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Sensors, Volume 17, Issue 12 (December 2017) – 262 articles

Cover Story (view full-size image): Berveglieri and co-workers report a low-cost technique that uses vertical optical scanning with a fisheye camera to produce dense point clouds in forest plots. From some constraints on the camera positions in the bundle adjustment, tree trunks can be accurately reconstructed and mapped in a local reference system. The quality of the generated measurements is comparable to point clouds obtained by terrestrial laser scanning (TLS), resulting in an average difference of less than 1 cm when reconstructing trunks. View the paper
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14869 KiB  
Article
Out-of-Focus Projector Calibration Method with Distortion Correction on the Projection Plane in the Structured Light Three-Dimensional Measurement System
by Jiarui Zhang, Yingjie Zhang and Bo Chen
Sensors 2017, 17(12), 2963; https://doi.org/10.3390/s17122963 - 20 Dec 2017
Cited by 5 | Viewed by 8839
Abstract
The three-dimensional measurement system with a binary defocusing technique is widely applied in diverse fields. The measurement accuracy is mainly determined by out-of-focus projector calibration accuracy. In this paper, a high-precision out-of-focus projector calibration method that is based on distortion correction on the [...] Read more.
The three-dimensional measurement system with a binary defocusing technique is widely applied in diverse fields. The measurement accuracy is mainly determined by out-of-focus projector calibration accuracy. In this paper, a high-precision out-of-focus projector calibration method that is based on distortion correction on the projection plane and nonlinear optimization algorithm is proposed. To this end, the paper experimentally presents the principle that the projector has noticeable distortions outside its focus plane. In terms of this principle, the proposed method uses a high-order radial and tangential lens distortion representation on the projection plane to correct the calibration residuals caused by projection distortion. The final accuracy parameters of out-of-focus projector were obtained using a nonlinear optimization algorithm with good initial values, which were provided by coarsely calibrating the parameters of the out-of-focus projector on the focal and projection planes. Finally, the experimental results demonstrated that the proposed method can accuracy calibrate an out-of-focus projector, regardless of the amount of defocusing. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1

Figure 1
<p>The principle of the structured light three-dimensional (3D) measurement system: (<b>a</b>) system components; and, (<b>b</b>) measurement principle.</p>
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<p>Pinhole camera model.</p>
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<p>Patterns encoding methods: (<b>a</b>) Four-bit gray-code; (<b>b</b>) Four-step phase-shifting; and, (<b>c</b>) absolute phase by combining four-bit gray-code and four-step phase-shifting.</p>
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<p>Simulation of the binary defocusing technique. (<b>a</b>) Binary structured pattern; (<b>b</b>) sinusoidal fringe pattern with low defocusing degree; (<b>c</b>) sinusoidal fringe pattern with high defocusing degree; and, (<b>d</b>) cross-section.</p>
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<p>Design of the calibration board.</p>
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<p>Re-projection errors under different defocusing degrees: (<b>a</b>) obtaining the different defocusing degrees using the first method; and, (<b>b</b>) obtaining the different defocusing degrees using the second method.</p>
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<p>The ARE of a projector under different defocusing degrees.</p>
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<p>Model of a structured light system with an out-of-focus projector.</p>
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<p>Model of an out-of-focus projector: (<b>a</b>) original model; and, (<b>b</b>) double the focal plane model.</p>
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<p>The four generated images in the first row are the vertical phase shifting fringe images <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">I</mi> <mrow> <msub> <mi mathvariant="normal">V</mi> <mn>1</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">I</mi> <mrow> <msub> <mi mathvariant="normal">V</mi> <mn>2</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">I</mi> <mrow> <msub> <mi mathvariant="normal">V</mi> <mn>3</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">I</mi> <mrow> <msub> <mi mathvariant="normal">V</mi> <mn>4</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>, the corresponding horizontal phase shifting fringe images <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">I</mi> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>1</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">I</mi> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>2</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">I</mi> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>3</mn> </msub> </mrow> </msub> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">I</mi> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>4</mn> </msub> </mrow> </msub> </mrow> </semantics> </math> are shown in the second row.</p>
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<p>Generated gray-code patterns.</p>
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<p>Example of the extracted correspondences circle centers for the camera and projector. (<b>a</b>) example of one calibration pose; (<b>b</b>) camera; and, (<b>c</b>) projector.</p>
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<p>Experimental system.</p>
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<p>Re-projection errors of the calibration points on the image planes: (<b>a</b>) camera; (<b>b</b>) out-of-focus projector using the conventional method in [<a href="#B29-sensors-17-02963" class="html-bibr">29</a>]; and, (<b>c</b>) out-of-focal projector using the proposed method.</p>
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<p>The measured distances in a 420 × 150 × 100 mm volume.</p>
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<p>Histogram of the measurement error of the 20 mm distances by: (<b>a</b>) the proposed method; and (<b>b</b>) the method in [<a href="#B29-sensors-17-02963" class="html-bibr">29</a>].</p>
