d</b>) sap flow sensor circuit.</p>
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<p>Custom sensor boards for TelosB motes.</p>
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<p>Examples of node types and their enclosures in the ASWP network, including (<b>a</b>) relay nodes hanging from a tree branch; (<b>b</b>) sap flow node mounted to the side of a tree; and (<b>c</b>) soil sensor node mounted to a PVC pipe.</p>
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<p>Mote application architecture.</p>
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<p>Map of the six sites of the ASWP testbed (October 2016 configuration). Relay nodes are represented as yellow circles, sap flow nodes are represented as red circles (the three pink circles in site 2 are used in this analysis), soil sensor nodes are represented as dark blue circles, and the base station is represented as a white square. The data loggers used for validation (i.e., DL1 and DL2) are shown as light blue diamonds and their corresponding nodes as light blue circles. The four-digit node numbers referenced in the analysis are indicated in the zoomed region of site 2.</p>
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<p>Field survey area within site 2 for the determination of a representative <span class="html-italic">A<sub>S</sub>/A<sub>G</sub></span> ratio.</p>
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<p>Comparison of (<b>a</b>) soil temperature, T in Celsius degrees; (<b>b</b>) matric water potential (WP), <math display="inline"> <semantics> <mi>ψ</mi> </semantics> </math> in kPa; and (<b>c</b>) volumetric soil moisture (SM), <math display="inline"> <semantics> <mi>θ</mi> </semantics> </math> in m<sup>3</sup>·m<sup>−3</sup> data collected by a data logger (DL2) and wireless nodes (2282 and 2292) from the ASWP network between 29 July and 23 August 2016. The variable at a depth of 10 cm is shown in dark red for the data logger and light red for the nodes. The variable at a depth of 30 cm is shown in dark blue for the data logger and in light blue for the nodes.</p>
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<p>Estimation of the soil hydraulic parameters from data at the location close to the middle of the hill in site 2 (i.e., Nodes 2282 and 2292, and data logger DL2). Wetness, <span class="html-italic">s</span>, in blue; Estimated wetness from measured matric water potential (WP) using the Clapp and Hornberger, and van Genuchten equations, in green and red, respectively. (<b>a</b>) Comparison of the wetness, <span class="html-italic">s</span>, time series, at a depth of 10 cm; (<b>b</b>) Same as part a, for a depth of 30 cm; (<b>c</b>) Relationship between the soil wetness, <span class="html-italic">s</span>, and the absolute value of the WP, in kPa, |<math display="inline"> <semantics> <mi>ψ</mi> </semantics> </math>| at a depth of 10 cm. The fitted Clapp and Hornberger equation is shown in green and the fitted van Genuchten equation in red; (<b>d</b>) Same as part c, for a depth of 30 cm.</p>
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<p>Sap flow results for node 2084 between 20 and 27 July 2016. (<b>a</b>) Raw voltages (i.e., ADC in mV) from the HP (red scatter plot) and TP (blue scatter plot) between 0 and 1000, and smoothed plot for the HP and TP in red and blue, respectively; (<b>b</b>) Filtered and smoothed temperatures for the HP (red) and TP (blue) from the raw voltages ADC0 and ADC1 respectively, difference in temperature (HP-TP) in Celsius degrees (magenta); (<b>c</b>) Sap flow time series (mm/h).</p>
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<p>Transpiration (<math display="inline"> <semantics> <mi>τ</mi> </semantics> </math>) calculations in the ASWP site based on the measurements in nodes 2014, 2034 and 2134, from 11 July 2016 (7/11/16) to 11 October 2016 (10/11/16). (<b>a</b>) Transpiration rates in mm/h based on a 10-min interval; (<b>b</b>) Transpiration rates in mm/day based on a 24-h interval.</p>
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<p>Comparison of the mean and standard deviation of volumetric soil moisture (SM), <math display="inline"> <semantics> <mi>θ</mi> </semantics> </math> in m<sup>3</sup>·m<sup>−3</sup> at sites 2 and 6 in red and blue, respectively, in the ASWP WSN testbed between 10 July and 10 October 2016. (<b>a</b>) Mean SM at a depth of 10 cm; (<b>b</b>) Mean SM at a depth of 30 cm; (<b>c</b>) Standard deviation of the SM at a depth of 10 cm; (<b>d</b>) Standard deviation of the SM at a depth of 30 cm.</p>
