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Sensors, Volume 16, Issue 9 (September 2016) – 206 articles

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18272 KiB  
Article
Digital Platform for Wafer-Level MEMS Testing and Characterization Using Electrical Response
by Nuno Brito, Carlos Ferreira, Filipe Alves, Jorge Cabral, João Gaspar, João Monteiro and Luís Rocha
Sensors 2016, 16(9), 1553; https://doi.org/10.3390/s16091553 - 21 Sep 2016
Cited by 9 | Viewed by 8650
Abstract
The uniqueness of microelectromechanical system (MEMS) devices, with their multiphysics characteristics, presents some limitations to the borrowed test methods from traditional integrated circuits (IC) manufacturing. Although some improvements have been performed, this specific area still lags behind when compared to the design and [...] Read more.
The uniqueness of microelectromechanical system (MEMS) devices, with their multiphysics characteristics, presents some limitations to the borrowed test methods from traditional integrated circuits (IC) manufacturing. Although some improvements have been performed, this specific area still lags behind when compared to the design and manufacturing competencies developed over the last decades by the IC industry. A complete digital solution for fast testing and characterization of inertial sensors with built-in actuation mechanisms is presented in this paper, with a fast, full-wafer test as a leading ambition. The full electrical approach and flexibility of modern hardware design technologies allow a fast adaptation for other physical domains with minimum effort. The digital system encloses a processor and the tailored signal acquisition, processing, control, and actuation hardware control modules, capable of the structure position and response analysis when subjected to controlled actuation signals in real time. The hardware performance, together with the simplicity of the sequential programming on a processor, results in a flexible and powerful tool to evaluate the newest and fastest control algorithms. The system enables measurement of resonant frequency (Fr), quality factor (Q), and pull-in voltage (Vpi) within 1.5 s with repeatability better than 5 ppt (parts per thousand). A full-wafer with 420 devices under test (DUTs) has been evaluated detecting the faulty devices and providing important design specification feedback to the designers. Full article
(This article belongs to the Collection Modeling, Testing and Reliability Issues in MEMS Engineering)
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Figure 1
<p>Capacitive microelectromechanical system (MEMS) sensor with sensing and actuation electrodes highlighted areas.</p>
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<p>Block diagram of the MEMS test and characterization system, including hardware- and software-embedded blocks. (DUT: device under test; FPGA: Field programmable gate array).</p>
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<p>Digital lock-in amplifier logic blocks. (LPF: Low-pass filter).</p>
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<p>Band-pass finite impulse response (FIR) filter adjusted by changing the sampling frequency.</p>
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<p>Pull-in ramp with highlighted detection points. The immediate actuation voltage removal is evident to avoid hitting the counter electrodes.</p>
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<p>Resonant frequency proportional controller blocks.</p>
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<p>Image of the prototyped digital platform.</p>
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<p>Output response of the capacitive readout block, including digital lock-in.</p>
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<p>Step responses of the structure.</p>
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<p>SEM image of a MEMS DUT.</p>
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<p>Full measurement cycle overview (actuation and voltage readout) (<b>a</b>) Pull-in voltages measurement cycle; (<b>b</b>) resonant frequency measurement cycle with inset showing 90° phase shift at resonance frequency; and (<b>c</b>) quality factor measurement cycle.</p>
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<p>Characterization values for four different types of devices, capturing the design differences between devices.</p>
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<p>A 200 mm processed silicon-on-insulator (SOI) wafer with 21 reticules (420 DUT). Each reticule contains 20 MEMS devices (five different configurations, M1–M5).</p>
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<p>Semiautomated probe during wafer-level testing.</p>
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<p>Results from the process yield.</p>
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<p>Cumulative distribution curves for resonance frequency, quality factor, and initial capacitance for the five different layouts (M1–M5).</p>
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<p>Over-etch wafer map.</p>
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2559 KiB  
Article
A Small Range Six-Axis Accelerometer Designed with High Sensitivity DCB Elastic Element
by Zhibo Sun, Jinhao Liu, Chunzhan Yu and Yili Zheng
Sensors 2016, 16(9), 1552; https://doi.org/10.3390/s16091552 - 21 Sep 2016
Cited by 20 | Viewed by 7694
Abstract
This paper describes a small range six-axis accelerometer (the measurement range of the sensor is ±g) with high sensitivity DCB (Double Cantilever Beam) elastic element. This sensor is developed based on a parallel mechanism because of the reliability. The accuracy of sensors is [...] Read more.
This paper describes a small range six-axis accelerometer (the measurement range of the sensor is ±g) with high sensitivity DCB (Double Cantilever Beam) elastic element. This sensor is developed based on a parallel mechanism because of the reliability. The accuracy of sensors is affected by its sensitivity characteristics. To improve the sensitivity, a DCB structure is applied as the elastic element. Through dynamic analysis, the dynamic model of the accelerometer is established using the Lagrange equation, and the mass matrix and stiffness matrix are obtained by a partial derivative calculation and a conservative congruence transformation, respectively. By simplifying the structure of the accelerometer, a model of the free vibration is achieved, and the parameters of the sensor are designed based on the model. Through stiffness analysis of the DCB structure, the deflection curve of the beam is calculated. Compared with the result obtained using a finite element analysis simulation in ANSYS Workbench, the coincidence rate of the maximum deflection is 89.0% along the x-axis, 88.3% along the y-axis and 87.5% along the z-axis. Through strain analysis of the DCB elastic element, the sensitivity of the beam is obtained. According to the experimental result, the accuracy of the theoretical analysis is found to be 90.4% along the x-axis, 74.9% along the y-axis and 78.9% along the z-axis. The measurement errors of linear accelerations ax, ay and az in the experiments are 2.6%, 0.6% and 1.31%, respectively. The experiments prove that accelerometer with DCB elastic element performs great sensitive and precision characteristics. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1
<p>Structure of the six-axis accelerometer: (<b>a</b>) three-dimensional model of a small range six-axis accelerometer; and (<b>b</b>) theoretical model of the six-axis accelerometer.</p>
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<p>Structure of the elastic leg. (<b>a</b>) Front side of the elastic leg; (<b>b</b>) 3D model of the elastic leg.</p>
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<p>Force analysis of the double cantilever beam (DCB) elastic leg.</p>
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<p>Deformation of the six beams: (<b>a</b>) deformation under a load <span class="html-italic"><b>g</b><sub>x</sub></span> on the moving platform; (<b>b</b>) deformation under a load <span class="html-italic"><b>g</b><sub>y</sub></span> on the moving platform; and (<b>c</b>) deformation under a load <span class="html-italic"><b>g</b><sub>z</sub></span> on the moving platform.</p>
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<p>Deformation of the six beams in ANSYS Workbench: (<b>a</b>) deformation under a load <span class="html-italic"><b>g</b><sub>x</sub></span> on the moving platform in ANSYS Workbench; (<b>b</b>) deformation under a load <span class="html-italic"><b>g</b><sub>y</sub></span> on the moving platform in ANSYS Workbench; and (<b>c</b>) deformation under a load <span class="html-italic"><b>g</b><sub>z</sub></span> on the moving platform in ANSYS Workbench.</p>
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<p>The location of the strain gauges and the bridge arrangement for one element: (<b>a</b>) the location of the strain gauges; and (<b>b</b>) bridge arrangement.</p>
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<p>Calibration test for linear sensitivity of the accelerometer: (<b>a</b>) the calibration system for the six-axis accelerometer; and (<b>b</b>) prototype of the elastic element of the six-axis accelerometer.</p>
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4349 KiB  
Article
A Portable Laser Photoacoustic Methane Sensor Based on FPGA
by Jianwei Wang, Huili Wang and Xianyong Liu
Sensors 2016, 16(9), 1551; https://doi.org/10.3390/s16091551 - 21 Sep 2016
Cited by 14 | Viewed by 7440
Abstract
A portable laser photoacoustic sensor for methane (CH4) detection based on a field-programmable gate array (FPGA) is reported. A tunable distributed feedback (DFB) diode laser in the 1654 nm wavelength range is used as an excitation source. The photoacoustic signal processing [...] Read more.
A portable laser photoacoustic sensor for methane (CH4) detection based on a field-programmable gate array (FPGA) is reported. A tunable distributed feedback (DFB) diode laser in the 1654 nm wavelength range is used as an excitation source. The photoacoustic signal processing was implemented by a FPGA device. A small resonant photoacoustic cell is designed. The minimum detection limit (1σ) of 10 ppm for methane is demonstrated. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic configuration of the PA methane sensor.</p>
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<p>The photograph of this PA methane sensor.</p>
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<p>(<b>a</b>) The PA cell and (<b>b</b>) the constant temperature treatment device.</p>
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<p>The resonance profiles showing the first longitude resonance frequency is 2450 Hz.</p>
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<p>Schematic diagram of the FPGA based lock in amplifier.</p>
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<p>Absorption spectrum of the gases (H<sub>2</sub>O, CO<sub>2</sub> and CH<sub>4</sub>) obtained from the HITRAN database.</p>
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<p>Measured 1<span class="html-italic">f</span> spectra of 200 ppm CH<sub>4</sub>.</p>
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<p>PA signals to different concentrations. Error bars give the range of the corresponding PA signals.</p>
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<p>Measured dilution process of the 5000 ppm CH<sub>4</sub> reference concentration.</p>
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<p>Continuous measurements of 2000 ppm CH<sub>4</sub>.</p>
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4079 KiB  
Article
Design and Implementation of Sound Searching Robots in Wireless Sensor Networks
by Lianfu Han, Zhengguang Shen, Changfeng Fu and Chao Liu
Sensors 2016, 16(9), 1550; https://doi.org/10.3390/s16091550 - 21 Sep 2016
Cited by 13 | Viewed by 7121
Abstract
A sound target-searching robot system which includes a 4-channel microphone array for sound collection, magneto-resistive sensor for declination measurement, and a wireless sensor networks (WSN) for exchanging information is described. It has an embedded sound signal enhancement, recognition and location method, and a [...] Read more.
A sound target-searching robot system which includes a 4-channel microphone array for sound collection, magneto-resistive sensor for declination measurement, and a wireless sensor networks (WSN) for exchanging information is described. It has an embedded sound signal enhancement, recognition and location method, and a sound searching strategy based on a digital signal processor (DSP). As the wireless network nodes, three robots comprise the WSN a personal computer (PC) in order to search the three different sound targets in task-oriented collaboration. The improved spectral subtraction method is used for noise reduction. As the feature of audio signal, Mel-frequency cepstral coefficient (MFCC) is extracted. Based on the K-nearest neighbor classification method, we match the trained feature template to recognize sound signal type. This paper utilizes the improved generalized cross correlation method to estimate time delay of arrival (TDOA), and then employs spherical-interpolation for sound location according to the TDOA and the geometrical position of the microphone array. A new mapping has been proposed to direct the motor to search sound targets flexibly. As the sink node, the PC receives and displays the result processed in the WSN, and it also has the ultimate power to make decision on the received results in order to improve their accuracy. The experiment results show that the designed three-robot system implements sound target searching function without collisions and performs well. Full article
(This article belongs to the Special Issue Advanced Robotics and Mechatronics Devices)
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<p>Architecture of three-robot system in WSN.</p>
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<p>Photograph of a mobile robot.</p>
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<p>Arrangement of the 4-chnanel microphone array.</p>
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<p>Functional architecture of the mobile robots in the WSN.</p>
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<p>Results of experimental signal after ISS with SNR = 0 dB.</p>
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<p>Functional architecture of template matching.</p>
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<p>Architecture of TDOA by IGCC.</p>
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<p>TDOA result of traditional GCC and IGCC.</p>
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<p>Diagram illustrating geometric relations among <span class="html-italic">m<sub>i</sub></span>, <span class="html-italic">m<sub>j</sub></span> and <b>S</b>.</p>
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<p>New mapping of heading angle deviation and PWM duty ratio.</p>
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<p>Schematic architecture of DSP system.</p>
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<p>Experimental process.</p>
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<p>PC interface.</p>
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2991 KiB  
Article
Underdetermined DOA Estimation Using MVDR-Weighted LASSO
by Amgad A. Salama, M. Omair Ahmad and M. N. S. Swamy
Sensors 2016, 16(9), 1549; https://doi.org/10.3390/s16091549 - 21 Sep 2016
Cited by 14 | Viewed by 7313
Abstract
The direction of arrival (DOA) estimation problem is formulated in a compressive sensing (CS) framework, and an extended array aperture is presented to increase the number of degrees of freedom of the array. The ordinary least square adaptable least absolute shrinkage and selection [...] Read more.