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<p>Measurement results and error of plane under different defocusing degrees: (<b>a</b>) fitting plane; (<b>b</b>) measurement error of the plane under defocusing degree 1 (projector in focus); (<b>c</b>–<b>f</b>) measurement error of the plane under defocusing degree 2–5 by our proposed calibration method; (<b>g</b>–<b>j</b>) corresponding the measurement error of the plane under defocusing degree 2–5 by the calibration method in [<a href="#B29-sensors-17-02963" class="html-bibr">29</a>].</p>
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<p>Measurement results and error of plane under different defocusing degrees: (<b>a</b>) fitting plane; (<b>b</b>) measurement error of the plane under defocusing degree 1 (projector in focus); (<b>c</b>–<b>f</b>) measurement error of the plane under defocusing degree 2–5 by our proposed calibration method; (<b>g</b>–<b>j</b>) corresponding the measurement error of the plane under defocusing degree 2–5 by the calibration method in [<a href="#B29-sensors-17-02963" class="html-bibr">29</a>].</p>
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<p>Measurement error of plane under different defocusing degrees using our proposed calibration method and the calibration method in [<a href="#B29-sensors-17-02963" class="html-bibr">29</a>].</p>
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<p>Illustration of three different defocusing degrees: (<b>a</b>) a captured fringe image under defocusing degree 1 (projector in focus); (<b>b</b>) a captured fringe image under defocusing degree 2 (projector slightly defocused); (<b>c</b>) a captured fringe image under defocusing degree 5 (projector very defocused); and, (<b>d</b>–<b>f</b>) Corresponding cross sections of the intensity of (<b>a</b>–<b>c</b>).</p>
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<p>Measurement results and error of a hemisphere surface under different defocusing degrees: (<b>a</b>) fitting hemisphere measured under defocusing degree 1 (projector in focus); (<b>b</b>) cross section of the measurement result and the ideal circle; (<b>c</b>) error estimated in x direction; (<b>d</b>–<b>f</b>) and (<b>j</b>–<b>l</b>) correspond to figures (<b>a</b>–<b>c</b>) measured using our proposed calibration method under defocusing degrees 2 and 5; and, (<b>g</b>–<b>i</b>) and (<b>m</b>–<b>o</b>) correspond to figures (<b>a</b>–<b>c</b>) measured using the calibration method in [<a href="#B29-sensors-17-02963" class="html-bibr">29</a>] under defocusing degrees 2 and 5.</p>
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<p>Measurement error of the hemisphere under different defocusing degrees using our proposed calibration method and the calibration method in [<a href="#B29-sensors-17-02963" class="html-bibr">29</a>].</p>
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1568 KiB  
Article
Quantitative and Sensitive Detection of Chloramphenicol by Surface-Enhanced Raman Scattering
by Yufeng Ding, Xin Zhang, Hongjun Yin, Qingyun Meng, Yongmei Zhao, Luo Liu, Zhenglong Wu and Haijun Xu
Sensors 2017, 17(12), 2962; https://doi.org/10.3390/s17122962 - 20 Dec 2017
Cited by 31 | Viewed by 5460
Abstract
We used surface-enhanced Raman scattering (SERS) for the quantitative and sensitive detection of chloramphenicol (CAP). Using 30 nm colloidal Au nanoparticles (NPs), a low detection limit for CAP of 10−8 M was obtained. The characteristic Raman peak of CAP centered at 1344 [...] Read more.
We used surface-enhanced Raman scattering (SERS) for the quantitative and sensitive detection of chloramphenicol (CAP). Using 30 nm colloidal Au nanoparticles (NPs), a low detection limit for CAP of 10−8 M was obtained. The characteristic Raman peak of CAP centered at 1344 cm−1 was used for the rapid quantitative detection of CAP in three different types of CAP eye drops, and the accuracy of the measurement result was verified by high-performance liquid chromatography (HPLC). The experimental results reveal that the SERS technique based on colloidal Au NPs is accurate and sensitive, and can be used for the rapid detection of various antibiotics. Full article
(This article belongs to the Special Issue Surface Plasmon Resonance Sensing)
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) Transmission electron microscopy (TEM) image of 30 nm colloidal Au nanoparticles (NPs); (<b>b</b>) surface-enhanced Raman scattering (SERS) spectra of 10<sup>−3</sup> M rhodamine 6G (R6G) using colloidal Au NPs with different sizes (10, 20, 30, 40, and 50 nm); (<b>c</b>) SERS spectra of R6G with concentrations from 10<sup>−17</sup> to 10<sup>−2</sup> M (bottom to top, concentration successively increasing by a factor of 10) using 30 nm colloidal Au NPs. The intensity is multiplied five times for 10<sup>−11</sup> to 10<sup>−7</sup> M R6G and 10 times for 10<sup>−17</sup> to 10<sup>−12</sup> M R6G; (<b>d</b>) Linear relationship between log <span class="html-italic">I</span> of the band peak at 1361 cm<sup>−1</sup> and log <span class="html-italic">C</span> based on the SERS data of R6G in (<b>c</b>).</p>
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<p>(<b>a</b>) SERS spectra of chloramphenicol (CAP) solid powder and CAP solutions with concentrations from 10<sup>−8</sup> to 10<sup>−3</sup> M using 30 nm colloidal Au NPs; (<b>b</b>) Linear relationship between log <span class="html-italic">I</span> of the band peak at 1344 cm<sup>−1</sup> and log <span class="html-italic">C</span> based on the SERS data of the CAP solutions in (<b>a</b>) (six black spots). The cyan circle, blue pentagon, and green triangle represent the results for three different types of CAP eye drops below (samples 1, 2, and 3); (<b>c</b>) SERS spectra of three types of CAP eye drops using 30 nm colloidal Au NPs; (<b>d</b>) High-performance liquid chromatography (HPLC) linear relationship between <span class="html-italic">A</span> and <span class="html-italic">C</span> based on the data of pure CAP solutions with different concentrations (five black spots). The green triangle corresponds to sample 3.</p>
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2112 KiB  
Article
Location Accuracy of INS/Gravity-Integrated Navigation System on the Basis of Ocean Experiment and Simulation
by Hubiao Wang, Lin Wu, Hua Chai, Lifeng Bao and Yong Wang
Sensors 2017, 17(12), 2961; https://doi.org/10.3390/s17122961 - 20 Dec 2017
Cited by 18 | Viewed by 4835
Abstract
An experiment comparing the location accuracy of gravity matching-aided navigation in the ocean and simulation is very important to evaluate the feasibility and the performance of an INS/gravity-integrated navigation system (IGNS) in underwater navigation. Based on a 1′ × 1′ marine gravity anomaly [...] Read more.