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<p>Interpolated surfaces (Kriging method) showing the average seasonal variation in volumetric soil moisture (SM) and soil water potential (WP), based on data retrieved from 2010 to 2016. (<b>a</b>) winter average (December–February): (<b>a1</b>) SM at 10 cm; (<b>a2</b>) SM at 30 cm; (<b>a3</b>) WP at 30 cm; (<b>b</b>) spring average (March–May): (<b>b1</b>) SM at 10 cm; (<b>b2</b>) SM at 30 cm; (<b>b3</b>) WP at 30 cm; (<b>c</b>) summer average (June–August): (<b>c1</b>) SM at 10 cm; (<b>c2</b>) SM at 30 cm; (<b>c3</b>) WP at 30 cm; (<b>d</b>) fall average (September–November): (<b>d1</b>) SM at 10 cm; (<b>d2</b>) SM at 30 cm; (<b>d3</b>) WP at 30 cm. SM is expressed in m<sup>3</sup>·m<sup>−3</sup>. WP is expressed in kPa. The elevation contours are expressed in m. The dots represent the nodes from which the data was retrieved.</p>
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<p>Interpolated surfaces (Kriging method) showing a comparison between the average fall (September–November) SM and WP of 2010 and 2016, and average SM and WP on 11 August 2016 (08/11/2016). (<b>a</b>) SM at 10 cm (fall 2010); (<b>b</b>) SM at 30 cm (fall 2010); (<b>c</b>) WP at 30 cm (fall 2010); (<b>d</b>) SM at 10 cm (fall 2016); (<b>e</b>) SM at 30 cm (fall 2016); (<b>f</b>) WP at 30 cm (fall 2016); (<b>g</b>) SM at 10 cm (11 August 2016); (<b>h</b>) SM at 30 cm (11 August 2016); (<b>i</b>) WP at 30 cm (11 August 2016). SM is expressed in m<sup>3</sup>·m<sup>−3</sup>. WP is expressed in kPa. The elevation contours are expressed in m. The dots represent the nodes from which the data was retrieved</p>
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<p>MobileDeluge, a hand-held mobile mote reprogramming tool.</p>
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3199 KiB  
Article
Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
by Bingbing Zhang, Zhengtao Wang, Lv Zhou, Jiandi Feng, Yaodong Qiu and Fupeng Li
Sensors 2017, 17(3), 635; https://doi.org/10.3390/s17030635 - 20 Mar 2017
Cited by 8 | Viewed by 5209
Abstract
Swarm is a European Space Agency (ESA) project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD) reduced-dynamic orbit [...] Read more.
Swarm is a European Space Agency (ESA) project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD) reduced-dynamic orbit solutions can be determined using space-borne GPS data and optimized pseudo-stochastic pulses under high ionospheric activity. We choose Swarm space-borne GPS data from 1–25 October 2014, and Swarm reduced-dynamic orbits are obtained. Orbit quality is assessed by GPS phase observation residuals and compared with Precise Science Orbits (PSOs) released by ESA. Results show that pseudo-stochastic pulses with a time interval of 6 min and a priori standard deviation (STD) of 10−2 mm/s in radial (R), along-track (T) and cross-track (N) directions are optimized to Swarm ZD reduced-dynamic precise orbit determination (POD). During high ionospheric activity, the mean Root Mean Square (RMS) of Swarm GPS phase residuals is at 9–11 mm, Swarm orbit solutions are also compared with Swarm PSOs released by ESA and the accuracy of Swarm orbits can reach 2–4 cm in R, T and N directions. Independent Satellite Laser Ranging (SLR) validation indicates that Swarm reduced-dynamic orbits have an accuracy of 2–4 cm. Swarm-B orbit quality is better than those of Swarm-A and Swarm-C. The Swarm orbits can be applied to the geomagnetic, geoelectric and gravity field recovery. Full article
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<p>Root Mean Square (RMS) values of GPS phase residuals for all three Swarm satellites on 1 November 2014.</p>
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<p>RMS values of orbit differences for all Swarm satellites in 3D direction between Swarm POD and Precise Science Orbits (PSOs) released by the European Space Agency (ESA), 1 November 2014.</p>
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<p>RMS values of orbit differences for all Swarm satellites in 3D direction between Swarm POD and PSOs released by ESA from Experiments (7) to (12), 1 November 2014.</p>
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<p>RMS values of orbit differences for all Swarm satellites in 3D direction between Swarm POD and PSOs released by ESA from Experiments (13) to (18), 1 November 2014.</p>
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<p>RMS values of orbit differences for all Swarm satellites in 3D direction between Swarm POD and PSOs released by ESA from Experiments (19) to (24), 1 November 2014</p>
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<p>F10.7 values during 1–25 October 2014.</p>
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<p>RMS values of GPS phase residuals for Swarm-A, Swarm-B and Swarm-C during day of year (DOY) 274–298, 2014.</p>