The direction of arrival (DOA) estimation problem is formulated in a compressive sensing (CS) framework, and an extended array aperture is presented to increase the number of degrees of freedom of the array. The ordinary least square adaptable least absolute shrinkage and selection operator (OLS A-LASSO) is applied for the first time for DOA estimation. Furthermore, a new LASSO algorithm, the minimum variance distortionless response (MVDR) A-LASSO, which solves the DOA problem in the CS framework, is presented. The proposed algorithm does not depend on the singular value decomposition nor on the orthogonality of the signal and the noise subspaces. Hence, the DOA estimation can be done without a priori knowledge of the number of sources. The proposed algorithm can estimate up to ( ( M 2 2 ) / 2 + M 1 ) / 2 sources using M sensors without any constraints or assumptions about the nature of the signal sources. Furthermore, the proposed algorithm exhibits performance that is superior compared to that of the classical DOA estimation methods, especially for low signal to noise ratios (SNR), spatially-closed sources and coherent scenarios. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1
<p>A flowchart of the algorithm for adaptable (A)-LASSO-based DOA estimation.</p>
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<p>(<b>a</b>) The data residual <math display="inline"> <semantics> <msubsup> <mfenced separators="" open="&#x2225;" close="&#x2225;"> <mi mathvariant="bold">y</mi> <mo>−</mo> <msup> <mi mathvariant="normal">Φ</mi> <mo>*</mo> </msup> <mover accent="true"> <mi mathvariant="bold">s</mi> <mo stretchy="false">¯</mo> </mover> </mfenced> <mn>2</mn> <mn>2</mn> </msubsup> </semantics> </math> versus the solution <math display="inline"> <semantics> <msub> <mi>ℓ</mi> <mn>1</mn> </msub> </semantics> </math>-norm linear scale on a log-log scale (L-curve); (<b>b</b>) DOA estimation for two source signals; <span class="html-italic">τ</span> was selected using L-curve, in the MVDR A-LASSO problem (SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> dB).</p>
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<p>(<b>a</b>) The data residual <math display="inline"> <semantics> <msubsup> <mfenced separators="" open="&#x2225;" close="&#x2225;"> <mi mathvariant="bold">y</mi> <mo>−</mo> <msup> <mi mathvariant="normal">Φ</mi> <mo>*</mo> </msup> <mover accent="true"> <mi mathvariant="bold">s</mi> <mo stretchy="false">¯</mo> </mover> </mfenced> <mn>2</mn> <mn>2</mn> </msubsup> </semantics> </math> versus the solution <math display="inline"> <semantics> <msub> <mi>ℓ</mi> <mn>1</mn> </msub> </semantics> </math>-norm linear scale on a log-log scale (L-curve); (<b>b</b>) DOA estimation for two source signals; <span class="html-italic">τ</span> was selected using L-curve, in the MVDR A-LASSO problem (SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> dB).</p>
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<p>The proposed sparse (<b>upper</b>) and the virtual co-array (<b>lower</b>).</p>
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<p>Performance of LASSO, OLS A-LASSO and MVDR A-LASSO, for two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, 10 snapshots, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> dB and one iteration. (<b>a</b>) LASSO; (<b>b</b>) OLS A-LASSO; and (<b>c</b>) MVDR A-LASSO.</p>
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<p>Performance of the three LASSO algorithms versus SNR, for two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, 10 snapshots and after one iteration of the MVDR A-LASSO and OLS A-LASSO algorithms.</p>
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<p>Performance of the LASSO algorithms as SNR is varied in comparison with that of MVDR and MUSIC algorithms, for two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, 10 snapshots and after one iteration of the MVDR A-LASSO and OLS A-LASSO algorithms.</p>
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<p>DOA estimation when the number of sources is more than the number of sensors: (<b>a</b>) After one iteration of OLS A-LASSO; (<b>b</b>) After one iteration of MVDR A-LASSO; (<b>c</b>) Classical LASSO and MVDR using a six-element array; (<b>d</b>) Classical LASSO, MVDR and MUSIC using a 23-element array; (<b>e</b>) MVDR and MUSIC using a 23-element array.</p>
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<p>DOA estimation for spatially-closed two-source signals using LASSO algorithms, for two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>85</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>95</mn> <mo>∘</mo> </msup> </semantics> </math>, 10 snapshots, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mn>15</mn> </mrow> </semantics> </math> dB and one iteration of the MVDR A-LASSO and OLS A-LASSO algorithms. (<b>a</b>) OLS A-LASSO after the first iteration; (<b>b</b>) MVDR A-LASSO after the first iteration; and (<b>c</b>) the classical LASSO algorithm.</p>
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<p>DOA estimation for two correlated source signals using LASSO algorithms, for two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>100</mn> <mo>∘</mo> </msup> </semantics> </math>, 10 snapshots, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mn>15</mn> </mrow> </semantics> </math> dB and one iteration of the MVDR A-LASSO and OLS A-LASSO algorithms. (<b>a</b>) OLS A-LASSO after the first iteration; (<b>b</b>) MVDR A-LASSO after the first iteration; and (<b>c</b>) the classical LASSO algorithm.</p>
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<p>DOA estimation using A-LASSO algorithms, for two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, 10 snapshots, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> dB. (<b>a</b>) MVDR A-LASSO after the first iteration; (<b>b</b>) OLS A-LASSO after the first iteration; (<b>c</b>) OLS A-LASSO after five iterations; and (<b>d</b>) initial weights of the two algorithms.</p>
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<p>DOA estimation of two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, and 10 snapshots (<b>a</b>) after five iterations and (<b>b</b>) after 15 iterations, using the A-LASSO algorithms.</p>
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<p>DOA estimation in the case of two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics> </math> dB, 50 snapshots using MVDR A-LASSO algorithm. (<b>a</b>–<b>e</b>) after <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>−</mo> <mn>5</mn> </mrow> </semantics> </math> iterations; and (<b>f</b>) MVDR A-LASSO weights as the number of iterations <span class="html-italic">k</span> varies.</p>
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<p>DOA estimation in the case of two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>10</mn> </mrow> </semantics> </math> dB, 150 snapshots, using the MVDR A-LASSO algorithm. (<b>a</b>) to (<b>e</b>), after one to five iterations; and (<b>f</b>) MVDR A-LASSO weights as the number of iterations <span class="html-italic">k</span> varies.</p>
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<p>DOA estimation, two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>15</mn> </mrow> </semantics> </math> dB, 200 snapshots, using the MVDR A-LASSO algorithm. (<b>a</b>) to (<b>e</b>) after one to five iterations; and (<b>f</b>) MVDR A-LASSO weights as the number of iterations <span class="html-italic">k</span> varies.</p>
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<p>MVDR A-LASSO DOA estimation performance versus the number of snapshots, two source signals with DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics> </math>.</p>
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<p>The residual for two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, with <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> <mo>,</mo> <mn>0.5</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mn>0.75</mn> </mrow> </semantics> </math>, 10 iterations, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics> </math> dB using the MVDR A-LASSO algorithm, (<b>a</b>) 10 snapshots and (<b>b</b>) 50 snapshots.</p>
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<p>DOA estimation of two source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, 50 snapshot, SNR <math display="inline"> <semantics> <mrow> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics> </math> dB, using the MVDR A-LASSO algorithm, (<b>a</b>)–(<b>d</b>) <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics> </math>; (<b>e</b>) <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.50</mn> </mrow> </semantics> </math>; and (<b>f</b>) <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics> </math>.</p>
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<p>DOA estimation error for two sources as a function of separation between the two sources, SNR = 10 dB, 10 snapshots and one iteration of MVDR A-LASSO.</p>
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<p>Wideband DOA estimation, two chirp source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mspace width="3.33333pt"/> <mo>=</mo> <mspace width="3.33333pt"/> <mn>0.5</mn> </mrow> </semantics> </math>, 10 snapshots, using uniform linear array (ULA) containing six sensors for conventional beamforming and the MUSIC algorithm. (<b>a</b>,<b>b</b>) The MVDR A-LASSO algorithm (after the first iteration); (<b>c</b>,<b>d</b>) Conventional beamforming; and (<b>e</b>,<b>f</b>) the MUSIC algorithm.</p>
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<p>Wideband DOA estimation, two chirp source signals at DOAs <math display="inline"> <semantics> <msup> <mn>60</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>120</mn> <mo>∘</mo> </msup> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics> </math>, 10 snapshots, using ULA containing 23 sensors for conventional beamforming and MUSIC algorithm. (<b>a</b>,<b>b</b>) The MVDR A-LASSO algorithm (after the first iteration); (<b>c</b>,<b>d</b>) Conventional beamforming; and (<b>e</b>,<b>f</b>) the MUSIC algorithm.</p>
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1122 KiB  
Article
Electronic Nose Testing Procedure for the Definition of Minimum Performance Requirements for Environmental Odor Monitoring
by Lidia Eusebio, Laura Capelli and Selena Sironi
Sensors 2016, 16(9), 1548; https://doi.org/10.3390/s16091548 - 21 Sep 2016
Cited by 44 | Viewed by 9168
Abstract
Despite initial enthusiasm towards electronic noses and their possible application in different fields, and quite a lot of promising results, several criticalities emerge from most published research studies, and, as a matter of fact, the diffusion of electronic noses in real-life applications is [...] Read more.
Despite initial enthusiasm towards electronic noses and their possible application in different fields, and quite a lot of promising results, several criticalities emerge from most published research studies, and, as a matter of fact, the diffusion of electronic noses in real-life applications is still very limited. In general, a first step towards large-scale-diffusion of an analysis method, is standardization. The aim of this paper is describing the experimental procedure adopted in order to evaluate electronic nose performances, with the final purpose of establishing minimum performance requirements, which is considered to be a first crucial step towards standardization of the specific case of electronic nose application for environmental odor monitoring at receptors. Based on the experimental results of the performance testing of a commercialized electronic nose type with respect to three criteria (i.e., response invariability to variable atmospheric conditions, instrumental detection limit, and odor classification accuracy), it was possible to hypothesize a logic that could be adopted for the definition of minimum performance requirements, according to the idea that these are technologically achievable. Full article
(This article belongs to the Special Issue E-noses: Sensors and Applications)
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<p>Electronic noses used for the tests in laboratory (<b>a</b>) and EOS 507 in the field (<b>b</b>).</p>
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<p>Examples of responses of sensor 1 in terms of resistance variation (Ω) to Acetone (<b>a</b>) and Ethanol (<b>b</b>) samples at 10 different dilution steps (from 10% to 100% of the original sample).</p>
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3032 KiB  
Article
Features of a Self-Mixing Laser Diode Operating Near Relaxation Oscillation
by Bin Liu, Yanguang Yu, Jiangtao Xi, Yuanlong Fan, Qinghua Guo, Jun Tong and Roger A. Lewis
Sensors 2016, 16(9), 1546; https://doi.org/10.3390/s16091546 - 21 Sep 2016
Cited by 11 | Viewed by 6231
Abstract
When a fraction of the light reflected by an external cavity re-enters the laser cavity, both the amplitude and the frequency of the lasing field can be modulated. This phenomenon is called the self-mixing effect (SME). A self-mixing laser diode (SM-LD) is a [...] Read more.
When a fraction of the light reflected by an external cavity re-enters the laser cavity, both the amplitude and the frequency of the lasing field can be modulated. This phenomenon is called the self-mixing effect (SME). A self-mixing laser diode (SM-LD) is a sensor using the SME. Usually, such LDs operate below the stability boundary where no relaxation oscillation happens. The boundary is determined by the operation condition including the injection current, optical feedback strength and external cavity length. This paper discovers the features of an SM-LD where the LD operates beyond the stability boundary, that is, near the relaxation oscillation (RO) status. We call the signals from such a SM-LD as RO-SM signals to differentiate them from the conventional SM signals reported in the literature. Firstly, simulations are made based on the well-known Lang and Kobayashi (L-K) equations. Then the experiments are conducted on different LDs to verify the simulation results. It shows that a RO-SM signal exhibits high frequency oscillation with its amplitude modulated by a slow time varying envelop which corresponds to the movement of the external target. The envelope has same fringe structure (half-wavelength displacement resolution) with the conventional SM signals. However, the amplitudes of the RO-SM signals are much higher compared to conventional SM signals. The results presented reveal that an SM-LD operating near the RO has potential for achieving sensing with improved sensitivity. Full article
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<p>Improved stability boundary for describing an SMI system when <math display="inline"> <semantics> <mrow> <msub> <mi>L</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>35</mn> <mtext> </mtext> <mi>cm</mi> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>J</mi> <mo>=</mo> <mn>1.1</mn> <mi>J</mi> <mi>th</mi> </mrow> </semantics> </math>.</p>
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<p>Modulated laser intensity at different regions and their corresponding spectra, where the laser intensity is scaled by <math display="inline"> <semantics> <mrow> <msup> <mi>E</mi> <mn>2</mn> </msup> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> <mo>/</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>20</mn> </mrow> </msup> </mrow> </semantics> </math> (<b>a</b>) displacement of the external target; (<b>b</b>–<b>f</b>) laser intensity when <span class="html-italic">C</span> = 9, <span class="html-italic">C</span> = 5, <span class="html-italic">C</span> = 3.5, <span class="html-italic">C</span> = 2.5, and <span class="html-italic">C</span> = 1.5 respectively; (<b>g</b>–<b>k</b>) spectra corresponding to (<b>b</b>–<b>f</b>) respectively. Note that the DC component in each case is removed when applying FFT.</p>
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<p>Experimental setup.</p>
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<p>Experimental signals and their spectra in semi-stable and unstable regions. (<b>a</b>) PZT control signal; (<b>b</b>,<b>c</b>) RO-SM signals in semi-stable region; (<b>d</b>) SM signal in unstable region; (<b>e</b>–<b>g</b>) Spectra corresponding to (<b>b</b>–<b>d</b>) respectively.</p>
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<p>Experimental signals and their spectra in stable and semi-stable region. (<b>a</b>) PZT control signal; (<b>b</b>) conventional SM signals at stable region; (<b>c</b>,<b>d</b>) RO-SM signals at semi-stable region; (<b>e</b>–<b>g</b>) the spectra corresponding to (<b>b</b>–<b>d</b>) respectively.</p>
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<p>Experimental signals with <span class="html-italic">J</span> = 46 mA and <span class="html-italic">L</span><sub>0</sub> = 12.5 cm for DL5032-001 (<b>a</b>) PZT control signal; (<b>b</b>) conventional SM signals at stable region; (<b>c</b>,<b>d</b>) RO-SM signals at semi-stable region.</p>
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<p>Experimental signals for DL4140-001S. (<b>a</b>) PZT control signal; (<b>b</b>) conventional SM signals at stable region; (<b>c</b>) RO-SM signals at semi-stable region; (<b>d</b>) the laser intensity when the target is stationary; (<b>e</b>,<b>f</b>) the spectra corresponding to (<b>c</b>,<b>d</b>) respectively.</p>
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1392 KiB  
Article
Intensity-Stabilized Fast-Scanned Direct Absorption Spectroscopy Instrumentation Based on a Distributed Feedback Laser with Detection Sensitivity down to 4 × 10−6
by Gang Zhao, Wei Tan, Mengyuan Jia, Jiajuan Hou, Weiguang Ma, Lei Dong, Lei Zhang, Xiaoxia Feng, Xuechun Wu, Wangbao Yin, Liantuan Xiao, Ove Axner and Suotang Jia
Sensors 2016, 16(9), 1544; https://doi.org/10.3390/s16091544 - 21 Sep 2016
Cited by 13 | Viewed by 6811
Abstract
A novel, intensity-stabilized, fast-scanned, direct absorption spectroscopy (IS-FS-DAS) instrumentation, based on a distributed feedback (DFB) diode laser, is developed. A fiber-coupled polarization rotator and a fiber-coupled polarizer are used to stabilize the intensity of the laser, which significantly reduces its relative intensity noise [...] Read more.