An experiment comparing the location accuracy of gravity matching-aided navigation in the ocean and simulation is very important to evaluate the feasibility and the performance of an INS/gravity-integrated navigation system (IGNS) in underwater navigation. Based on a 1′ × 1′ marine gravity anomaly reference map and multi-model adaptive Kalman filtering algorithm, a matching location experiment of IGNS was conducted using data obtained using marine gravimeter. The location accuracy under actual ocean conditions was 2.83 nautical miles (n miles). Several groups of simulated data of marine gravity anomalies were obtained by establishing normally distributed random error N ( u , σ 2 ) with varying mean u and noise variance σ 2 . Thereafter, the matching location of IGNS was simulated. The results show that the changes in u had little effect on the location accuracy. However, an increase in σ 2 resulted in a significant decrease in the location accuracy. A comparison between the actual ocean experiment and the simulation along the same route demonstrated the effectiveness of the proposed simulation method and quantitative analysis results. In addition, given the gravimeter (1–2 mGal accuracy) and the reference map (resolution 1′ × 1′; accuracy 3–8 mGal), location accuracy of IGNS was up to reach ~1.0–3.0 n miles in the South China Sea. Full article
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)
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<p>Comparison between actual location of underwater submersible and INS navigation location.</p>
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<p>Ship-measured gravity areas and tracks.</p>
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<p>Comparison between gravimeter-measured data along the route and gravity anomaly interpolation at corresponding location of the reference map.</p>
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<p>Matching and location trace during the ocean experiment.</p>
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<p>Comparison between error of the INS-indicated location and the error of the matched location.</p>
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<p>Matching location trace in the simulation under noise condition <span class="html-italic">u</span> = 1 mGal and <span class="html-italic">σ</span><sup>2</sup> = 9 mGal<sup>2</sup>.</p>
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4388 KiB  
Article
Remotely Exploring Deeper-Into-Matter by Non-Contact Detection of Audible Transients Excited by Laser Radiation
by Javier Moros, Inmaculada Gaona and J. Javier Laserna
Sensors 2017, 17(12), 2960; https://doi.org/10.3390/s17122960 - 20 Dec 2017
Cited by 1 | Viewed by 4197
Abstract
An acoustic spectroscopic approach to detect contents within different packaging, with substantially wider applicability than other currently available subsurface spectroscopies, is presented. A frequency-doubled Nd:YAG (neodymium-doped yttrium aluminum garnet) pulsed laser (13 ns pulse length) operated at 1 Hz was used to generate [...] Read more.
An acoustic spectroscopic approach to detect contents within different packaging, with substantially wider applicability than other currently available subsurface spectroscopies, is presented. A frequency-doubled Nd:YAG (neodymium-doped yttrium aluminum garnet) pulsed laser (13 ns pulse length) operated at 1 Hz was used to generate the sound field of a two-component system at a distance of 50 cm. The acoustic emission was captured using a unidirectional microphone and analyzed in the frequency domain. The focused laser pulse hitting the system, with intensity above that necessary to ablate the irradiated surface, transferred an impulsive force which led the structure to vibrate. Acoustic airborne transients were directly radiated by the vibrating elastic structure of the outer component that excited the surrounding air in contact with. However, under boundary conditions, sound field is modulated by the inner component that modified the dynamical integrity of the system. Thus, the resulting frequency spectra are useful indicators of the concealed content that influences the contributions originating from the wall of the container. High-quality acoustic spectra could be recorded from a gas (air), liquid (water), and solid (sand) placed inside opaque chemical-resistant polypropylene and stainless steel sample containers. Discussion about effects of laser excitation energy and sampling position on the acoustic emission events is reported. Acoustic spectroscopy may complement the other subsurface alternative spectroscopies, severely limited by their inherent optical requirements for numerous detection scenarios. Full article
(This article belongs to the Special Issue Spectroscopy Based Sensors)
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) Graphical representation of the laser-induced (40 mJ) acoustic response from the central position of the <span class="html-italic">generatrix</span> of an SS container (350 mL) filled with air; (<b>b</b>) Laser-induced acoustic spectra for the three containers considered containing air. From <b>top</b> to <b>bottom</b>: SS container (350 mL), SS container (250 mL), and polypropylene (PP) container. More details in the body of the text.</p>
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<p>(<b>a</b>) Laser-induced (40 mJ) acoustic spectra for an SS container (250 mL) filled with different materials; (<b>b</b>) Laser-induced (40 mJ) acoustic spectra for a PP container filled with different materials. Sampling point was in both cases the central position of the container <span class="html-italic">generatrix</span>.</p>
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<p>(<b>a</b>) Acoustic spectra induced at the bottom of the <span class="html-italic">generatrix</span> of an SS container (350 mL) filled with sand induced at varying laser pulse energy; (<b>b</b>) Acoustic spectra induced at the central position of the <span class="html-italic">generatrix</span> of an SS container (350 mL) filled with water induced at varying laser pulse energy.</p>
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<p>(<b>a</b>) Laser-induced (40 mJ) acoustic spectra for an SS container (350 mL) filled with sand at five different sampling positions along its <span class="html-italic">generatrix</span>; (<b>b</b>) Laser-induced (40 mJ) acoustic spectra for a PP container filled with water at five different sampling positions along its <span class="html-italic">generatrix</span>.</p>
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5217 KiB  
Article
A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks
by Wei Zheng, Xiaoyong Yan, Wei Zhao and Chengshan Qian
Sensors 2017, 17(12), 2959; https://doi.org/10.3390/s17122959 - 20 Dec 2017
Cited by 9 | Viewed by 4559
Abstract
A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which [...] Read more.
A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters. Full article
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<p>Different distribution of nodes (<b>a</b>) Isotropic network; (<b>b</b>) Anisotropic network.</p>
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<p>The localization results of SM for different regional center nodes and for different number of regions (<b>a</b>) The distribution of nodes; (<b>b</b>) The distribution of anchor nodes; (<b>c</b>) The center node is #12, area is divided into three parts; (<b>d</b>) The localization result of SM corresponds to the <a href="#sensors-17-02959-f002" class="html-fig">Figure 2</a>c; (<b>e</b>) The center node is #17, area is divided into three parts; (<b>f</b>) The localization result of SM corresponds to the <a href="#sensors-17-02959-f002" class="html-fig">Figure 2</a>e; (<b>g</b>) The center node is #12, area is divided into two parts; (<b>h</b>) The localization result of SM corresponds to the <a href="#sensors-17-02959-f002" class="html-fig">Figure 2</a>g.</p>
Full article ">Figure 2 Cont.