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<p>RMS values of orbit differences for Swarm-A, Swarm-B and Swarm-C in the radial (R), along-track (T) and cross-track (N) directions during DOY 274–298, 2014: (<b>a</b>) RMS values of Swarm-A orbit differences; (<b>b</b>) RMS values of Swarm-B orbit differences; (<b>c</b>) RMS values of Swarm-C orbit differences.</p>
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<p>Orbit differences for Swarm-A, Swarm-B and Swarm-C in the R, T and N directions on the DOY 296, 2014.</p>
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<p>SLR residuals for the Swarm ZD reduced-dynamic POD during DOY 274–298, 2014.</p>
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3057 KiB  
Article
Detrimental Effect Elimination of Laser Frequency Instability in Brillouin Optical Time Domain Reflectometer by Using Self-Heterodyne Detection
by Yongqian Li, Xiaojuan Li, Qi An and Lixin Zhang
Sensors 2017, 17(3), 634; https://doi.org/10.3390/s17030634 - 20 Mar 2017
Cited by 11 | Viewed by 5091
Abstract
A useful method for eliminating the detrimental effect of laser frequency instability on Brillouin signals by employing the self-heterodyne detection of Rayleigh and Brillouin scattering is presented. From the analysis of Brillouin scattering spectra from fibers with different lengths measured by heterodyne detection, [...] Read more.
A useful method for eliminating the detrimental effect of laser frequency instability on Brillouin signals by employing the self-heterodyne detection of Rayleigh and Brillouin scattering is presented. From the analysis of Brillouin scattering spectra from fibers with different lengths measured by heterodyne detection, the maximum usable pulse width immune to laser frequency instability is obtained to be about 4 µs in a self-heterodyne detection Brillouin optical time domain reflectometer (BOTDR) system using a broad-band laser with low frequency stability. Applying the self-heterodyne detection of Rayleigh and Brillouin scattering in BOTDR system, we successfully demonstrate that the detrimental effect of laser frequency instability on Brillouin signals can be eliminated effectively. Employing the broad-band laser modulated by a 130-ns wide pulse driven electro-optic modulator, the observed maximum errors in temperatures measured by the local heterodyne and self-heterodyne detection BOTDR systems are 7.9 °C and 1.2 °C, respectively. Full article
(This article belongs to the Section Physical Sensors)
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<p>Relative frequency variations of the two laser sources.</p>
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<p>Experimental setups for Brillouin spectrum measurement employing narrow-band and broad-band lasers based on (<b>a</b>) local heterodyne detection and (<b>b</b>) self-heterodyne detection.</p>
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<p>Brillouin spectrum width versus fiber length with different lasers: (<b>a</b>) local heterodyne detection and (<b>b</b>) self-heterodyne detection.</p>
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<p>Brillouin spectra of 0.4 km and 9.5 km long fibers with different lasers: (<b>a</b>) local heterodyne detection and (<b>b</b>) self-heterodyne detection.</p>
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<p>Experimental setup of temperature sensing based on the local heterodyne detection BOTDR technique.</p>
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<p>3D power spectra of the local heterodyne detection Brillouin signals with (<b>a</b>) narrow-band laser and (<b>b</b>) broad-band laser. The temperature of the thermostatic water bath is set at 50 °C.</p>
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<p>Distribution of Brillouin signals and demodulated temperature along the fiber obtained by local heterodyne detection system: (<b>a</b>) Brillouin frequency shift; (<b>b</b>) Brillouin linewidth; (<b>c</b>) Brillouin peak power; and (<b>d</b>) temperature.</p>
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<p>Experimental setup of temperature sensing based on the self-heterodyne detection BOTDR technique.</p>
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<p>3D power spectra of the self-heterodyne detection Brillouin signals with a (<b>a</b>) narrow-band laser and (<b>b</b>) broad-band laser. The temperature of the thermostatic water bath is set at 50 °C.</p>
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<p>Distribution of Brillouin signals and demodulated temperature along the fiber based on the self-heterodyne detection: (<b>a</b>) Brillouin frequency shift; (<b>b</b>) Brillouin linewidth; (<b>c</b>) Brillouin peak power; and (<b>d</b>) temperature.</p>