A novel, intensity-stabilized, fast-scanned, direct absorption spectroscopy (IS-FS-DAS) instrumentation, based on a distributed feedback (DFB) diode laser, is developed. A fiber-coupled polarization rotator and a fiber-coupled polarizer are used to stabilize the intensity of the laser, which significantly reduces its relative intensity noise (RIN). The influence of white noise is reduced by fast scanning over the spectral feature (at 1 kHz), followed by averaging. By combining these two noise-reducing techniques, it is demonstrated that direct absorption spectroscopy (DAS) can be swiftly performed down to a limit of detection (LOD) (1σ) of 4 × 10−6, which opens up a number of new applications. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic diagram of the intensity-stabilization of a distributed feedback (DFB) laser; <span class="html-italic">f</span>-PR: fiber polarization rotator; <span class="html-italic">f</span>-Pol: fiber polarization; <span class="html-italic">f</span>-SP: fiber splitter; <span class="html-italic">f</span>-C; fiber coupler; PD: photodiode; PI: proportion- integration controller. The <span class="html-italic">f</span>-PR and <span class="html-italic">f</span>-Pol make jointly up the intensity controller (IC).</p>
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<p>(<b>a</b>) Transmission of linearly polarized light through the intensity stabilizer as a function of the voltage applied to the fiber polarization rotator (<span class="html-italic">f</span>-PR); Frequency dependence of the (<b>b</b>) amplitude and (<b>c</b>) phase responses of the PR. Solid squared markers represent individual data points. Blue solid lines make up curves to guide the eye through the individual data points. The blue dotted lines in panel (a) represent the x- and y-values of the nominal working position of the <span class="html-italic">f</span>-PR while those in panel (b) indicate the amplitudes and frequency for the 3-dB response. The dashed dotted lines represent the linear responses at the positions for close-to-linear response.</p>
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<p>(<b>a</b>) Long term monitoring of the light intensity with and without stabilization; the (<b>b</b>) corresponding noise spectra and (<b>c</b>) Allan-Werle deviations.</p>
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<p>Schematic diagram of the DFB-laser-based intensity-stabilized fast-scanned direct absorption (IS-FS-DAS) spectroscopy instrumentation used in this work. IS: intensity stabilization system; FG: function generation; PC: personal computer.</p>
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<p>Upper windows: A comparison of the transmitted intensity without (panel <b>a</b>) and with (panel <b>b</b>) intensity stabilization. The red curves represent measurements from an empty gas cell while the black ones correspond to the case when 16 ppm of C<sub>2</sub>H<sub>2</sub> is in the cell. Lower windows: The corresponding absorption coefficients created by the use of Beer’s law and the data in the upper windows.</p>
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<p>A comparison of absorption coefficients obtained by the use of the (<b>a</b>) conventional and (<b>b</b>) new DAS scheme for a given measurement time (1 s).</p>
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2036 KiB  
Article
Biomimetic Precapillary Flow Patterns for Enhancing Blood Plasma Separation: A Preliminary Study
by Bumseok Namgung, Justin Kok Soon Tan, Peter Agustinus Wong, Sung-Yong Park, Hwa Liang Leo and Sangho Kim
Sensors 2016, 16(9), 1543; https://doi.org/10.3390/s16091543 - 21 Sep 2016
Cited by 3 | Viewed by 6655
Abstract
In this study, a biomimetic microfluidic plasma separation device is discussed. The design of the device drew inspiration from in vivo observations of enhanced cell-free layer (CFL) formation downstream of vascular bifurcations. The working principle for the plasma separation was based on the [...] Read more.
In this study, a biomimetic microfluidic plasma separation device is discussed. The design of the device drew inspiration from in vivo observations of enhanced cell-free layer (CFL) formation downstream of vascular bifurcations. The working principle for the plasma separation was based on the plasma skimming effect in an arteriolar bifurcation, which is modulated by CFL formation. The enhancement of the CFL width was achieved by a local hematocrit reduction near the collection channel by creating an uneven hematocrit distribution at the bifurcation of the channel. The device demonstrated a high purity of separation (~99.9%) at physiological levels of hematocrit (~40%). Full article
(This article belongs to the Special Issue Biomicrofluidics)
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<p>In vivo observation of plasma skimming at an arteriolar bifurcation (<b>a</b>); And its corresponding schematic depicting the red blood cell (RBCs) flow trajectory (<b>b</b>); Dashed and solid arrows indicate the cell trajectory and flow direction, respectively. Dashed cells indicate an absence of RBCs in the daughter branch. Plasma from the cell-free layer (CFL) can be observed to skim off into the daughter branch.</p>
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<p>In vivo observation of CFL enhancement due to a local hematocrit reduction at an arteriolar bifurcation (<b>a</b>) and its corresponding illustration for RBCs trajectory (<b>b</b>); Dashed and solid arrows indicate the cell trajectory and flow direction, respectively. Bold cells represent RBCs of higher concentration in the main channel while gray cells indicate a lower concentration of RBCs in the daughter vessel.</p>
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<p>Schematic diagram of the microchannel design for plasma separation. The gray area indicates the region where CFL width measurements were performed. Due to the difference in flow resistance in channels 1 and 2, RBC flow was biased towards the channel with the higher flow rate, enhancing the CFL formation in the collection channel. The inset shows a photograph of the fabricated microchannel.</p>
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<p>Effect of inlet flow rate (<span class="html-italic">Q</span><sub>inlet</sub>) on the normalized CFL width with different angles (θ). The lines represent linear regression fits for each data set.</p>
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<p>Typical examples for plasma separation from 40% hematocrit blood samples with the bifurcation angle of θ = 60° at 20 µL/min (<b>a</b>); 40 µL/min (<b>b</b>) and 80 µL/min (<b>c</b>) of flow rate (<span class="html-italic">Q</span><sub>inlet</sub>).</p>
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<p>Pseudo hematocrit (Hct) of separated plasma as a function of CFL width. Solid and dashed lines represent a regression fit and 95% confidence band, respectively.</p>
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1906 KiB  
Article
Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems
by Abdul-Wahid Mohammed, Yang Xu, Haixiao Hu and Brighter Agyemang
Sensors 2016, 16(9), 1542; https://doi.org/10.3390/s16091542 - 21 Sep 2016
Cited by 7 | Viewed by 5694
Abstract
In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic [...] Read more.
In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic formation of collaborative functionality given high-level system goals has become practical. In this paper, we propose a cross-layer automation and management model for cyber-physical systems. This models the dynamic formation of collaborative services pursuing laid-down system goals as an ontology-oriented hierarchical task network. Ontological intelligence provides the semantic technology of this model, and through semantic reasoning, primitive tasks can be dynamically composed from high-level system goals. In dealing with uncertainty, we further propose a novel bridge between hierarchical task networks and Markov logic networks, called the Markov task network. This leverages the efficient inference algorithms of Markov logic networks to reduce both computational and inferential loads in task decomposition. From the results of our experiments, high-precision service composition under uncertainty can be achieved using this approach. Full article
(This article belongs to the Special Issue Real-Time and Cyber-Physical Systems)
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<p>Layered view of the service composition process from a workflow process.</p>
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<p>Hierarchical semantic task model.</p>
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<p>Capability and deployment modeling.</p>
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<p>Mapping between hierarchical semantic service (HSS) ontology and oneM2Mbase ontology for generic inter-working.</p>
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<p>An example of a nested conceptual graph of task decomposition.</p>
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<p>Ground Markov network (MN) obtained by applying MTN for the decomposition of task fireNotification <span class="html-italic">Task(FN)</span> into subtask activateFireAlarm <span class="html-italic">SubTask(AFA)</span>.</p>
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<p>Use case of an automatic fire control system.</p>
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<p>Single-stage task decomposition with single-stage training sets. (<b>a</b>) Fixed input with varying training set; (<b>b</b>) Varying input with fixed training set.</p>
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<p>Recursive decomposition and cross-validation of single-stage decomposition and recursive decomposition. (<b>a</b>) Recursive task decompostion using recursive decomposition training set; (<b>b</b>) Single-stage task decomposition using cross-training set; (<b>c</b>) Recursive task decomposition using cross-training set.</p>
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<p>Performance of propositional and lazy probabilistic inference algorithms. (<b>a</b>) Time complexity as a measure of performance; (<b>b</b>) Precision as a measure of performance.</p>
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<p>Example of resource modeling.</p>
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<p>Example of task modeling.</p>
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<p>Task operation of an operator.</p>
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<p>Task operation of a method.</p>
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3411 KiB  
Article
Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II)
by Guo Zhao, Hui Wang, Gang Liu and Zhiqiang Wang
Sensors 2016, 16(9), 1540; https://doi.org/10.3390/s16091540 - 21 Sep 2016
Cited by 20 | Viewed by 6935
Abstract
An easy, but effective, method has been proposed to detect and quantify the Pb(II) in the presence of Cd(II) based on a Bi/glassy carbon electrode (Bi/GCE) with the combination of a back propagation artificial neural network (BP-ANN) and square wave anodic stripping voltammetry [...] Read more.
An easy, but effective, method has been proposed to detect and quantify the Pb(II) in the presence of Cd(II) based on a Bi/glassy carbon electrode (Bi/GCE) with the combination of a back propagation artificial neural network (BP-ANN) and square wave anodic stripping voltammetry (SWASV) without further electrode modification. The effects of Cd(II) in different concentrations on stripping responses of Pb(II) was studied. The results indicate that the presence of Cd(II) will reduce the prediction precision of a direct calibration model. Therefore, a two-input and one-output BP-ANN was built for the optimization of a stripping voltammetric sensor, which considering the combined effects of Cd(II) and Pb(II) on the SWASV detection of Pb(II) and establishing the nonlinear relationship between the stripping peak currents of Pb(II) and Cd(II) and the concentration of Pb(II). The key parameters of the BP-ANN and the factors affecting the SWASV detection of Pb(II) were optimized. The prediction performance of direct calibration model and BP-ANN model were tested with regard to the mean absolute error (MAE), root mean square error (RMSE), average relative error (ARE), and correlation coefficient. The results proved that the BP-ANN model exhibited higher prediction accuracy than the direct calibration model. Finally, a real samples analysis was performed to determine trace Pb(II) in some soil specimens with satisfactory results. Full article
(This article belongs to the Special Issue Sensors for Environmental Monitoring 2016)
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<p>Schematic of an ANN structure to predict the concentration of Pb(II).</p>
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<p>Effects of (<b>a</b>) pH value; (<b>b</b>) Bi(III) concentration (<b>c</b>) deposition potential; and (<b>d</b>) deposition time on the stripping peak currents of 50 μg/L Pb(II) in the presence of 20 μg/L Cd(II).</p>
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<p>(<b>a</b>) Square wave anodic stripping voltammograms of 35 μg/L Pb(II) in 0.1 M acetate buffer solution (pH 5.0) on GCE and Bi/GCE. Deposition time: 140 s; deposition potential: −1.2 V; concentration of Bi(III): 600 μg/L; (<b>b</b>) Stripping current measurements of 25 μg/L Cd(II) and Pb(II) on Bi/GCE in 0.1 M acetate buffer solution (pH 5.0). The insets correspond to data collected from every SWASV response over nine repetitions. RSD: relative standard deviation.</p>
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<p>Voltamogrames for Pb(II) ranged from 1.0 to 110 μg/L in different concentrations of Cd(II) ranged from 0 to 110 μg/L: (<b>a</b>) 0 μg/L; (<b>b</b>) 1 μg/L; (<b>c</b>) 5 μg/L; (<b>d</b>) 10 μg/L; (<b>e</b>) 20 μg/L; (<b>f</b>) 40 μg/L; (<b>g</b>) 70 μg/L; (<b>h</b>) 110 μg/L. Deposition time: 140 s. Deposition potential: −1.2V. Concentration of Bi(III): 600 μg/L.</p>
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<p>(<b>a</b>) The effects of different concentrations of Cd(II) on the fitting curve of Pb(II); (<b>b</b>) The effects of different concentrations of Cd(II) on the stripping peak currents of Pb(II).</p>
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<p>Selection and optimization of the ANN model. The transfer functions and training functions: Logsig and Pureline (LP), Logsig and Logsig (LL), Pureline and Pureline (PP), Pureline and Logsig (PL), Trainbr (Tb), and Traingdx (Tg).</p>
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<p>The comparison of prediction results between (<b>a</b>) the direct calibration model and (<b>b</b>) the ANN model.</p>
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<p>Linear regression analysis of the prediction results obtained from (<b>a</b>) the direct calibration model and (<b>b</b>) the ANN model.</p>
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2446 KiB  
Article
Zwitterionic Surfactant Modified Acetylene Black Paste Electrode for Highly Facile and Sensitive Determination of Tetrabromobisphenol A
by Xiaoyun Wei, Qiang Zhao, Weixiang Wu, Tong Zhou, Shunli Jiang, Yeqing Tong and Qing Lu
Sensors 2016, 16(9), 1539; https://doi.org/10.3390/s16091539 - 21 Sep 2016
Cited by 16 | Viewed by 6111
Abstract
A electrochemical sensor for the highly sensitive detection of tetrabromobisphenol A (TBBPA) was fabricated based on acetylene black paste electrode (ABPE) modified with 3-(N,N-Dimethylpalmitylammonio) propanesulfonate (SB3-16) in this study. The peak current of TBBPA was significantly enhanced at SB3-16/ABPE [...] Read more.