<p>The localization results of SM for different regional center nodes and for different number of regions (<b>a</b>) The distribution of nodes; (<b>b</b>) The distribution of anchor nodes; (<b>c</b>) The center node is #12, area is divided into three parts; (<b>d</b>) The localization result of SM corresponds to the <a href="#sensors-17-02959-f002" class="html-fig">Figure 2</a>c; (<b>e</b>) The center node is #17, area is divided into three parts; (<b>f</b>) The localization result of SM corresponds to the <a href="#sensors-17-02959-f002" class="html-fig">Figure 2</a>e; (<b>g</b>) The center node is #12, area is divided into two parts; (<b>h</b>) The localization result of SM corresponds to the <a href="#sensors-17-02959-f002" class="html-fig">Figure 2</a>g.</p>
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<p>The schematic diagram of ELM.</p>
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<p>The framework of proposed algorithm. The mapping is firstly trained by REML, using supervised data consist of the known hop-counts and physical distances. After that, in the testing phase, the physical distances of the unknown node are predicted by the learned mapping.</p>
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<p>The regular distribution of nodes.</p>
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<p>The localization results of regularly distributed nodes.</p>
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<p>Average RMS for different algorithms in the regular deployment environment.</p>
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<p>Comparison of different algorithms with different number of anchor nodes in the regular deployment.</p>
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<p>The random distribution of nodes.</p>
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<p>The localization results of randomly distributed nodes.</p>
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<p>The localization results of randomly distributed nodes.</p>
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<p>Average RMS for different algorithms in the random deployment environment.</p>
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<p>Comparison of different algorithms with different number of anchor nodes in the random deployment.</p>
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<p>Nodes distribution under realistic scenarios (<b>a</b>) Satellite picture of outdoor location; (<b>b</b>) Picture of outdoor test-bed; (<b>c</b>) Topological graph of outdoor test-bed; (<b>d</b>) Satellite picture of indoor location; (<b>e</b>) Picture of indoor test-bed; (<b>f</b>) Topological graph of indoor test-bed.</p>
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<p>Results of location estimation in realistic scenarios (<b>a</b>) DV-hop in the outdoor, RMS = 3.1446; (<b>b</b>) PDM in the outdoor, RMS = 3.0085; (<b>c</b>) LSVR in the outdoor, RMS = 5.7151; (<b>d</b>) ML-RELM in the outdoor, RMS = 2.6829; (<b>e</b>) DV-hop in the indoor, RMS = 6.6422; (<b>f</b>) PDM in the indoor, RMS = 4.4114; (<b>g</b>) LSVR in the indoor, RMS = 5.0065; (<b>h</b>) ML-RELM in the indoor, RMS = 2.5197.</p>
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<p>The Cumulative Distribution Function (CDF) of the errors in realistic scenarios (<b>a</b>) CDF of outdoor; (<b>b</b>) CDF of indoor.</p>
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1170 KiB  
Article
Optimal Rate Schedules with Data Sharing in Energy Harvesting Communication Systems
by Weiwei Wu, Huafan Li, Feng Shan and Yingchao Zhao
Sensors 2017, 17(12), 2958; https://doi.org/10.3390/s17122958 - 20 Dec 2017
Cited by 1 | Viewed by 3541
Abstract
Despite the abundant research on energy-efficient rate scheduling polices in energy harvesting communication systems, few works have exploited data sharing among multiple applications to further enhance the energy utilization efficiency, considering that the harvested energy from environments is limited and unstable. In this [...] Read more.
Despite the abundant research on energy-efficient rate scheduling polices in energy harvesting communication systems, few works have exploited data sharing among multiple applications to further enhance the energy utilization efficiency, considering that the harvested energy from environments is limited and unstable. In this paper, to overcome the energy shortage of wireless devices at transmitting data to a platform running multiple applications/requesters, we design rate scheduling policies to respond to data requests as soon as possible by encouraging data sharing among data requests and reducing the redundancy. We formulate the problem as a transmission completion time minimization problem under constraints of dynamical data requests and energy arrivals. We develop offline and online algorithms to solve this problem. For the offline setting, we discover the relationship between two problems: the completion time minimization problem and the energy consumption minimization problem with a given completion time. We first derive the optimal algorithm for the min-energy problem and then adopt it as a building block to compute the optimal solution for the min-completion-time problem. For the online setting without future information, we develop an event-driven online algorithm to complete the transmission as soon as possible. Simulation results validate the efficiency of the proposed algorithm. Full article
(This article belongs to the Section Sensor Networks)
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<p>Exampary schedules with and without data sharing.</p>
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<p>The case that <math display="inline"> <semantics> <msup> <mi>r</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>t</mi> </mrow> </msup> </semantics> </math> intersects with <math display="inline"> <semantics> <msup> <mi>r</mi> <mrow> <mi>M</mi> <mi>T</mi> </mrow> </msup> </semantics> </math>.</p>
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<p>Performance of O<span class="html-small-caps">nline</span>-S<span class="html-small-caps">elect</span> as the requests change.</p>
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<p>Performance of O<span class="html-small-caps">nline</span>-S<span class="html-small-caps">elect</span> as the harvestings change.</p>
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<p>Effect of exploiting data sharing as the average harvesting amount/workload changes.</p>
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3695 KiB  
Article
An Architecture Providing Depolarization Ratio Capability for a Multi-Wavelength Raman Lidar: Implementation and First Measurements
by Alejandro Rodríguez-Gómez, Michaël Sicard, María-José Granados-Muñoz, Enis Ben Chahed, Constantino Muñoz-Porcar, Rubén Barragán, Adolfo Comerón, Francesc Rocadenbosch and Eric Vidal
Sensors 2017, 17(12), 2957; https://doi.org/10.3390/s17122957 - 20 Dec 2017
Cited by 9 | Viewed by 4746
Abstract
A new architecture for the measurement of depolarization produced by atmospheric aerosols with a Raman lidar is presented. The system uses two different telescopes: one for depolarization measurements and another for total-power measurements. The system architecture and principle of operation are described. The [...] Read more.