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<p>Distribution of Brillouin signals and demodulated temperature along the fiber based on the self-heterodyne detection: (<b>a</b>) Brillouin frequency shift; (<b>b</b>) Brillouin linewidth; (<b>c</b>) Brillouin peak power; and (<b>d</b>) temperature.</p>
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8561 KiB  
Article
A Vehicle Steering Recognition System Based on Low-Cost Smartphone Sensors
by Xinhua Liu, Huafeng Mei, Huachang Lu, Hailan Kuang and Xiaolin Ma
Sensors 2017, 17(3), 633; https://doi.org/10.3390/s17030633 - 20 Mar 2017
Cited by 21 | Viewed by 10731
Abstract
Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver’s safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using [...] Read more.
Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver’s safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. To identify vehicle steering maneuvers, we focus on the vehicle’s angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW) is adopted to recognize the vehicle steering. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers. Full article
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<p>Lane-change from right to left.</p>
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<p>Left turn and left U-turn.</p>
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<p>Architecture of the system.</p>
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<p>The raw gyroscope and accelerometer data.</p>
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<p>The filtered gyroscope and accelerometer data.</p>
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<p>The coordinate systems of a smartphone and a vehicle.</p>
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<p>Rotation around the x-axis.</p>
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<p>Rotation around the y-axis.</p>
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<p>Coordinate alignment.</p>
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<p>The features of turn signals.</p>
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<p>The amplitude of the audio signal with noise.</p>
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<p>The spectrum of the audio signal with noise.</p>
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<p>The amplitude of the audio signal after filtering.</p>
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<p>Vehicle steering recognition.</p>
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<p>Gyro and short time energy for a left lane change.</p>
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<p>Gyro and short time energy during a left turn.</p>
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<p>Gyro and short time energy during a U-turn.</p>
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<p>Three matching modes.</p>
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<p>The DTW principle.</p>
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<p>FastDTW principle.</p>
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<p>Angular velocity of left turn and U-turn.</p>
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<p>Gyro amplitude of different steering maneuvers.</p>
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<p>Pavement environment.</p>
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<p>Steering pattern templates. (<b>a</b>) Turn left normally; (<b>b</b>) Turn right normally; (<b>c</b>) U-turn normally; (<b>d</b>) Left lane normally; (<b>e</b>) Right lane normally.</p>
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<p>Steering recognition results.</p>
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<p>Steering recognition results.</p>
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<p>Different vehicle recognition results.</p>
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<p>Steering maneuver comparison results.</p>
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4195 KiB  
Article
A Colorimetric Sensor for the Highly Selective Detection of Sulfide and 1,4-Dithiothreitol Based on the In Situ Formation of Silver Nanoparticles Using Dopamine
by Lingzhi Zhao, Liu Zhao, Yanqing Miao, Chunye Liu and Chenxiao Zhang
Sensors 2017, 17(3), 626; https://doi.org/10.3390/s17030626 - 20 Mar 2017
Cited by 31 | Viewed by 6971
Abstract
Hydrogen sulfide (H2S) has attracted attention in biochemical research because it plays an important role in biosystems and has emerged as the third endogenous gaseous signaling compound along with nitric oxide (NO) and carbon monoxide (CO). Since H2S is [...] Read more.