A electrochemical sensor for the highly sensitive detection of tetrabromobisphenol A (TBBPA) was fabricated based on acetylene black paste electrode (ABPE) modified with 3-(N,N-Dimethylpalmitylammonio) propanesulfonate (SB3-16) in this study. The peak current of TBBPA was significantly enhanced at SB3-16/ABPE compared with unmodified electrodes. To further improve the electrochemical performance of the modified electrode, corresponding experimental parameters such as the length of hydrophobic chains of zwitterionic surfactant, the concentration of SB3-16, pH value, and accumulation time were examined. The peak currents of TBBPA were found to be linearly correlated with its concentrations in the range of 1 nM to 1 µM, with a detection limit of 0.4 nM. Besides, a possible mechanism was also discussed, and the hydrophobic interaction between TBBPA and the surfactants was suggested to take a leading role in enhancing the responses. Finally, this sensor was successfully employed to detect TBBPA in water samples. Full article
(This article belongs to the Special Issue Chemiresistive Sensors)
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<p>(<b>A</b>) Cyclic voltammograms of CPE (a), ABPE (b), SB3-16/CPE (c), and SB3-16/ABPE (d) in 0.1 M KCl solution containing 1.0 mM [Fe(CN)<sub>6</sub>]<sup>4−/3−</sup> with scan rate of 100 mV·s<sup>−1</sup>. Inset: Plot of <span class="html-italic">I</span><sub>p</sub> versus <span class="html-italic">ν</span><sup>1/2</sup> (V·s<sup>−1</sup>)<sup>1/2</sup> at CPE (a′), AB-CPE (b′), SB3-16/CPE (c’) and SB3-16/ABPE (d′); (<b>B</b>) Nyquist plots of SB3-16/ABPE (a), SB3-16/CPE (b), ABPE (c), and CPE (d) in 0.1 M KCl solution containing 5.0 mM [Fe(CN)<sub>6</sub>]<sup>4−/3−</sup>. Inset: The amplication of a.</p>
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<p>(<b>A</b>) Cyclic voltammograms of 0.5 µM TBBPA at the CPE (a), ABPE (b), SB3-16/CPE (c), and SB3-16/ABPE (d) in 0.1 M phosphate buffer (pH = 7.0) after a 240 s accumulation. Scan rate: 100 mV·s<sup>−1</sup>; (<b>B</b>) The current density derived from cyclic voltammograms responses of 0.5 µM TBBPA at the surface of different electrodes.</p>
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<p>(<b>A</b>) The influences of different kinds of surfactants on the oxidation peak current of 0.5 µM TBBPA; (<b>B</b>) The effects of zwitterionic surfactant with different length of hydrophobic chains on the oxidation peak current of 0.5 µM TBBPA; (<b>C</b>) The current density of 0.5 µM TBBPA at the surface of different kind of surfactant modified ABPE; (<b>D</b>) The current density of 0.5 µM TBBPA at the surface of zwitterionic surfactant with different lengths of hydrophobic chain modified ABPE.</p>
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<p>(<b>A</b>) Cyclic voltammograms of 0.5 µM TBBPA at SB3-16/ABPE in 0.1 M phosphate buffer of different pH: 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, and 9.0; (<b>B</b>) The effects of pH on the peak current and potential. Scan rate: 100 mV·s<sup>−1</sup>.</p>
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<p>(<b>A</b>) Cyclic voltammograms of 0.5 µM TBBPA in 0.1 M phosphate buffer (pH = 7.0) on SB3-16/ABPE at dufferent scan rates: 10, 20, 40, 60, 80, 100, 150, 200, and 300 mV·s<sup>−1</sup>; (<b>B</b>) The linear relationship of peak current and scan rate; (<b>C</b>) The plot of peak potential (<span class="html-italic">E</span><sub>p</sub>) and natural logarithm of scan rate (ln <span class="html-italic">ν</span>).</p>
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<p>(<b>A</b>) Cyclic voltammograms of SB3-16/ABPE at various concentration of TBBPA, from a to k: 0.001, 0.005, 0.01, 0.03, 0.05, 0.07, 0.1, 0.3, 0.5, 0.7, and 1 µM in 0.1 M phosphate buffer (pH = 7.0), scan rate 100 mV·s<sup>−1</sup>. Inset: The plot of electrocatalytic peak current versus TBBPA concentration; (<b>B</b>) Amperometic current response of the SB3/ABPE by successive additon of TBBPA with concentration of 0.02, 0.04, 0.06, 0.08, 0.1 0.2, 0.4, 0.6, 0.8, and 1 µM into 0.1 M phosphate buffer (pH = 7.0). Applied potential: 0.7 V. Inset: The plot of electrocatalytic peak current versus TBBPA concentration.</p>
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5967 KiB  
Article
Collection and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-User Scenarios
by Francisco De Arriba-Pérez, Manuel Caeiro-Rodríguez and Juan M. Santos-Gago
Sensors 2016, 16(9), 1538; https://doi.org/10.3390/s16091538 - 21 Sep 2016
Cited by 116 | Viewed by 19989
Abstract
Over recent years, we have witnessed the development of mobile and wearable technologies to collect data from human vital signs and activities. Nowadays, wrist wearables including sensors (e.g., heart rate, accelerometer, pedometer) that provide valuable data are common in market. We are working [...] Read more.
Over recent years, we have witnessed the development of mobile and wearable technologies to collect data from human vital signs and activities. Nowadays, wrist wearables including sensors (e.g., heart rate, accelerometer, pedometer) that provide valuable data are common in market. We are working on the analytic exploitation of this kind of data towards the support of learners and teachers in educational contexts. More precisely, sleep and stress indicators are defined to assist teachers and learners on the regulation of their activities. During this development, we have identified interoperability challenges related to the collection and processing of data from wearable devices. Different vendors adopt specific approaches about the way data can be collected from wearables into third-party systems. This hinders such developments as the one that we are carrying out. This paper contributes to identifying key interoperability issues in this kind of scenario and proposes guidelines to solve them. Taking into account these topics, this work is situated in the context of the standardization activities being carried out in the Internet of Things and Machine to Machine domains. Full article
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<p>Wrist wearable devices’ 2015 market share on the left side, 1Q2016 market share on the right side, both by IDC Research Inc. (Framingham, MA, USA) (Adapted from [<a href="#B30-sensors-16-01538" class="html-bibr">30</a>,<a href="#B31-sensors-16-01538" class="html-bibr">31</a>]).</p>
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<p>2016 smartwatch OS prediction in the wrist wearable sector by IDC Research Inc. Adapted from [<a href="#B32-sensors-16-01538" class="html-bibr">32</a>].</p>
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<p>Sensors available in wearables.</p>
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<p>Systems involved in the smartwatch data collection scenario.</p>
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<p>System representation for the wearable data transfer—indirect access.</p>
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<p>System representation for the warehouse data transfer—direct access.</p>
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<p>System representation for the warehouse data transfer—indirect access.</p>
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<p>System representation for the wearable data transfer—direct access.</p>
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<p>Sleep analysis by Fitbit based on data from the Fitbit Surge.</p>
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<p>Sleep analysis by Microsoft through the Microsoft Band.</p>
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<p>Sleep analysis by Jawbone through the Jawbone Up move.</p>
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<p>Architecture of our analytic engine.</p>
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<p>Sleep segments in Microsoft.</p>
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<p>Sleep segments in Fitbit. We only can detect two states of sleep in Fitbit wearables.</p>
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<p>Sleep segments in Jawbone.</p>
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<p>Analytics location models.</p>
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<p>Adapted representation of the IoT Reference Model by Cisco System, Inc. [<a href="#B66-sensors-16-01538" class="html-bibr">66</a>].</p>
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5355 KiB  
Article
Efficient Data Gathering Methods in Wireless Sensor Networks Using GBTR Matrix Completion
by Donghao Wang, Jiangwen Wan, Zhipeng Nie, Qiang Zhang and Zhijie Fei
Sensors 2016, 16(9), 1532; https://doi.org/10.3390/s16091532 - 21 Sep 2016
Cited by 4 | Viewed by 5986
Abstract
To obtain efficient data gathering methods for wireless sensor networks (WSNs), a novel graph based transform regularized (GBTR) matrix completion algorithm is proposed. The graph based transform sparsity of the sensed data is explored, which is also considered as a penalty term in [...] Read more.
To obtain efficient data gathering methods for wireless sensor networks (WSNs), a novel graph based transform regularized (GBTR) matrix completion algorithm is proposed. The graph based transform sparsity of the sensed data is explored, which is also considered as a penalty term in the matrix completion problem. The proposed GBTR-ADMM algorithm utilizes the alternating direction method of multipliers (ADMM) in an iterative procedure to solve the constrained optimization problem. Since the performance of the ADMM method is sensitive to the number of constraints, the GBTR-A2DM2 algorithm obtained to accelerate the convergence of GBTR-ADMM. GBTR-A2DM2 benefits from merging two constraint conditions into one as well as using a restart rule. The theoretical analysis shows the proposed algorithms obtain satisfactory time complexity. Extensive simulation results verify that our proposed algorithms outperform the state of the art algorithms for data collection problems in WSNs in respect to recovery accuracy, convergence rate, and energy consumption. Full article
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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<p>The real deployment topology of GreenOrbs.</p>
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<p>The random topology of synthetized data with 500 nodes in a 1000 m × 1000 m area.</p>
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<p>The sorted GBT coefficients of the datasets.</p>
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<p>The performance of GBTR-ADMM in respect to different <span class="html-italic">β</span>.</p>
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<p>The effect of the sparsity regularization parameter λ.</p>
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<p>Recovery errors on temperature dataset.</p>
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<p>Recovery errors on humidity dataset.</p>
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<p>Recovery errors in the synthesized dataset.</p>
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<p>Necessary number of iterations for different algorithms.</p>
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<p>Variation of recovery errors in respect to iteration numbers for different algorithms.</p>
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<p>Network lifetime comparison.</p>
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1933 KiB  
Article
Small Moving Vehicle Detection in a Satellite Video of an Urban Area
by Tao Yang, Xiwen Wang, Bowei Yao, Jing Li, Yanning Zhang, Zhannan He and Wencheng Duan
Sensors 2016, 16(9), 1528; https://doi.org/10.3390/s16091528 - 21 Sep 2016
Cited by 82 | Viewed by 9541
Abstract
Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. [...] Read more.
Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately-sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information. In this paper, we look into the problem of moving vehicle detection in satellite imagery. To the best of our knowledge, it is the first time to deal with moving vehicle detection from satellite videos. Our approach consists of two stages: first, through foreground motion segmentation and trajectory accumulation, the scene motion heat map is dynamically built. Following this, a novel saliency based background model which intensifies moving objects is presented to segment the vehicles in the hot regions. Qualitative and quantitative experiments on sequence from a recent Skybox satellite video dataset demonstrates that our approach achieves a high detection rate and low false alarm simultaneously. Full article
(This article belongs to the Section Remote Sensors)
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<p>Skybox satellite video sequences. (<b>a</b>) is a frame of a skybox satellite video sequences. (<b>b</b>) is the enlarged part of the red labelled area in (<b>a</b>). The vehicle covers only several pixels in the image.</p>
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<p>The proposed framework. The partial original satellite image is enlarged with red lines as marked roads. The proposed method contains two steps: building motion heat map and saliency based motion detection. In addition, the main process results of the two steps are displayed.</p>
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<p>The process of trajectory generation and accumulation. Two roads of the original image are segmented. The trajectories are generating and accumulating with time, the final trajectory accumulation image is displayed on the right, and the trajectories have occupied the road area.</p>
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<p>The main process result of building motion heat map. (<b>a</b>): real scenario captured by Google Earth; (<b>b</b>): partially enlarged image of (<b>a</b>); (<b>c</b>): accumulated trajectory image; (<b>d</b>): the partially enlarged image of (<b>b</b>); (<b>e</b>): the motion heat map; (<b>f</b>): the partially enlarged image of (<b>e</b>); (<b>g</b>) segmented result of motion heat map; and (<b>h</b>): the partially enlarged image of (<b>g</b>).</p>
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<p>The original image with road boundary marked in red and three regions in bounding boxes are chosen to explain our method.</p>
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<p>The comparison between grayscale original image and saliency map of the same region. Three regions are selected, and for each regions, two rows are displayed. For each regions, the first rows show the original image sequence of the region and the second rows show the saliency map of the region.</p>
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<p>(<b>a</b>): the original image; (<b>b</b>): the detection result of ViBe (Visual Background extractor); (<b>c</b>): the detection result of our method; (<b>d</b>): partially enlarged image of the original image; (<b>e</b>): the partially enlarged image of (<b>b</b>); and (<b>f</b>): the partially enlarged image of (<b>c</b>).</p>
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<p>Final detection results. Truth Positive (<math display="inline"> <semantics> <mrow> <mi>T</mi> <mi>P</mi> </mrow> </semantics> </math>) is labelled by red triangles. False positive (<math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>P</mi> </mrow> </semantics> </math>) is labelled by yellow ellipses. False Negative (<math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>N</mi> </mrow> </semantics> </math>) is labelled by blue squares.</p>
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<p>Comparision result of original ViBe(Visual Background extractor) detection and ViBe with motion heat map. In every row of the figure, from left to right: the original image with manually marked ground truth in red, the original ViBe detection result, and ViBe detection result with motion heat map.</p>
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<p>The comparison between grayscale image background and saliency based background. We select two regions in the original image. In each row of the figure, from left to right: the original image with manually marked ground truth in red, the saliency based detection result, and the grayscale image background result.</p>
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<p>Partially enlarged details of detection results using different methods.</p>
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2021 KiB  
Article
A Security Mechanism for Cluster-Based WSN against Selective Forwarding
by Hai Zhou, Yuanming Wu, Li Feng and Daolei Liu
Sensors 2016, 16(9), 1537; https://doi.org/10.3390/s16091537 - 20 Sep 2016
Cited by 32 | Viewed by 4904
Abstract
A wireless sensor network (WSN) faces a number of outsider and insider attacks, and it is difficult to detect and defend against insider attacks. In particular, an insider selective-forwarding attack, in which the attackers select some of the received packets to drop, most [...] Read more.