A new architecture for the measurement of depolarization produced by atmospheric aerosols with a Raman lidar is presented. The system uses two different telescopes: one for depolarization measurements and another for total-power measurements. The system architecture and principle of operation are described. The first experimental results are also presented, corresponding to a collection of atmospheric conditions over the city of Barcelona. Full article
(This article belongs to the Section Remote Sensors)
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<p>Main receiver of UPC Raman lidar [<a href="#B20-sensors-17-02957" class="html-bibr">20</a>], presenting the telescope, the fiber bundle and the wavelength separation unit, which delivers the collected light to the different receivers: an avalanche-photodiode (APD) for 1064 nm and photo-multiplier tubes (PMT) for the other channels.</p>
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<p>Auxiliary channel for depolarization measurements, where the most relevant elements are labelled.</p>
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<p>Depolarization channel optical configuration; L1 to L3 are the lenses included in the telephoto lens; L4 works as an eye-piece lens that produces an image of the telephoto lens input aperture on the PMT active surface; P is a polarizing analyzer; IF is an interference filter centered at 532 nm; distances d4 to d8 are listed in <a href="#sensors-17-02957-t001" class="html-table">Table 1</a>.</p>
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<p>Spot diagram of the distribution of the collected rays, parallel to the optical axis, over the 8-mm diameter active surface of the photo-multiplier detector tube calculated with ZEMAX<sup>®</sup> software.</p>
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<p>Spot diagram of the distribution of the collected extreme rays, entering the optical system with an angle equal to half the effective field of view (0.09°, over the 8-mm diameter active surface of the photo-multiplier detector tube calculated with ZEMAX<sup>®</sup> software. This diagram shows that the centroid of the collected rays is displaced by approximately 130 µm in the vertical (negative sense) direction, with respect to <a href="#sensors-17-02957-f004" class="html-fig">Figure 4</a>.</p>
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<p>Complete view of the UPC lidar system: the laser on the left (including 2nd and 3rd harmonic generators), the main telescope in the middle and the depolarization auxiliary channel on the right.</p>
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<p>History of the calibrations of the depolarization channel system function obtained from March 2016 to June 2017. The colder colors refer to early calibrations while the warmer ones to the recent ones.</p>
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<p>Stability of the value of the depolarization channel system function for far range; the values comprised between realignment actions are marked by closed curves.</p>
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<p>Some examples of volume and particle depolarization ratio retrievals showing (left) time-height plots of range-square corrected signals in arbitrary units, (center) particle backscatter coefficient at 532 nm, (right) volume and particle depolarization ratios at 532 nm for (<b>a</b>) pollen; (<b>b</b>) dust; (<b>c</b>) dust and fire smoke; (<b>d</b>) cirrus cloud; (<b>e</b>) local urban. The points of the particle depolarization ratio profiles for which the associated error is larger than 50% are not represented.</p>
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<p>Some examples of volume and particle depolarization ratio retrievals showing (left) time-height plots of range-square corrected signals in arbitrary units, (center) particle backscatter coefficient at 532 nm, (right) volume and particle depolarization ratios at 532 nm for (<b>a</b>) pollen; (<b>b</b>) dust; (<b>c</b>) dust and fire smoke; (<b>d</b>) cirrus cloud; (<b>e</b>) local urban. The points of the particle depolarization ratio profiles for which the associated error is larger than 50% are not represented.</p>
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<p>Some examples of volume and particle depolarization ratio retrievals showing (left) time-height plots of range-square corrected signals in arbitrary units, (center) particle backscatter coefficient at 532 nm, (right) volume and particle depolarization ratios at 532 nm for (<b>a</b>) pollen; (<b>b</b>) dust; (<b>c</b>) dust and fire smoke; (<b>d</b>) cirrus cloud; (<b>e</b>) local urban. The points of the particle depolarization ratio profiles for which the associated error is larger than 50% are not represented.</p>
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7375 KiB  
Article
Enhancing Time Synchronization Support in Wireless Sensor Networks
by Leandro Tavares Bruscato, Tales Heimfarth and Edison Pignaton de Freitas
Sensors 2017, 17(12), 2956; https://doi.org/10.3390/s17122956 - 20 Dec 2017
Cited by 32 | Viewed by 5776
Abstract
With the emerging Internet of Things (IoT) technology becoming reality, a number of applications are being proposed. Several of these applications are highly dependent on wireless sensor networks (WSN) to acquire data from the surrounding environment. In order to be really useful for [...] Read more.