Hydrogen sulfide (H2S) has attracted attention in biochemical research because it plays an important role in biosystems and has emerged as the third endogenous gaseous signaling compound along with nitric oxide (NO) and carbon monoxide (CO). Since H2S is a kind of gaseous molecule, conventional approaches for H2S detection are mostly based on the detection of sulfide (S2−) for indirectly reflecting H2S levels. Hence, there is a need for an accurate and reliable assay capable of determining sulfide in physiological systems. We report here a colorimetric, economic, and green method for sulfide anion detection using in situ formation of silver nanoparticles (AgNPs) using dopamine as a reducing and protecting agent. The changes in the AgNPs absorption response depend linearly on the concentration of Na2S in the range from 2 to 15 μM, with a detection limit of 0.03 μM. Meanwhile, the morphological changes in AgNPs in the presence of S2− and thiol compounds were characterized by transmission electron microscopy (TEM). The as-synthetized AgNPs demonstrate high selectivity, free from interference, especially by other thiol compounds such as cysteine and glutathione. Furthermore, the colorimetric sensor developed was applied to the analysis of sulfide in fetal bovine serum and spiked serum samples with good recovery. Full article
(This article belongs to the Section Chemical Sensors)
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<p>(<b>A</b>) UV-Vis absorption responses of AgNPs in the absence (curve a) and presence of different concentrations of S<sup>2−</sup> (from curve a to curve k: 0, 0.1, 0.5, 1, 2, 3, 5, 7, 10, 15, 20 μM). The color changes of AgNPs before (the first tube) and after the reaction with different concentrations of S<sup>2−</sup> (from left to right: 0, 0.5, 1, 3, 5, 7, 10, 15, 20 μM) are shown in the inset. The reation time, 20 min. (<b>B</b>) UV-Vis absorption responses versus the S<sup>2−</sup> concentration. The reation time, 20 min.</p>
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<p>TEM images of dopamine-functionalized AgNPs in the absence (<b>A</b>) and the presence (<b>B</b>) of 10 μM·S<sup>2−</sup>. The reation time, 20 min. (<b>C</b>) Energy disperse X-ray spectroscope (EDS) spectrum of dopamine-functionalized AgNPs in the presence of 10 μM·S<sup>2−</sup>.</p>
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<p>UV-Vis absorption responses of AgNPs in the absence (curve a) and presence of different concentrations of (<b>A</b>) Cys (from curve a to g: 0, 10, 15, 20, 30, 50, 100 μM), (<b>B</b>) Hcy (from curve a to g: 0, 10, 15, 20, 30, 50, 100 μM), (<b>C</b>) GSH (from curve a to f: 0, 10, 20, 30, 50, 100 μM), (<b>D</b>) GSSG(from curve a to g: 0, 10, 15, 20, 30, 50, 100 μM), and (<b>E</b>) MUA (from curve a to f: 0, 10, 20, 30, 50, 100 μM), and (<b>F</b>) N-cys (from curve a to g: 0, 10, 15, 20, 30, 50, 100 μM). The color changes of AgNPs before (the first tube) and after the reaction with different concentrations of (<b>A</b>) Cys (from curve a to g: 0, 10, 15, 20, 30, 50, 100 μM), (<b>B</b>) Hcy(from curve a to g: 0, 10, 15, 20, 30, 50, 100 μM), (<b>C</b>) GSH (from curve a to g: 0, 10, 15, 20, 30, 50, 100 μM) are shown in the corresponding inset. The reation time, 2 h.</p>
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<p>(<b>A</b>) UV-Vis absorption responses of AgNPs in the absence (curve a) and presence of different concentrations of DTT (from curve a to j: 0, 1, 3, 5, 10, 15, 20, 30, 40, 50 μM). The color changes of AgNPs before (the first tube) and after the reaction with different concentrations of captopril (from curve a to i: 0, 1, 3, 5, 10, 20, 30, 40, 50 μM) are shown in the corresponding inset. The reation time, 20 min. (<b>B</b>) UV-Vis absorption responses versus the DTT concentration.</p>
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<p>TEM image of dopamine-functionalized AgNPs in the presence of (<b>A</b>) and (<b>B</b>) 20 μM DTT, and (<b>C</b>) 50 μM Cys, (<b>D</b>) 50 μM GSH. The reation time for DTT, 20 min; The reation time for Cys and GSH, 2 h.</p>