A wireless sensor network (WSN) faces a number of outsider and insider attacks, and it is difficult to detect and defend against insider attacks. In particular, an insider selective-forwarding attack, in which the attackers select some of the received packets to drop, most threatens a WSN. Compared to a distributed WSN, a cluster-based WSN will suffer more losses, even the whole network’s destruction, if the cluster head is attacked. In this paper, a scheme solving the above issues is proposed with three types of nodes, the Cluster Head (CH), the Inspector Node (IN) and Member Nodes (MNs). The IN monitors the CH’s transmission to protect the cluster against a selective-forwarding attack; the CH forwards packets from MNs and other CHs, and randomly checks the IN to ascertain if it works properly; and the MNs send the gathered data packets to the CH and evaluate the behaviors of the CH and IN based on their own reputation mechanism. The novelty of our scheme is that in order to take both the safety and the lifespan of a network into consideration, the composite reputation value (CRV) including forwarding rate, detecting malicious nodes, and surplus energy of the node is utilized to select CH and IN under the new suggested network arrangement, and the use of a node’s surplus energy can balance the energy consumption of a node, thereby prolonging the network lifespan. Theoretical analysis and simulation results indicate that the proposed scheme can detect the malicious node accurately and efficiently, so the false alarm rate is lowered by 25.7% compared with Watchdog and the network lifespan is prolonged by 54.84% compared with LEACH (Low Energy Adaptive Clustering Hierarchy). Full article
(This article belongs to the Section Sensor Networks)
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<p>The basic topological structure of a cluster.</p>
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<p>Local monitoring.</p>
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<p>IN’s inspection of the CH.</p>
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<p>CH’s random check for IN.</p>
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<p>Workflow of local network.</p>
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<p>Collusion attack.</p>
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<p>Clustering.</p>
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<p>Detection of node 6 captured in different time.</p>
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<p>Forward probability of the network with one malicious node.</p>
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<p>Forward probability of the network with two malicious nodes.</p>
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<p>Forward probability of the network with three malicious nodes.</p>
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<p>The number of remaining nodes in the network.</p>
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2360 KiB  
Article
Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity
by Dayan Adionel Guimarães, Lucas Jun Sakai, Antonio Marcos Alberti and Rausley Adriano Amaral De Souza
Sensors 2016, 16(9), 1536; https://doi.org/10.3390/s16091536 - 20 Sep 2016
Cited by 6 | Viewed by 7031
Abstract
In this paper, a simple and flexible method for increasing the lifetime of fixed or mobile wireless sensor networks is proposed. Based on past residual energy information reported by the sensor nodes, the sink node or another central node dynamically optimizes the communication [...] Read more.
In this paper, a simple and flexible method for increasing the lifetime of fixed or mobile wireless sensor networks is proposed. Based on past residual energy information reported by the sensor nodes, the sink node or another central node dynamically optimizes the communication activity levels of the sensor nodes to save energy without sacrificing the data throughput. The activity levels are defined to represent portions of time or time-frequency slots in a frame, during which the sensor nodes are scheduled to communicate with the sink node to report sensory measurements. Besides node mobility, it is considered that sensors’ batteries may be recharged via a wireless power transmission or equivalent energy harvesting scheme, bringing to the optimization problem an even more dynamic character. We report large increased lifetimes over the non-optimized network and comparable or even larger lifetime improvements with respect to an idealized greedy algorithm that uses both the real-time channel state and the residual energy information. Full article
(This article belongs to the Section Sensor Networks)
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<p>Topology adopted for the MWSN. The mobile sensor nodes inside the coverage area are those that can have direct wireless links to the mobile sink node. Others communicate with the sink node by means of multiple hops.</p>
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<p>Time slotted approach for optimizing the activity levels of the mobile sensor nodes.</p>
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<p>Pictorial representation of a net random energy consumption during the <span class="html-italic">k</span>-th block of <math display="inline"> <semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> frames for the <span class="html-italic">n</span>-th sensor node.</p>
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<p>Pictorial representation of the maximum energy consumptions of five sensor nodes during 100 frames, for <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.999</mn> </mrow> </semantics> </math> (<b>a</b>) and <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.98</mn> </mrow> </semantics> </math> (<b>b</b>), for <math display="inline"> <semantics> <mrow> <msub> <mi>B</mi> <mi>min</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>B</mi> <mi>max</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>.</p>
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<p>Time line illustrating the operation of Algorithm 1.</p>
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<p>Residual energies obtained by the proposed optimization method for <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> (<b>a</b>); <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> (<b>b</b>); and <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> (<b>c</b>). The non-optimized network is considered in all graphs for reference.</p>
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<p>Activity levels assigned by the proposed optimization method respective to the situations considered in <a href="#sensors-16-01536-f006" class="html-fig">Figure 6</a>, i.e., <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> (<b>a</b>); <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> (<b>b</b>); and <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> (<b>c</b>).</p>
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<p>Residual energies obtained by the proposed optimization method with <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math>, for <math display="inline"> <semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.98</mn> </mrow> </semantics> </math> (<b>a</b>); <math display="inline"> <semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.98</mn> </mrow> </semantics> </math> (<b>b</b>); <math display="inline"> <semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.98</mn> </mrow> </semantics> </math> (<b>c</b>); and <math display="inline"> <semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.999999</mn> </mrow> </semantics> </math> (<b>d</b>). The non-optimized network is also considered for reference.</p>
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<p>Residual energies obtained by the proposed optimization method for <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> (<b>a</b>) and <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> (<b>b</b>) with recharge and unequal initial energies. The non-optimized network is also considered for reference.</p>
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<p>Residual energies (<b>a</b>) and activity levels (<b>b</b>) from the proposed optimization method for <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math>, with recharge at <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <msub> <mi>t</mi> <mi mathvariant="normal">r</mi> </msub> <mo>=</mo> <mn>184</mn> </mrow> </semantics> </math> and unequal initial energies. The non-optimized network is also considered on the graph (<b>a</b>) for reference.</p>
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<p>Residual energies obtained by the proposed optimization method for <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>B</mi> <mi>min</mi> </msub> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.98</mn> </mrow> </semantics> </math>, with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>B</mi> <mi>max</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics> </math> (<b>a</b>); and <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>B</mi> <mi>max</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> (<b>b</b>). The non-optimized network is also considered for reference.</p>
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<p>Residual energies (<b>a</b>) and sensor node activity states (<b>b</b>) produced by the greedy method of [<a href="#B21-sensors-16-01536" class="html-bibr">21</a>]. The vertical slashes on the graph (<b>b</b>) indicate <span class="html-italic">on</span> states, which are mutually exclusive for each <span class="html-italic">t</span>.</p>
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<p>Average lifetimes achieved with the greedy algorithm of [<a href="#B21-sensors-16-01536" class="html-bibr">21</a>] and with the proposed optimization method for <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math>, under a variable number of sensor nodes, <span class="html-italic">N</span>. The average lifetime of the non-optimized network is also shown for reference.</p>
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6186 KiB  
Article
A Novel Passive Wireless Sensor for Concrete Humidity Monitoring
by Shuangxi Zhou, Fangming Deng, Lehua Yu, Bing Li, Xiang Wu and Baiqiang Yin
Sensors 2016, 16(9), 1535; https://doi.org/10.3390/s16091535 - 20 Sep 2016
Cited by 35 | Viewed by 9915
Abstract
This paper presents a passive wireless humidity sensor for concrete monitoring. After discussing the transmission of electromagnetic wave in concrete, a novel architecture of wireless humidity sensor, based on Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) technology, is proposed for low-power application. The [...] Read more.
This paper presents a passive wireless humidity sensor for concrete monitoring. After discussing the transmission of electromagnetic wave in concrete, a novel architecture of wireless humidity sensor, based on Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) technology, is proposed for low-power application. The humidity sensor utilizes the top metal layer to form the interdigitated electrodes, which were then filled with polyimide as the humidity sensing layer. The sensor interface converts the humidity capacitance into a digital signal in the frequency domain. A two-stage rectifier adopts a dynamic bias-voltage generator to boost the effective gate-source voltage of the switches in differential-drive architecture. The clock generator employs a novel structure to reduce the internal voltage swing. The measurement results show that our proposed wireless humidity can achieve a high linearity with a normalized sensitivity of 0.55% %RH at 20 °C. Despite the high losses of concrete, the proposed wireless humidity sensor achieves reliable communication performances in passive mode. The maximum operating distance is 0.52 m when the proposed wireless sensor is embedded into the concrete at the depth of 8 cm. The measured results are highly consistent with the results measured by traditional methods. Full article
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<p>Total loss of electromagnetic wave penetrating concrete.</p>
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<p>Computed S-parameters of two antennas coupling in free space and wet concrete: (<b>a</b>) return loss of dipoles; (<b>b</b>) return loss of patches.</p>
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<p>Architecture of the proposed wireless humidity sensor.</p>
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<p>Proposed humidity sensor structure (<b>a</b>) humidity sensor structure; (<b>b</b>) top view of the humidity sensor</p>
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<p>Proposed sensor interface: (<b>a</b>) architecture of sensor interface; (<b>b</b>) simulation results of <span class="html-italic">f<sub>osc</sub></span> vs. temperature on different process corners.</p>
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<p>Schematic of the proposed two-stage rectifier.</p>
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<p>Second stage bias and gate voltages of the proposed gate boosting scheme.</p>
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<p>Proposed voltage regulator.</p>
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<p>Simulated supply voltage variation of the proposed rectifier.</p>
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<p>Schematic of the proposed clock generator.</p>
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<p>Proposed patch antenna: (<b>a</b>) antenna design; (<b>b</b>) return-loss plot.</p>
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<p>Top view of the proposed package box.</p>
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<p>(<b>a</b>) Photo of the proposed tag chip; (<b>b</b>) Concrete measuring environment.</p>
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<p>Wireless measurement environment.</p>
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<p>(<b>a</b>) Measured power conversion efficiency of the rectifier; (<b>b</b>) output waveforms of the proposed ring oscillator at different nodes.</p>
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<p>Measured humidity performances of the wireless sensor: (<b>a</b>) digital outputs at 20 °C, 35 °C, and 60 °C; (<b>b</b>) digital outputs of five test chips at 20 °C, 35 °C, and 60 °C; (<b>c</b>) hysteresis performances at 20 °C.</p>
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<p>(<b>a</b>) Experimental scheme; (<b>b</b>) Test site.</p>
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<p>(<b>a</b>) Influence of incidence angle on power transmission; (<b>b</b>) Influence of concrete humidity on power transmission.</p>
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<p>Performances comparison measured by different sensors.</p>
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4050 KiB  
Article
A Laser Line Auto-Scanning System for Underwater 3D Reconstruction
by Shukai Chi, Zexiao Xie and Wenzhu Chen
Sensors 2016, 16(9), 1534; https://doi.org/10.3390/s16091534 - 20 Sep 2016
Cited by 56 | Viewed by 9724
Abstract
In this study, a laser line auto-scanning system was designed to perform underwater close-range 3D reconstructions with high accuracy and resolution. The system changes the laser plane direction with a galvanometer to perform automatic scanning and obtain continuous laser strips for underwater 3D [...] Read more.
In this study, a laser line auto-scanning system was designed to perform underwater close-range 3D reconstructions with high accuracy and resolution. The system changes the laser plane direction with a galvanometer to perform automatic scanning and obtain continuous laser strips for underwater 3D reconstruction. The system parameters were calibrated with the homography constraints between the target plane and image plane. A cost function was defined to optimize the galvanometer’s rotating axis equation. Compensation was carried out for the refraction of the incident and emitted light at the interface. The accuracy and the spatial measurement capability of the system were tested and analyzed with standard balls under laboratory underwater conditions, and the 3D surface reconstruction for a sealing cover of an underwater instrument was proved to be satisfactory. Full article
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<p>Laser line auto-scanning system.</p>
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<p>The schematic for system coordinate system setup.</p>
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<p>The schematic for the target location planning when performing the system calibration.</p>
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<p>World coordinate system when performing camera calibration.</p>
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<p>A planar view of the working system and the refraction caused by the air-glass-water interface of the incident and reflected light.</p>
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<p>The balls that are already measured by a coordinate measurement machine (CMM) for the tank experiments. (<b>a</b>) The standard ball whose precise radius and spherical error are known for the accuracy test. (<b>b</b>) The three balls fixed on the one base board for the spatial error test. The radii of the three balls and the distances between any two balls are accurately known.</p>
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<p>The standard ball measurement experimental setup in the water tank.</p>
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<p>The errors between the fitted radii and the standard radius.</p>
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<p>The maximum distance from the points outside and inside the sphere to the fitted surface.</p>
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<p>The error distribution between the fitted sphere and the scatter points measured at Position 6.</p>
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<p>Measurement for a sealing cover of an underwater instrument. (<b>a</b>) Photograph; (<b>b</b>) Surface point cloud; (<b>c</b>) Triangular mesh representation.</p>
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4009 KiB  
Article
Fabrication of Micro-Needle Electrodes for Bio-Signal Recording by a Magnetization-Induced Self-Assembly Method
by Keyun Chen, Lei Ren, Zhipeng Chen, Chengfeng Pan, Wei Zhou and Lelun Jiang
Sensors 2016, 16(9), 1533; https://doi.org/10.3390/s16091533 - 20 Sep 2016
Cited by 58 | Viewed by 11635
Abstract
Micro-needle electrodes (MEs) have attracted more and more attention for monitoring physiological electrical signals, including electrode-skin interface impedance (EII), electromyography (EMG) and electrocardiography (ECG) recording. A magnetization-induced self-assembling method (MSM) was developed to fabricate a microneedle array (MA). A MA coated with Ti/Au [...] Read more.