With the emerging Internet of Things (IoT) technology becoming reality, a number of applications are being proposed. Several of these applications are highly dependent on wireless sensor networks (WSN) to acquire data from the surrounding environment. In order to be really useful for most of applications, the acquired data must be coherent in terms of the time in which they are acquired, which implies that the entire sensor network presents a certain level of time synchronization. Moreover, to efficiently exchange and forward data, many communication protocols used in WSN rely also on time synchronization among the sensor nodes. Observing the importance in complying with this need for time synchronization, this work focuses on the second synchronization problem, proposing, implementing and testing a time synchronization service for low-power WSN using low frequency real-time clocks in each node. To implement this service, three algorithms based on different strategies are proposed: one based on an auto-correction approach, the second based on a prediction mechanism, while the third uses an analytical correction mechanism. Their goal is the same, i.e., to make the clocks of the sensor nodes converge as quickly as possible and then to keep them most similar as possible. This goal comes along with the requirement to keep low energy consumption. Differently from other works in the literature, the proposal here is independent of any specific protocol, i.e., it may be adapted to be used in different protocols. Moreover, it explores the minimum number of synchronization messages by means of a smart clock update strategy, allowing the trade-off between the desired level of synchronization and the associated energy consumption. Experimental results, which includes data acquired from simulations and testbed deployments, provide evidence of the success in meeting this goal, as well as providing means to compare these three approaches considering the best synchronization results and their costs in terms of energy consumption. Full article
(This article belongs to the Special Issue Internet of Things and Ubiquitous Sensing)
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<p>Clock model of sensor nodes.</p>
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<p>Self-Correction algorithm.</p>
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<p>Single-hop simulation.</p>
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<p>Multi-hop simulation design. To simplify the graphical presentation of the simulation results, only the measured results for nodes 1, 2, 5, and 10 are displayed. Since nodes 3 and 4 have intermediate values between nodes 2 and 5, and nodes 6, 7, 8, and 9 are intermediate nodes between 5 and 10, this simplification can be made assuming that the errors are cumulative. Thus, the provided results are representative to the whole network.</p>
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<p>Multi-hop simulation.</p>
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<p>Test layout of many client and server network.</p>
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<p>Set of experiments with 1 s transmission period.</p>
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<p>Set of experiments with 10 s transmission period.</p>
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<p>Set of experiments with 30 s transmission period.</p>
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<p>Set of experiments with 60 s transmission period.</p>
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<p>Set of experiments with chain synchronization.</p>
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<p>Energy consumption results during 1 h.</p>
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3300 KiB  
Article
Anti-Runaway Prevention System with Wireless Sensors for Intelligent Track Skates at Railway Stations
by Chaozhe Jiang, Yibo Xu, Chao Wen and Dilin Chen
Sensors 2017, 17(12), 2955; https://doi.org/10.3390/s17122955 - 19 Dec 2017
Cited by 3 | Viewed by 4737
Abstract
Anti-runaway prevention of rolling stocks at a railway station is essential in railway safety management. The traditional track skates for anti-runaway prevention of rolling stocks have some disadvantages since they are operated and monitored completely manually. This paper describes an anti-runaway prevention system [...] Read more.
Anti-runaway prevention of rolling stocks at a railway station is essential in railway safety management. The traditional track skates for anti-runaway prevention of rolling stocks have some disadvantages since they are operated and monitored completely manually. This paper describes an anti-runaway prevention system (ARPS) based on intelligent track skates equipped with sensors and real-time monitoring and management system. This system, which has been updated from the traditional track skates, comprises four parts: intelligent track skates, a signal reader, a database station, and a monitoring system. This system can monitor the real-time situation of track skates without changing their workflow for anti-runaway prevention, and thus realize the integration of anti-runaway prevention information management. This system was successfully tested and practiced at Sunjia station in Harbin Railway Bureau in 2014, and the results confirmed that the system showed 100% accuracy in reflecting the usage status of the track skates. The system could meet practical demands, as it is highly reliable and supports long-distance communication. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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<p>Composition and the work process of anti-runaway prevention system (ARPS).</p>
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<p>The terminal of intelligent track skate shell.</p>
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<p>Detecting and communicating sensor of intelligent track skates.</p>
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<p>On-track inspection travel switch.</p>
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<p>Wireless transceiver circuit.</p>
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<p><b>A</b> 7000mAh/3.6 V lithium battery.</p>
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<p>The position of the sensor and its enclosure of a track skate.</p>
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<p>Signal Reader.</p>
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<p>Internal structure of data station.</p>
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<p>Monitoring interface.</p>
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1824 KiB  
Article
Sulfophenyl-Functionalized Reduced Graphene Oxide Networks on Electrospun 3D Scaffold for Ultrasensitive NO2 Gas Sensor
by Bin Zou, Yunlong Guo, Nannan Shen, Anshan Xiao, Mingjun Li, Liang Zhu, Pengbo Wan and Xiaoming Sun
Sensors 2017, 17(12), 2954; https://doi.org/10.3390/s17122954 - 19 Dec 2017
Cited by 18 | Viewed by 6384
Abstract
Ultrasensitive room temperature real-time NO2 sensors are highly desirable due to potential threats on environmental security and personal respiratory. Traditional NO2 gas sensors with highly operated temperatures (200–600 °C) and limited reversibility are mainly constructed from semiconducting oxide-deposited ceramic tubes or [...] Read more.