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<p>TEM image of dopamine-functionalized AgNPs in the presence of (<b>A</b>) and (<b>B</b>) 20 μM DTT, and (<b>C</b>) 50 μM Cys, (<b>D</b>) 50 μM GSH. The reation time for DTT, 20 min; The reation time for Cys and GSH, 2 h.</p>
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<p>(<b>A</b>) The color changes and UV-Vis absorption responses of AgNPs in the absence (curve a) and presence of different species (from curve b to t: tryptophan, histidine, proline, alanine, lysine, phenylalanine, leucine, threonine, arginine, aspartic acid, glycine, valine, serine, glutamine, methionine, tyrosine, 40 μM; 0.01% BSA and S<sup>2−</sup>, 20 μM ). (<b>B</b>) The color changes and UV-Vis absorption responses of AgNPs in the absence (curve a) and presence of different species (from curve b to l: glucose, glutamic acid, 5-hydroxytryptamine, uric acid, lactate, ATP, norepinephrine, hypoxanthine, H<sub>2</sub>O<sub>2</sub>, and sodium ascorbate, 20 μM and S<sup>2−</sup>, 20 μM ). The reation time for S<sup>2−</sup> and DTT, 20 min. The reation time for other species, 2 h.</p>
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<p>UV-Vis absorption responses A<sub>400</sub> of AgNPs in the absence (black bars) and presence of various metal ions containing 50 μM EDTA. The concentration of Fe<sup>3+</sup>, Fe<sup>2+</sup>, Zn<sup>2+</sup>, Cu<sup>2+</sup>, S<sup>2−</sup>, Al<sup>3+</sup>, 20 μM; Cr<sup>3+</sup>, Cd<sup>3</sup>, Co<sup>2+</sup>,5 μM; Mg<sup>2+</sup>, Na<sup>+</sup>, K<sup>+</sup>, Ca<sup>2+</sup>, 50 μM; Photographs from curve left to right: AgNPs with 80 μM EDTA, AgNPs with 20 μM Cu<sup>2+</sup> and 80 μM EDTA, AgNPs with 20 μM Cu<sup>2+</sup>. The reation time for metal ions, 2 h.</p>
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<p>Proposed mechanism of colorimetric determination of S<sup>2−</sup>, DTT, biothiols with silver/dopamine nanoparticles.</p>
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1358 KiB  
Article
Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations
by Jian Ou, Yongguang Chen, Feng Zhao, Jin Liu and Shunping Xiao
Sensors 2017, 17(3), 632; https://doi.org/10.3390/s17030632 - 19 Mar 2017
Cited by 28 | Viewed by 5191
Abstract
The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR [...] Read more.
The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity. Full article
(This article belongs to the Special Issue Non-Contact Sensing)
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<p>The hierarchical structure of MFR signals. (<b>a</b>) A radar word structure of the “Mercury” radar; and (<b>b</b>) layered signal structure of a hierarchical MFR example.</p>
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<p>The process for MFR behaviour recognition and prediction based on PSR.</p>
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<p>Operating mode transition of the MFR.</p>
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<p>Recognition for MFR operating modes based on three recognition algorithms. (<b>a</b>) Probability distribution estimation based on HMM; (<b>b</b>) recognition based on HMM; (<b>c</b>) probability distribution estimation based on PSR and grid-filter; (<b>d</b>) recognition based on PSR and grid-filter; (<b>e</b>) probability distribution estimation based on PSR and proposed method; and (<b>f</b>) recognition based on PSR and proposed method.</p>
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<p>Recognition rate for the two mode recognition algorithms versus the impact factors.</p>
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<p>Prediction accuracy and efficiency of the normal algorithm and fast algorithm. (<b>a</b>) Prediction rate versus the number of prediction steps; and (<b>b</b>) runtime versus the number of prediction steps.</p>
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