Micro-needle electrodes (MEs) have attracted more and more attention for monitoring physiological electrical signals, including electrode-skin interface impedance (EII), electromyography (EMG) and electrocardiography (ECG) recording. A magnetization-induced self-assembling method (MSM) was developed to fabricate a microneedle array (MA). A MA coated with Ti/Au film was assembled as a ME. The fracture and insertion properties of ME were tested by experiments. The bio-signal recording performance of the ME was measured and compared with a typical commercial wet electrode (Ag/AgCl electrode). The results show that the MA self-assembled from the magnetic droplet array under the sum of gravitational surface tension and magnetic potential energies. The ME had good toughness and could easily pierce rabbit skin without being broken or buckling. When the compression force applied on the ME was larger than 2 N, ME could stably record EII, which was a lower value than that measured by Ag/AgCl electrodes. EMG signals collected by ME varied along with the contraction of biceps brachii muscle. ME could record static ECG signals with a larger amplitude and dynamic ECG signals with more distinguishable features in comparison with a Ag/AgCl electrode, therefore, ME is an alternative electrode for bio-signal monitoring in some specific situations. Full article
(This article belongs to the Section Physical Sensors)
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<p>Interface between skin and: (<b>a</b>) wet electrode; and (<b>b</b>) ME.</p>
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<p>ME fabrication process: (<b>a</b>) The magnetization-induced MA equipment; (<b>b</b>) MA formation by MSM; (<b>c</b>) Sputtering coating Ti/Au films on the surface of MA; and (<b>d</b>) rendered image of ME.</p>
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<p>Schematic illustration of (<b>a</b>) fracture; and (<b>b</b>) insertion test ex vivo.</p>
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<p>Setup designed for EII recording during the insertion process.</p>
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<p>EMG recorded by: (<b>a</b>) Ag/AgCl electrodes; and (<b>b</b>) ME; (<b>c</b>) Recording positions.</p>
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<p>(<b>a</b>) ME and Ag/AgCl electrode; (<b>b</b>) SEM image of MA; (<b>c</b>) micro-needle; (<b>d</b>) micro-needle tip; (<b>e</b>) micro-needle bottom; and (<b>f</b>) micro-needle middle.</p>
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<p>(<b>a</b>) Resistance force during the fracture test; and (<b>b</b>) SEM images of bent MEs after the fracture test.</p>
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<p>(<b>a</b>) Insertion force vs. displacement curve; (<b>b</b>) the penetration point; and (<b>c</b>) fluorescence image of punctured rabbit skin.</p>
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<p>Insertion force and EII test. (<b>a</b>) EII during the insertion process; and (<b>b</b>) EII under different input voltage frequency.</p>
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<p>ECG signals recorded by Ag/AgCl electrodes and ME in the (<b>a</b>) static state; and (<b>b</b>) dynamic state.</p>
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<p>Frequency spectrum of ECG signals recorded by Ag/AgCl electrode and ME: (<b>a</b>,<b>c</b>) at the static state; and (<b>b</b>,<b>d</b>) at the dynamic state.</p>
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5980 KiB  
Review
Design of Highly Selective Gas Sensors via Physicochemical Modification of Oxide Nanowires: Overview
by Hyung-Sik Woo, Chan Woong Na and Jong-Heun Lee
Sensors 2016, 16(9), 1531; https://doi.org/10.3390/s16091531 - 20 Sep 2016
Cited by 66 | Viewed by 13301
Abstract
Strategies for the enhancement of gas sensing properties, and specifically the improvement of gas selectivity of metal oxide semiconductor nanowire (NW) networks grown by chemical vapor deposition and thermal evaporation, are reviewed. Highly crystalline NWs grown by vapor-phase routes have various advantages, and [...] Read more.
Strategies for the enhancement of gas sensing properties, and specifically the improvement of gas selectivity of metal oxide semiconductor nanowire (NW) networks grown by chemical vapor deposition and thermal evaporation, are reviewed. Highly crystalline NWs grown by vapor-phase routes have various advantages, and thus have been applied in the field of gas sensors over the years. In particular, n-type NWs such as SnO2, ZnO, and In2O3 are widely studied because of their simple synthetic preparation and high gas response. However, due to their usually high responses to C2H5OH and NO2, the selective detection of other harmful and toxic gases using oxide NWs remains a challenging issue. Various strategies—such as doping/loading of noble metals, decorating/doping of catalytic metal oxides, and the formation of core–shell structures—have been explored to enhance gas selectivity and sensitivity, and are discussed herein. Additional methods such as the transformation of n-type into p-type NWs and the formation of catalyst-doped hierarchical structures by branch growth have also proven to be promising for the enhancement of gas selectivity. Accordingly, the physicochemical modification of oxide NWs via various methods provides new strategies to achieve the selective detection of a specific gas, and after further investigations, this approach could pave a new way in the field of NW-based semiconductor-type gas sensors. Full article
(This article belongs to the Special Issue Gas Nanosensors)
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<p>Schematic illustration of the vapor-liquid-solid mechanism.</p>
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<p>(<b>a</b>) Schematic diagram of the electron depletion layer in <span class="html-italic">n</span>-type oxide semiconductor nanowires decorated with <span class="html-italic">p</span>-type oxide semiconductor nanoclusters; TEM images of (<b>b</b>,<b>c</b>) Cr<sub>2</sub>O<sub>3</sub>-decorated ZnO nanowires; (<b>d</b>) Cr<sub>2</sub>O<sub>3</sub>-decorated SnO<sub>2</sub> nanowires; (<b>e</b>,<b>f</b>) Co<sub>3</sub>O<sub>4</sub>-decorated ZnO nanowires; (<b>g</b>) NiO-decorated ZnO nanowires; and (<b>h</b>) Mn<sub>3</sub>O<sub>4</sub>-decorated ZnO nanowires. Reproduced from [<a href="#B80-sensors-16-01531" class="html-bibr">80</a>,<a href="#B81-sensors-16-01531" class="html-bibr">81</a>,<a href="#B82-sensors-16-01531" class="html-bibr">82</a>,<a href="#B83-sensors-16-01531" class="html-bibr">83</a>,<a href="#B84-sensors-16-01531" class="html-bibr">84</a>] with permission; (<b>a</b>,<b>e</b>–<b>g</b>) [<a href="#B82-sensors-16-01531" class="html-bibr">82</a>,<a href="#B83-sensors-16-01531" class="html-bibr">83</a>] Copyright (2011,2012) The Royal Society of Chemistry; (<b>b</b>,<b>c</b>) [<a href="#B80-sensors-16-01531" class="html-bibr">80</a>] Copyright (2012) IOP Publishing Ltd.; (<b>d</b>) [<a href="#B81-sensors-16-01531" class="html-bibr">81</a>] Copyright (2014) Elsevier; (<b>h</b>) [<a href="#B84-sensors-16-01531" class="html-bibr">84</a>] Copyright (2012) American Chemical Society.</p>
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<p>Dynamic sensing transients of (<b>a</b>) ZnO NWs; (<b>b</b>) Cr<sub>2</sub>O<sub>3</sub>-decorated ZnO NWs; and (<b>c</b>) ZnO-Cr<sub>2</sub>O<sub>3</sub> core-shell nanocalbes (NCs) to 5 ppm trimethylamine (TMA) at 400 °C. Dynamic sensing transients of (<b>d</b>) ZnO NWs; (<b>e</b>) Mn<sub>3</sub>O<sub>4</sub>-decorated ZnO NWs; and (<b>f</b>) ZnO-ZnMn<sub>2</sub>O<sub>4</sub> core–shell NCs to 100 ppm C<sub>2</sub>H<sub>5</sub>OH at 400 °C. Adapted from [<a href="#B80-sensors-16-01531" class="html-bibr">80</a>,<a href="#B84-sensors-16-01531" class="html-bibr">84</a>] with permission; (<b>a</b>–<b>c</b>) [<a href="#B80-sensors-16-01531" class="html-bibr">80</a>] Copyright (2012) IOP Publishing Ltd.; (<b>d</b>–<b>f</b>) [<a href="#B84-sensors-16-01531" class="html-bibr">84</a>] Copyright (2012) American Chemical Society.</p>
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<p>Gas selectivity of (<b>a</b>) pristine SnO<sub>2</sub> NW network sensor and (<b>b</b>) CuO-decorated SnO<sub>2</sub> NW network sensors at 300 °C; gas selectivity of (<b>c</b>) pristine ZnO NW network sensor and (<b>d</b>) Cr<sub>2</sub>O<sub>3</sub>-decorated ZnO NW network sensor at 400 °C. Adapted from [<a href="#B80-sensors-16-01531" class="html-bibr">80</a>,<a href="#B91-sensors-16-01531" class="html-bibr">91</a>] with permission; (<b>a</b>,<b>b</b>) [<a href="#B91-sensors-16-01531" class="html-bibr">91</a>] Copyright (2009) Elsevier; (<b>c</b>,<b>d</b>) [<a href="#B80-sensors-16-01531" class="html-bibr">80</a>] Copyright (2012) IOP Publishing Ltd.</p>
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<p>(<b>a</b>–<b>e</b>) Schematic illustration of the synthesis process of Mo-doped ZnO NW network gas sensors. Sensing transients of pure and Mo-doped ZnO NW network gas sensors to 5 ppm H<sub>2</sub>S at 300, 325, and 350 °C: (<b>f-1</b>) ZnO NW sensor, 300 °C; (<b>f-2</b>) ZnO NW sensor, 325 °C; (<b>f-3</b>) ZnO NW sensor, 350 °C; (<b>g-1</b>) Mo-doped ZnO NW sensor, 300 °C; (<b>g-2</b>) Mo-doped ZnO NW sensor, 325 °C; and (<b>g-3</b>) Mo-doped ZnO NW sensor, 350 °C; (<b>f-4</b>,<b>g-4</b>) Recovery (%) = (R<sub>air-recovery</sub> − R<sub>gas-H2S</sub>)/(R<sub>air-fresh</sub> − R<sub>gas-H2S</sub>) × 100 (%) of pure and Mo-doped ZnO NW sensors at 300–400 °C (where, R<sub>air-fresh</sub>: sensor resistance in air before exposure to H<sub>2</sub>S, R<sub>gas-H2S</sub>: sensor resistance in 5 ppm H<sub>2</sub>S, and R<sub>air-recovery</sub>: sensor resistance in air after 10 min exposure to air). Reproduced from [<a href="#B88-sensors-16-01531" class="html-bibr">88</a>] with permission. Copyright (2014) The Royal Society of Chemistry.</p>
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<p>(<b>a</b>) Relationship between ZnO shell thickness and number of atomic layer deposition (ALD) cycles; (<b>b</b>) sensor resistance in air and (<b>c</b>) gas responses of SnO<sub>2</sub>-ZnO core-shell NWs as a function of ZnO shell thickness. Reprinted from [<a href="#B104-sensors-16-01531" class="html-bibr">104</a>] with permission. Copyright (2014) American Chemical Society.</p>
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<p>A schematic illustration of the transformation from ZnO NW to CoO NW and Co<sub>3</sub>O<sub>4</sub> NW. Adapted from [<a href="#B105-sensors-16-01531" class="html-bibr">105</a>] with permission. Copyright (2012) The Royal Society of Chemistry.</p>
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<p>Morphologies and crystal structures of ZnO-ZnMn<sub>2</sub>O<sub>4</sub> NCs: (<b>a</b>,<b>b</b>) TEM images of ZnO-ZnMn<sub>2</sub>O<sub>4</sub> NCs grown on Si substrates; (<b>c</b>) Lattice-resolved image of ZnO-ZnMn<sub>2</sub>O<sub>4</sub> NCs; (<b>d</b>) Energy dispersive X-ray spectroscopy (EDS) elemental mapping of Zn, Mn, and O. Reprinted from [<a href="#B84-sensors-16-01531" class="html-bibr">84</a>] with permission. Copyright (2012) American Chemical Society.</p>
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<p>TEM image and selected area electron diffraction (SAED) patterns for the Mn<sub>3</sub>O<sub>4</sub> NW. Reprinted from [<a href="#B84-sensors-16-01531" class="html-bibr">84</a>] with permission. Copyright (2012) American Chemical Society.</p>
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<p>(<b>a</b>) Growth of ZnO NWs on alumina substrate with Au electrodes; (<b>b</b>) transformation of ZnO NWs into CoO NWs; (<b>c</b>) growth of Co-doped branched ZnO NWs from CoO NWs; (<b>d</b>) transformation of ZnO NWs into NiO NWs; (<b>e</b>) growth of Ni-doped ZnO NWs from NiO NWs. Reproduced from [<a href="#B131-sensors-16-01531" class="html-bibr">131</a>,<a href="#B137-sensors-16-01531" class="html-bibr">137</a>] with permission; (<b>a</b>–<b>c</b>) [<a href="#B137-sensors-16-01531" class="html-bibr">137</a>] Copyright (2014) American Chemical Society; (<b>d</b>,<b>e</b>) [<a href="#B131-sensors-16-01531" class="html-bibr">131</a>] Copyright (2015) Elsevier.</p>
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<p>Gas response of (<b>a</b>) pristine ZnO; (<b>b</b>) Co-doped branched ZnO NWs; and (<b>c</b>) Ni-doped branched ZnO NWs to 5 ppm <span class="html-italic">p</span>-xylene, toluene, C<sub>2</sub>H<sub>5</sub>OH, TMA, HCHO, NH<sub>3</sub>, CO, benzene, and H<sub>2</sub> at 350–450 °C. Reproduced from [<a href="#B131-sensors-16-01531" class="html-bibr">131</a>,<a href="#B137-sensors-16-01531" class="html-bibr">137</a>] with permission; (<b>a</b>,<b>b</b>) [<a href="#B137-sensors-16-01531" class="html-bibr">137</a>] Copyright (2014) American Chemical Society; (<b>c</b>) [<a href="#B131-sensors-16-01531" class="html-bibr">131</a>] Copyright (2015) Elsevier.</p>
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<p>Various oxide nanowires for highly selective gas detection.</p>
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1314 KiB  
Article
Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
by Xi Liu, Hua Qu, Jihong Zhao, Pengcheng Yue and Meng Wang
Sensors 2016, 16(9), 1530; https://doi.org/10.3390/s16091530 - 20 Sep 2016
Cited by 99 | Viewed by 7990
Abstract
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, [...] Read more.
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm. Full article
(This article belongs to the Section Physical Sensors)
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<p>Illustration of example 2.</p>
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<p>Measurement coordinate system.</p>
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<p>Relative motion of the deputy spacecraft in the Hill frame.</p>
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<p><math display="inline"> <semantics> <msub> <mi>MSD</mi> <mi mathvariant="normal">p</mi> </msub> </semantics> </math> with different filters in Gaussian noises.</p>
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<p><math display="inline"> <semantics> <msub> <mi>MSD</mi> <mi mathvariant="normal">v</mi> </msub> </semantics> </math> with different filters in Gaussian noises.</p>
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<p><math display="inline"> <semantics> <msub> <mi>MSD</mi> <mi mathvariant="normal">p</mi> </msub> </semantics> </math> with different filters in non-Gaussian noises.</p>
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<p><math display="inline"> <semantics> <msub> <mi>MSD</mi> <mi mathvariant="normal">v</mi> </msub> </semantics> </math> with different filters in non-Gaussian noises.</p>
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2290 KiB  
Article
Research on the Lift-off Effect of Receiving Longitudinal Mode Guided Waves in Pipes Based on the Villari Effect
by Jiang Xu, Yong Sun and Jinhai Zhou
Sensors 2016, 16(9), 1529; https://doi.org/10.3390/s16091529 - 20 Sep 2016
Cited by 11 | Viewed by 6549
Abstract
The magnetostrictive guided wave technology as a non-contact measurement can generate and receive guided waves with a large lift-off distance up to tens of millimeters. However, the lift-off distance of the receiving coil would affect the coupling efficiency from the elastic energy to [...] Read more.