Ultrasensitive room temperature real-time NO2 sensors are highly desirable due to potential threats on environmental security and personal respiratory. Traditional NO2 gas sensors with highly operated temperatures (200–600 °C) and limited reversibility are mainly constructed from semiconducting oxide-deposited ceramic tubes or inter-finger probes. Herein, we report the functionalized graphene network film sensors assembled on an electrospun three-dimensional (3D) nanonetwork skeleton for ultrasensitive NO2 sensing. The functional 3D scaffold was prepared by electrospinning interconnected polyacrylonitrile (PAN) nanofibers onto a nylon window screen to provide a 3D nanonetwork skeleton. Then, the sulfophenyl-functionalized reduced graphene oxide (SFRGO) was assembled on the electrospun 3D nanonetwork skeleton to form SFRGO network films. The assembled functionalized graphene network film sensors exhibit excellent NO2 sensing performance (10 ppb to 20 ppm) at room temperature, reliable reversibility, good selectivity, and better sensing cycle stability. These improvements can be ascribed to the functionalization of graphene with electron-withdrawing sulfophenyl groups, the high surface-to-volume ratio, and the effective sensing channels from SFRGO wrapping onto the interconnected 3D scaffold. The SFRGO network-sensing film has the advantages of simple preparation, low cost, good processability, and ultrasensitive NO2 sensing, all advantages that can be utilized for potential integration into smart windows and wearable electronic devices for real-time household gas sensors. Full article
(This article belongs to the Special Issue Carbon Materials Based Sensors and the Application)
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<p>SEM images of (<b>a</b>) the electrospun polyacrylonitrile (PAN) nanofiber skeleton (inset: the high-resolution SEM image), (<b>b</b>) the wrapped SFRGO onto the PAN nanofiber skeleton (inset: the high-resolution SEM image). Photographs of (<b>c</b>) the electrospun PAN nanofibers on the nylon scaffold, and (<b>d</b>) the flexible SFRGO network films. The school badges are placed behind the film.</p>
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<p>The energy-dispersive X-ray spectroscopy (EDX) spectra of sulfophenyl-functionalized reduced graphene oxide (SFRGO).</p>
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<p>(<b>a</b>) The X-ray photoelectron spectroscopy (XPS) spectra of SFRGO. XPS spectra for (<b>b</b>) C 1s, (<b>c</b>) S2p, and (<b>d</b>) O 1s.</p>
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<p>Raman spectra of SFRGO and reduced graphene oxide (rGO).</p>
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<p>Sensing performance of the SFRGO network film devices to different concentrations of NO<sub>2</sub>, from 10 ppb to 20 ppm ((<b>a</b>) 10–100 ppb; (<b>b</b>) 100–1000 ppb; (<b>c</b>) 1 ppm–20 ppm).</p>
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<p>Gas sensing selectivity of the SFRGO network film devices to 10 ppm NO<sub>2</sub> and 25 ppm methanol, 25 ppm ethanol, 25 ppm isopropanol, 25 ppm chlorine, and 50% relative humidity.</p>
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<p>The sensing repeatability for the SFRGO network film devices to 500 ppb NO<sub>2</sub>.</p>
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<p>Schematic illustration for the fabrication of the sulfophenyl-functionalized reduced graphene oxide (SFRGO) network films by assembling the SFRGO onto an electrospun 3D nanonetwork skeleton.</p>
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3981 KiB  
Article
3D-Printed Detector Band for Magnetic Off-Plane Flux Measurements in Laminated Machine Cores
by Georgi Shilyashki, Helmut Pfützner, Martin Palkovits, Andreas Windischhofer and Markus Giefing
Sensors 2017, 17(12), 2953; https://doi.org/10.3390/s17122953 - 19 Dec 2017
Cited by 7 | Viewed by 4261
Abstract
Laminated soft magnetic cores of transformers, rotating machines etc. may exhibit complex 3D flux distributions with pronounced normal fluxes (off-plane fluxes), perpendicular to the plane of magnetization. As recent research activities have shown, detections of off-plane fluxes tend to be essential for the [...] Read more.
Laminated soft magnetic cores of transformers, rotating machines etc. may exhibit complex 3D flux distributions with pronounced normal fluxes (off-plane fluxes), perpendicular to the plane of magnetization. As recent research activities have shown, detections of off-plane fluxes tend to be essential for the optimization of core performances aiming at a reduction of core losses and of audible noise. Conventional sensors for off-plane flux measurements tend to be either of high thickness, influencing the measured fluxes significantly, or require laborious preparations. In the current work, thin novel detector bands for effective and simple off-plane flux detections in laminated machine cores were manufactured. They are printed in an automatic way by an in-house developed 3D/2D assembler. The latter enables a unique combination of conductive and non-conductive materials. The detector bands were effectively tested in the interior of a two-package, three-phase model transformer core. They proved to be mechanically resilient, even for strong clamping of the core. Full article
(This article belongs to the Special Issue Magnetic Sensors and Their Applications)
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<p>Manufacturing of detector bands: (<b>a</b>) 2D/3D printing assembler (insert: print sensors); (<b>b</b>) A manufactured band with three frame coil sensors for simultaneous detection of off-plane induction at three locations.</p>
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<p>A rough outline of the T-joint region of a two-package model transformer core. The detector band is located between the wider main package P1 and the narrower upper package P2, very close to the overlap of P1. The paper presents a result only from the first uppermost frame-coil sensor, denoted as a “test region”.</p>
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<p>Detected off-plane induction <span class="html-italic">B</span><sub>ND</sub>(<span class="html-italic">t</span>) in the “test region” (compare <a href="#sensors-17-02953-f002" class="html-fig">Figure 2</a>) for <span class="html-italic">B</span><sub>NOM</sub> = 1.8 T. The courses of time of the inductions <span class="html-italic">B</span><sub>S</sub>(<span class="html-italic">t</span>), <span class="html-italic">B</span><sub>T</sub>(<span class="html-italic">t</span>), <span class="html-italic">B</span><sub>R</sub>(<span class="html-italic">t</span>) of the three limbs, measured by means of search coils are illustrated as well.</p>
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2456 KiB  
Review
Sensors and Biosensors for C-Reactive Protein, Temperature and pH, and Their Applications for Monitoring Wound Healing: A Review
by Pietro Salvo, Valentina Dini, Arno Kirchhain, Agata Janowska, Teresa Oranges, Andrea Chiricozzi, Tommaso Lomonaco, Fabio Di Francesco and Marco Romanelli
Sensors 2017, 17(12), 2952; https://doi.org/10.3390/s17122952 - 19 Dec 2017
Cited by 91 | Viewed by 13482
Abstract
Wound assessment is usually performed in hospitals or specialized labs. However, since patients spend most of their time at home, a remote real time wound monitoring would help providing a better care and improving the healing rate. This review describes the advances in [...] Read more.