The magnetostrictive guided wave technology as a non-contact measurement can generate and receive guided waves with a large lift-off distance up to tens of millimeters. However, the lift-off distance of the receiving coil would affect the coupling efficiency from the elastic energy to the electromagnetic energy. In the existing magnetomechanical models, the change of the magnetic field in the air gap was ignored since the permeability of the rod is much greater than that of air. The lift-off distance of the receiving coil will not affect the receiving signals based on these models. However, the experimental phenomenon is in contradiction with these models. To solve the contradiction, the lift-off effect of receiving the longitudinal mode guided waves in pipes is investigated based on the Villari effect. A finite element model of receiving longitudinal guided waves in pipes is obtained based on the Villari effect, which takes into account the magnetic field in the pipe wall and the air zone at the same time. The relation between the amplitude of the induced signals and the radius (lift-off distance) of the receiving coil is obtained, which is verified by experiment. The coupling efficiency of the receiver is a monotonic decline with the lift-off distance increasing. The decay rate of the low frequency wave is slower than the high frequency wave. Additionally, the results show that the rate of change of the magnetic flux in the air zone and in the pipe wall is the same order of magnitude, but opposite. However, the experimental results show that the error of the model in the large lift-off distance is obvious due to the diffusion of the magnetic field in the air, especially for the high frequency guided waves. Full article
(This article belongs to the Special Issue Non-Contact Sensing)
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<p>The magnetization curve and the magnetostriction curve of the steel pipe. (<b>a</b>) The magnetization curve; (<b>b</b>) the magnetostriction curve.</p>
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<p>Schematic diagram of the FE model for generating and receiving guided waves based on the magnetostrictive effect.</p>
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<p>Layout of the model at the receiving position.</p>
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<p>Group velocity dispersion curve of the steel pipe with a 38 mm outer diameter and a 5 mm wall thickness.</p>
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<p>The FE calculation results of the changing rate of magnetic flux density of four radial points at 20 kHz.</p>
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<p>The FE calculation results of the changing rate of magnetic flux density in the radial direction at 20 kHz: (<b>a</b>) the internal air zone; (<b>b</b>) the pipe wall; (<b>c</b>) the external air zone; (<b>d</b>) the 0–80 mm zone.</p>
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<p>The FE calculation results of the changing rate of magnetic flux density in the radial direction at 20 kHz: (<b>a</b>) the internal air zone; (<b>b</b>) the pipe wall; (<b>c</b>) the external air zone; (<b>d</b>) the 0–80 mm zone.</p>
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<p>The FE calculation results of the changing rate of magnetic flux density in the radial direction at 80 kHz: (<b>a</b>) the internal air zone; (<b>b</b>) the pipe wall; (<b>c</b>) the external air zone; (<b>d</b>) the 0–80 mm zone.</p>
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<p>The FE calculation results of the changing rate of magnetic flux density in the radial direction at 80 kHz: (<b>a</b>) the internal air zone; (<b>b</b>) the pipe wall; (<b>c</b>) the external air zone; (<b>d</b>) the 0–80 mm zone.</p>
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<p>The signals induced by the internal coil with a 26.2 mm diameter and the external coil with a 38.5 mm diameter at 20 kHz.</p>
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<p>The normalized experimental and FE data of the peak-peak value of the first passing signal: (<b>a</b>) 20 kHz; (<b>b</b>) 80 kHz.</p>
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420 KiB  
Article
Combating QR-Code-Based Compromised Accounts in Mobile Social Networks
by Dong Guo, Jian Cao, Xiaoqi Wang, Qiang Fu and Qiang Li
Sensors 2016, 16(9), 1522; https://doi.org/10.3390/s16091522 - 20 Sep 2016
Cited by 5 | Viewed by 6527
Abstract
Cyber Physical Social Sensing makes mobile social networks (MSNs) popular with users. However, such attacks are rampant as malicious URLs are spread covertly through quick response (QR) codes to control compromised accounts in MSNs to propagate malicious messages. Currently, there are generally two [...] Read more.
Cyber Physical Social Sensing makes mobile social networks (MSNs) popular with users. However, such attacks are rampant as malicious URLs are spread covertly through quick response (QR) codes to control compromised accounts in MSNs to propagate malicious messages. Currently, there are generally two types of methods to identify compromised accounts in MSNs: one type is to analyze the potential threats on wireless access points and the potential threats on handheld devices’ operation systems so as to stop compromised accounts from spreading malicious messages; the other type is to apply the method of detecting compromised accounts in online social networks to MSNs. The above types of methods above focus neither on the problems of MSNs themselves nor on the interaction of sensors’ messages, which leads to the restrictiveness of platforms and the simplification of methods. In order to stop the spreading of compromised accounts in MSNs effectively, the attacks have to be traced to their sources first. Through sensors, users exchange information in MSNs and acquire information by scanning QR codes. Therefore, analyzing the traces of sensor-related information helps to identify the compromised accounts in MSNs. This paper analyzes the diversity of information sending modes of compromised accounts and normal accounts, analyzes the regularity of GPS (Global Positioning System)-based location information, and introduces the concepts of entropy and conditional entropy so as to construct an entropy-based model based on machine learning strategies. To achieve the goal, about 500,000 accounts of Sina Weibo and about 100 million corresponding messages are collected. Through the validation, the accuracy rate of the model is proved to be as high as 87.6%, and the false positive rate is only 3.7%. Meanwhile, the comparative experiments of the feature sets prove that sensor-based location information can be applied to detect the compromised accounts in MSNs. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>System overview.</p>
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<p>Activities of message data.</p>
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<p>Sample of items in sending messages.</p>
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<p>Entropy of users’ behavior.</p>
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<p>Regularity of users’ behavior.</p>
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<p>Conditional entropy of location-based features.</p>
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<p>Entropy of location-based features.</p>
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1909 KiB  
Article
Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder
by Jun Luo, Zhiqian Wang, Chengwu Shen, Arjan Kuijper, Zhuoman Wen and Shaojin Liu
Sensors 2016, 16(9), 1513; https://doi.org/10.3390/s16091513 - 20 Sep 2016
Cited by 7 | Viewed by 7919
Abstract
Even when the Global Positioning System (GPS) signal is blocked, a rate gyroscope (gyro) north finder is capable of providing the required azimuth reference information to a certain extent. In order to measure the azimuth between the observer and the north direction very [...] Read more.
Even when the Global Positioning System (GPS) signal is blocked, a rate gyroscope (gyro) north finder is capable of providing the required azimuth reference information to a certain extent. In order to measure the azimuth between the observer and the north direction very accurately, we propose a multi-position non-continuous rotation gyro north finding scheme. Our new generalized mathematical model analyzes the elements that affect the azimuth measurement precision and can thus provide high precision azimuth reference information. Based on the gyro’s principle of detecting a projection of the earth rotation rate on its sensitive axis and the proposed north finding scheme, we are able to deduct an accurate mathematical model of the gyro outputs against azimuth with the gyro and shaft misalignments. Combining the gyro outputs model and the theory of propagation of uncertainty, some approaches to optimize north finding are provided, including reducing the gyro bias error, constraining the gyro random error, increasing the number of rotation points, improving rotation angle measurement precision, decreasing the gyro and the shaft misalignment angles. According them, a north finder setup is built and the azimuth uncertainty of 18” is obtained. This paper provides systematic theory for analyzing the details of the gyro north finder scheme from simulation to implementation. The proposed theory can guide both applied researchers in academia and advanced practitioners in industry for designing high precision robust north finder based on different types of rate gyroscopes. Full article
(This article belongs to the Section Physical Sensors)
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<p>Layout of the north finder. (<b>a</b>) Inside part. (<b>b</b>) Outside part.</p>
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<p>The gyro north finder multi-position non-continuous rotation scheme.</p>
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<p>The east, north, up geographical coordinate system.</p>
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<p>The gyro axis’s trajectory with the gyro misalignment or the shaft misalignment when the shaft is rotated counterclockwise around itself for 360<math display="inline"> <semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics> </math> (the sphere is not the earth, but is used to facilitate understanding of the misalignments).</p>
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<p>The gyro axis’s trajectory with the shaft misalignment and the gyro misalignment when the shaft is rotated counterclockwise around the shaft for 360<math display="inline"> <semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics> </math> (the sphere is not the earth, but used to facilitate understanding of the misalignments).</p>
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<p>The relationship of the azimuth uncertaintyagainst the gyro random drift and the measuring points. (<b>a</b>) The azimuth uncertainty against random drift at various points <span class="html-italic">n</span>; (<b>b</b>) The azimuth uncertainty against random drift at various values of <math display="inline"> <semantics> <msub> <mi>σ</mi> <mi>ω</mi> </msub> </semantics> </math>.</p>
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<p>Azimuth uncertainty against shaft misalignment. (<b>a</b>) Azimuth uncertainty against shaft misalignment when <span class="html-italic">ε</span>∈ [<math display="inline"> <semantics> <mrow> <msup> <mn>0.001</mn> <mo>∘</mo> </msup> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msup> <mn>0.1</mn> <mo>∘</mo> </msup> </mrow> </semantics> </math>] and <span class="html-italic">ψ</span> = <math display="inline"> <semantics> <msup> <mn>45</mn> <mo>∘</mo> </msup> </semantics> </math>; (<b>b</b>) Azimuth uncertainty against shaft misalignment when <math display="inline"> <semantics> <mrow> <mi>ψ</mi> <mo>∈</mo> <mo>[</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> <mo>,</mo> <msup> <mn>360</mn> <mo>∘</mo> </msup> <mo>]</mo> </mrow> </semantics> </math> and <span class="html-italic">ε</span> = <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <msup> <mn>01</mn> <mo>∘</mo> </msup> </mrow> </semantics> </math>.</p>
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<p>Gyro north finder setup.</p>
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<p>Comparison of the gyro outputs when the shaft is misaligned and aligned.</p>
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<p>Gyro outputs residual. (<b>a</b>) aligned state. (<b>b</b>) misaligned state.</p>
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<p>Gyro outputs fitting results when the number of measurement points <span class="html-italic">n</span> = 72. (<b>a</b>) Fitting curve when <span class="html-italic">n</span> = 72; (<b>b</b>) Residual of the corresponding fitting curve.</p>
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<p>Gyro outputs fitting results when the number of measurement points <span class="html-italic">n</span> = 90. (<b>a</b>) Fitting curve when <span class="html-italic">n</span> = 90; (<b>b</b>) Residual of the corresponding fitting curve.</p>
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<p>Gyro outputs fitting results when the number of measurement points <span class="html-italic">n</span> = 120. (<b>a</b>) Fitting curve when <span class="html-italic">n</span> = 120; (<b>b</b>) Residual of the corresponding fitting curve.</p>
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<p>Gyro outputs fitting results when the number of measurement points <span class="html-italic">n</span> = 180. (<b>a</b>) Fitting curve when <span class="html-italic">n</span> = 180; (<b>b</b>) Residual of the corresponding fitting curve.</p>
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1501 KiB  
Article
Spatial Ecology of Estuarine Crocodile (Crocodylus porosus) Nesting in a Fragmented Landscape
by Luke J. Evans, T. Hefin Jones, Keeyen Pang, Silvester Saimin and Benoit Goossens
Sensors 2016, 16(9), 1527; https://doi.org/10.3390/s16091527 - 19 Sep 2016
Cited by 30 | Viewed by 9455
Abstract
The role that oil palm plays in the Lower Kinabatangan region of Eastern Sabah is of considerable scientific and conservation interest, providing a model habitat for many tropical regions as they become increasingly fragmented. Crocodilians, as apex predators, widely distributed throughout the tropics, [...] Read more.
The role that oil palm plays in the Lower Kinabatangan region of Eastern Sabah is of considerable scientific and conservation interest, providing a model habitat for many tropical regions as they become increasingly fragmented. Crocodilians, as apex predators, widely distributed throughout the tropics, are ideal indicator species for ecosystem health. Drones (or unmanned aerial vehicles (UAVs)) were used to identify crocodile nests in a fragmented landscape. Flights were targeted through the use of fuzzy overlay models and nests located primarily in areas indicated as suitable habitat. Nests displayed a number of similarities in terms of habitat characteristics allowing for refined modelling of survey locations. As well as being more cost-effective compared to traditional methods of nesting survey, the use of drones also enabled a larger survey area to be completed albeit with a limited number of flights. The study provides a methodology for targeted nest surveying, as well as a low-cost repeatable flight methodology. This approach has potential for widespread applicability across a range of species and for a variety of study designs. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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<p>Nesting suitability model for the LKWS. Defined using a “fuzzy overlay” function in ArcGIS. Areas of suitability are defined by the presence of a coloured pixel with increasing suitability defined on a red (low) to green (high) scale. Suitable nesting locations are largely confined to major waterways.</p>
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<p>(<b>a</b>) Potential nest sites in relation to habitat suitability model; the majority of nests sites fell inside of, or close to, identified suitable areas within the study site. Suitability defined as areas of coloured pixels as in <a href="#sensors-16-01527-f001" class="html-fig">Figure 1</a>, with potential nest sites overlaid as blue dots; (<b>b</b>) Locations of confirmed nest sites showing close proximity to water, as well as, on three occasions, close proximity to oil palm plantations.</p>
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<p>Plotting predictions from binomial GLMM. Model provides a binomial predictive distribution, indicating that nesting is less likely further away from permanent water sources. Solid lines denote predicted probability; with dashed lines showing the error associated with the probability levels. Data included both confirmed nest sites as well as those that were “potential” and later discounted nest sites. Despite trajectory of confidence intervals, prediction could not be less than zero.</p>
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2274 KiB  
Article
Potential Seasonal Terrestrial Water Storage Monitoring from GPS Vertical Displacements: A Case Study in the Lower Three-Rivers Headwater Region, China
by Bao Zhang, Yibin Yao, Hok Sum Fok, Yufeng Hu and Qiang Chen
Sensors 2016, 16(9), 1526; https://doi.org/10.3390/s16091526 - 19 Sep 2016
Cited by 22 | Viewed by 6985
Abstract
This study uses the observed vertical displacements of Global Positioning System (GPS) time series obtained from the Crustal Movement Observation Network of China (CMONOC) with careful pre- and post-processing to estimate the seasonal crustal deformation in response to the hydrological loading in lower [...] Read more.
This study uses the observed vertical displacements of Global Positioning System (GPS) time series obtained from the Crustal Movement Observation Network of China (CMONOC) with careful pre- and post-processing to estimate the seasonal crustal deformation in response to the hydrological loading in lower three-rivers headwater region of southwest China, followed by inferring the annual EWH changes through geodetic inversion methods. The Helmert Variance Component Estimation (HVCE) and the Minimum Mean Square Error (MMSE) criterion were successfully employed. The GPS inferred EWH changes agree well qualitatively with the Gravity Recovery and Climate Experiment (GRACE)-inferred and the Global Land Data Assimilation System (GLDAS)-inferred EWH changes, with a discrepancy of 3.2–3.9 cm and 4.8–5.2 cm, respectively. In the research areas, the EWH changes in the Lancang basin is larger than in the other regions, with a maximum of 21.8–24.7 cm and a minimum of 3.1–6.9 cm. Full article
(This article belongs to the Section Remote Sensors)
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<p>(<b>a</b>–<b>d</b>) Daily values of vertical relative positions (with linear term removal) at four typical stations, with the mean position (blue solid lines), 2σ range (red dash lines), and the abrupt changes (red solid lines) when jumps are presented.</p>
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<p>(<b>a</b>–<b>d</b>) Time series of vertical positions at four stations before (red dots) and after (green dots) regional filtering. The time series are shifted by an offset for clarity.</p>
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<p>(<b>a</b>–<b>d</b>) Convergence plots of annual amplitude at GZSC, KMIN, SCMN, XIAG. Obtained by fitting constant, linear and annual terms to the first 365 days of the data, followed by adding that of the next 30 days and the fit is repeated until all data are used.</p>
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<p>Annual amplitudes of the vertical positions at the 29 stations estimated by maximum likelihood method, the noise model is power law plus white noise.</p>
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<p>Plots of the grid cells (with 1° × 1° size) and GPS station locations. The core area is inside the red box and the outside is the additional area.</p>
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<p>Plots of annual amplitude of EWH in the core areas derived from GPS, GRACE and GLDAS model. (<b>a</b>) GPS inferred EWH by HVCE method; (<b>b</b>) GPS inferred EWH by MMSE method; (<b>c</b>) GRACE derived EWH; (<b>d</b>) GLDAS derived EWH.</p>
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<p>Uncertainties of the two solutions. (<b>a</b>) Uncertainties of HVCE method; (<b>b</b>) Uncertainties of the MMSE method. The uncertainties are derived by performing a boot-strapping approach.</p>
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<p>Differences between EWHs derived by different methods. (<b>a</b>) GPS(HVCE)-GRACE; (<b>b</b>) GPS(MMSE)-GRACE; (<b>c</b>) GPS(HVCE)-GLDAS; (<b>d</b>) GPS(MMSE)-GLDAS.</p>
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6679 KiB  
Article
FGG-NUFFT-Based Method for Near-Field 3-D Imaging Using Millimeter Waves
by Yingzhi Kan, Yongfeng Zhu, Liang Tang, Qiang Fu and Hucheng Pei
Sensors 2016, 16(9), 1525; https://doi.org/10.3390/s16091525 - 19 Sep 2016
Cited by 11 | Viewed by 6196
Abstract
In this paper, to deal with the concealed target detection problem, an accurate and efficient algorithm for near-field millimeter wave three-dimensional (3-D) imaging is proposed that uses a two-dimensional (2-D) plane antenna array. First, a two-dimensional fast Fourier transform (FFT) is performed on [...] Read more.
In this paper, to deal with the concealed target detection problem, an accurate and efficient algorithm for near-field millimeter wave three-dimensional (3-D) imaging is proposed that uses a two-dimensional (2-D) plane antenna array. First, a two-dimensional fast Fourier transform (FFT) is performed on the scattered data along the antenna array plane. Then, a phase shift is performed to compensate for the spherical wave effect. Finally, fast Gaussian gridding based nonuniform FFT (FGG-NUFFT) combined with 2-D inverse FFT (IFFT) is performed on the nonuniform 3-D spatial spectrum in the frequency wavenumber domain to achieve 3-D imaging. The conventional method for near-field 3-D imaging uses Stolt interpolation to obtain uniform spatial spectrum samples and performs 3-D IFFT to reconstruct a 3-D image. Compared with the conventional method, our FGG-NUFFT based method is comparable in both efficiency and accuracy in the full sampled case and can obtain more accurate images with less clutter and fewer noisy artifacts in the down-sampled case, which are good properties for practical applications. Both simulation and experimental results demonstrate that the FGG-NUFFT-based near-field 3-D imaging algorithm can have better imaging performance than the conventional method for down-sampled measurements. Full article
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<p>Geometry of near-field 3-D imaging by 2-D plane array.</p>
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<p>3-D imaging result of the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Slice result at <span class="html-italic">y</span> = 0 m of the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Slice result at <span class="html-italic">z</span> = 0 m of the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Profile comparisons along <span class="html-italic">x</span> direction (<b>left</b>) and <span class="html-italic">z</span> direction (<b>right</b>).</p>
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<p>3-D imaging result under down-sampling rate of the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Slice result at <span class="html-italic">y</span> = 0 m under down-sampling rate of the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Slice result at <span class="html-italic">z</span> = 0 m under down-sampling rate of the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Profile comparisons under down-sampling rate along <span class="html-italic">x</span> direction (<b>left</b>) and <span class="html-italic">z</span> direction (<b>right</b>).</p>
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<p>Experimental imaging scene. (<b>a</b>) Experimental imaging system; (<b>b</b>) Scissors to be imaged.</p>
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<p>3-D reconstructed result of real data under down-sampled rate using the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Slice result at <span class="html-italic">z</span> = 0 m of real data under down-sampled rate using the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Slice result at <span class="html-italic">y</span> = 0 m of real data under down-sampled rate using the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Slice result at <span class="html-italic">x</span> = 0 m of real data under down-sampled rate using the conventional method (<b>left</b>) and the proposed method (<b>right</b>).</p>
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<p>Slices at different <span class="html-italic">z</span> depths using the conventional method and the proposed method. (<b>a</b>) Slice at <span class="html-italic">z</span> = 0 m using the conventional method; (<b>b</b>) Slice at <span class="html-italic">z</span> = 0 m using the proposed method; (<b>c</b>) Slice at <span class="html-italic">z</span> = 0.0075 m using the conventional method; (<b>d</b>) Slice at <span class="html-italic">z</span> = 0.0075 m using the proposed method; (<b>e</b>) Slice at <span class="html-italic">z</span> = −0.0075 m using the conventional method; (<b>f</b>) Slice at <span class="html-italic">z</span> = −0.0075 m using the proposed method.</p>
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<p>Slices at different <span class="html-italic">z</span> depths using the conventional method and the proposed method. (<b>a</b>) Slice at <span class="html-italic">z</span> = 0 m using the conventional method; (<b>b</b>) Slice at <span class="html-italic">z</span> = 0 m using the proposed method; (<b>c</b>) Slice at <span class="html-italic">z</span> = 0.0075 m using the conventional method; (<b>d</b>) Slice at <span class="html-italic">z</span> = 0.0075 m using the proposed method; (<b>e</b>) Slice at <span class="html-italic">z</span> = −0.0075 m using the conventional method; (<b>f</b>) Slice at <span class="html-italic">z</span> = −0.0075 m using the proposed method.</p>
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6129 KiB  
Article
A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings
by Huaqing Wang, Yanliang Ke, Liuyang Song, Gang Tang and Peng Chen
Sensors 2016, 16(9), 1524; https://doi.org/10.3390/s16091524 - 19 Sep 2016
Cited by 29 | Viewed by 5621
Abstract
The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse [...] Read more.
The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples. Full article
(This article belongs to the Section Physical Sensors)
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<p>Two-channel filter bank.</p>
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<p>Frequency response with different Q-factor values (<b>a</b>) Q = 1, R = 3; (<b>b</b>) Q = 5, R = 3.</p>
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<p>Frequency response with different Q-factor values (<b>a</b>) Q = 1, R = 3; (<b>b</b>) Q = 5, R = 3.</p>
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<p>Wavelet transform with level <span class="html-italic">L</span> = 3.</p>
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<p>The proposed fault detection method based on TQWT and CS.</p>
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<p>The simulated harmonic signal.</p>
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<p>The sparsity of the harmonic signal in the Fourier domain.</p>
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<p>(<b>a</b>) Fan system (<b>b</b>) Flow diagram of the fan system (<b>c</b>) Location of the sensors.</p>
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<p>(<b>a</b>) Inner-race fault (<b>b</b>) rolling-element fault.</p>
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<p>Original vibration signals collected from roller bearing with an inner-race fault.</p>
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<p>First detection result using the raw signals based on the method in [<a href="#B37-sensors-16-01524" class="html-bibr">37</a>].</p>
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<p>Second detection result using the raw signals based on the method in [<a href="#B37-sensors-16-01524" class="html-bibr">37</a>].</p>
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<p>Kurtogram of the signals as shown in <a href="#sensors-16-01524-f007" class="html-fig">Figure 7</a>.</p>
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<p>The transient impact component using TQWT.</p>
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<p>Envelope spectrum of the transient impact component in <a href="#sensors-16-01524-f011" class="html-fig">Figure 11</a>.</p>
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<p>Random sampling of transient impact component through Gaussian random matrix.</p>
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<p>The detected fault characteristic frequency through matching pursuit.</p>
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<p>Second harmonic of the detected fault characteristic frequency through matching pursuit.</p>
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<p>Original vibration signals collected from roller bearing with a rolling-element fault.</p>
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<p>First detection result using the raw signals based on the method in [<a href="#B37-sensors-16-01524" class="html-bibr">37</a>].</p>
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<p>Second detection result using the raw signals based on the method in [<a href="#B37-sensors-16-01524" class="html-bibr">37</a>].</p>
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<p>Kurtogram of the signals as shown in <a href="#sensors-16-01524-f016" class="html-fig">Figure 16</a>.</p>
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<p>The transient impact component using TQWT.</p>
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<p>Envelope spectrum of the transient impact component in <a href="#sensors-16-01524-f020" class="html-fig">Figure 20</a>.</p>
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<p>Random sampling of transient impact component through Gaussian random matrix.</p>
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<p>The detected fault characteristic frequency through matching pursuit.</p>
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<p>Second harmonic of the detected fault characteristic frequency through matching pursuit.</p>
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<p>Original vibration signals collected from healthy roller bearing.</p>
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<p>Kurtogram of the signals as shown in <a href="#sensors-16-01524-f025" class="html-fig">Figure 25</a>.</p>
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<p>The transient impact component using TQWT.</p>
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<p>Random sampling of transient impact component through Gaussian random matrix.</p>
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<p>The detected fault characteristic frequency through matching pursuit.</p>
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<p>Second harmonic of the detected fault characteristic frequency through matching pursuit.</p>
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2373 KiB  
Article
Friendly-Sharing: Improving the Performance of City Sensoring through Contact-Based Messaging Applications
by Jorge Herrera-Tapia, Enrique Hernández-Orallo, Andrés Tomás, Pietro Manzoni, Carlos Tavares Calafate and Juan-Carlos Cano
Sensors 2016, 16(9), 1523; https://doi.org/10.3390/s16091523 - 18 Sep 2016
Cited by 13 | Viewed by 7518
Abstract
Regular citizens equipped with smart devices are being increasingly used as “sensors” by Smart Cities applications. Using contacts among users, data in the form of messages is obtained and shared. Contact-based messaging applications are based on establishing a short-range communication directly between mobile [...] Read more.
Regular citizens equipped with smart devices are being increasingly used as “sensors” by Smart Cities applications. Using contacts among users, data in the form of messages is obtained and shared. Contact-based messaging applications are based on establishing a short-range communication directly between mobile devices, and on storing the messages in these devices for subsequent delivery to cloud-based services. An effective way to increase the number of messages that can be shared is to increase the contact duration. We thus introduce the Friendly-Sharing diffusion approach, where, during a contact, the users are aware of the time needed to interchange the messages stored in their buffers, and they can thus decide to wait more time in order to increase the message sharing probability. The performance of this approach is anyway closely related to the size of the buffer in the device. We therefore compare various policies either for the message selection at forwarding times and for message dropping when the buffer is full. We evaluate our proposal with a modified version of the Opportunistic Networking Environment (ONE) simulator and using real human mobility traces. Full article
(This article belongs to the Special Issue Smart City: Vision and Reality)
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<p>Several screenshots of the GRChat app. (<b>a</b>) a typical chat conversation; and (<b>b</b>) status of message interchange, showing the remaining time for end the transmission.</p>
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<p>Message forwarding (<b>a</b>) and dropping (<b>b</b>) policies in the local buffer.</p>
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<p>The ONE simulator (Helsinki University of Technology) running with the NCCU (National Chengchi University) traces.</p>
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<p>Modifications to the ONE simulator code (original figure from [<a href="#B16-sensors-16-01523" class="html-bibr">16</a>]).</p>
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<p>Contact graph for different time intervals of the trace: (<b>a</b>) 3 h; (<b>b</b>) 6 h; (<b>c</b>) 12 h and (<b>d</b>) 24 h.</p>
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<p>Average node speed at each hour of the movement trace.</p>
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<p>Average speed and number of contacts for all nodes at each hour of the movement trace.</p>
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<p>Delivery probability versus latency for a 12 and 24 h TTL (Time To Live) with different buffer sizes and queue policies.</p>
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<p>Average delivery success ratio and latency.</p>
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<p>Overhead results: Buffer occupancy and forwarded bytes. (<b>a</b>) maximum of the average buffer occupancy from each node; and (<b>b</b>) average bytes daily forwarded per node (<span class="html-italic">y</span>-axis in log scale).</p>
Full article ">Figure 11
<p>Delivery ratio (<b>a</b>) and latency (<b>b</b>) for different contact stop probabilities (16 s max. stop).</p>
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