Wound assessment is usually performed in hospitals or specialized labs. However, since patients spend most of their time at home, a remote real time wound monitoring would help providing a better care and improving the healing rate. This review describes the advances in sensors and biosensors for monitoring the concentration of C-reactive protein (CRP), temperature and pH in wounds. These three parameters can be used as qualitative biomarkers to assess the wound status and the effectiveness of therapy. CRP biosensors can be classified in: (a) field effect transistors, (b) optical immunosensors based on surface plasmon resonance, total internal reflection, fluorescence and chemiluminescence, (c) electrochemical sensors based on potentiometry, amperometry, and electrochemical impedance, and (d) piezoresistive sensors, such as quartz crystal microbalances and microcantilevers. The last section reports the most recent developments for wearable non-invasive temperature and pH sensors suitable for wound monitoring. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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<p>(<b>a</b>) Schematization of the nanogap-embedded NG-SiNW-FET (top view) and (<b>b</b>) detail of the use as immunosensor for CRP (Adapted from [<a href="#B55-sensors-17-02952" class="html-bibr">55</a>]); (<b>c</b>) schematization of the hybrid MOSFET-BJT. The BJT base current could be used for tuning the sensitivity for CRP detection (Adapted with permission from [<a href="#B57-sensors-17-02952" class="html-bibr">57</a>]); (<b>d</b>) schematic view of the nanogap-embedded FET with the gate dielectric made of air and SiO<sub>2</sub> (Adapted with permission from [<a href="#B58-sensors-17-02952" class="html-bibr">58</a>]); (<b>e</b>) schematic view of a HEMT for detecting CRP. 2DEG is the two dimensional electron gas at the interface modulated by CRP binding ([<a href="#B62-sensors-17-02952" class="html-bibr">62</a>], licensed under CC BY 4.0).</p>
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<p>(<b>a</b>) Schematic view of the SPR chip with biotinylated aptamers coated by NIR-QDs (nanoenhancers) ([<a href="#B72-sensors-17-02952" class="html-bibr">72</a>], licensed under CC BY 3.0); (<b>b</b>) schematic view of the S-DAB–ZnSe–PEA QDs nanocomposite (Reprinted with permission from [<a href="#B86-sensors-17-02952" class="html-bibr">86</a>]).</p>
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<p>(<b>a</b>) Schematic process of the fabrication and working principle of the bismuth citrate-modified sandwich-type immunosensor proposed in (Reprinted with permission from [<a href="#B93-sensors-17-02952" class="html-bibr">93</a>]). The detection was performed by anodic scan voltammetry to detect the release of Pb(II) from the QDs in HNO<sub>3</sub>; (<b>b</b>) top: DNA-direct immobilization of anti-CRP onto a gold nanowire; bottom: changes in the electrochemical impedance after each step ([<a href="#B97-sensors-17-02952" class="html-bibr">97</a>], licensed under CC BY-NC-ND 4.0).</p>
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<p>(<b>a</b>) Preparation of the Fe<sub>3</sub>O<sub>4</sub>-Au magnetic nanoparticles coated with anti-CRP and HRP ([<a href="#B104-sensors-17-02952" class="html-bibr">104</a>], licensed under CC BY 3.0); (<b>b</b>) SEM image of the microcantilever fabricated in [<a href="#B111-sensors-17-02952" class="html-bibr">111</a>] (licensed under CC BY 3.0).</p>
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<p>(<b>a</b>) An example of a flexible PAN pH sensor with (<b>b</b>) sensor dimensions; (<b>c</b>,<b>d</b>) SEM images of the PAN sensor (Reprinted with permission from [<a href="#B128-sensors-17-02952" class="html-bibr">128</a>]); (<b>e</b>) examples of the pH (left) and temperature (right) screen-printed sensors to use in direct contact with a wound (Reprinted with permission from [<a href="#B120-sensors-17-02952" class="html-bibr">120</a>]).</p>
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1536 KiB  
Review
Achievements and Prospects in Electrochemical-Based Biosensing Platforms for Aflatoxin M1 Detection in Milk and Dairy Products
by Ana-Maria Gurban, Petru Epure, Florin Oancea and Mihaela Doni
Sensors 2017, 17(12), 2951; https://doi.org/10.3390/s17122951 - 19 Dec 2017
Cited by 27 | Viewed by 7658
Abstract
Aflatoxins, which are mainly produced by Aspergillus flavus and parasiticus growing on plants and products stored under inappropriate conditions, represent the most studied group of mycotoxins. Contamination of human and animal milk with aflatoxin M1, the hydroxylated metabolite of aflatoxin B [...] Read more.
Aflatoxins, which are mainly produced by Aspergillus flavus and parasiticus growing on plants and products stored under inappropriate conditions, represent the most studied group of mycotoxins. Contamination of human and animal milk with aflatoxin M1, the hydroxylated metabolite of aflatoxin B1, is an important health risk factor due to its carcinogenicity and mutagenicity. Due to the low concentration of this aflatoxin in milk and milk products, the analytical methods used for its quantification have to be highly sensitive, specific and simple. This paper presents an overview of the analytical methods, especially of the electrochemical immunosensors and aptasensors, used for determination of aflatoxin M1. Full article
(This article belongs to the Special Issue Protein-Based Biosensors)
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<p>Chemical structures of aflatoxins and their metabolites.</p>
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<p>The biodegradation of aflatoxin B<sub>1</sub> through metabolic pathways. DNA = deoxyribonucleic acid. NADPH = nicotinamide adenine dinucleotide phosphate. CYP450 = cytochrome P450. GST = glutathione-S-transferase.</p>
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<p>Detection of aflatoxin M<sub>1</sub> (AFM<sub>1</sub>) using immunostrip.</p>
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3650 KiB  
Article
Investigation of Wavenumber Domain Imaging Algorithm for Ground-Based Arc Array SAR
by Zengshu Huang, Jinping Sun, Weixian Tan, Pingping Huang and Kuoye Han
Sensors 2017, 17(12), 2950; https://doi.org/10.3390/s17122950 - 19 Dec 2017
Cited by 15 | Viewed by 4919
Abstract
Ground-based synthetic aperture radar (GB-SAR) has become an important technique for remote sensing deformation monitoring. However, most of the existing GB-SAR systems realize synthetic aperture by exploiting two closely spaced horn antennas to move along a linear rail. In order to obtain higher [...] Read more.
Ground-based synthetic aperture radar (GB-SAR) has become an important technique for remote sensing deformation monitoring. However, most of the existing GB-SAR systems realize synthetic aperture by exploiting two closely spaced horn antennas to move along a linear rail. In order to obtain higher data acquisition efficiency and a wider view angle, we introduce arc antenna array technology into the GB-SAR system, which realizes a novel kind of system: ground-based arc array SAR (GB-AA-SAR). In this paper, we analyze arc observation geometry and derive analytic expressions of sampling criteria. Then, we propose a novel wavenumber domain imaging algorithm for GB-AA-SAR, which can achieve high image reconstruction precision through numerical solutions in the wavenumber domain. The proposed algorithm can be applied in wide azimuth view angle scenarios, and the problem of azimuth mismatch caused by distance approximation in arc geometric efficient omega-k imaging can be solved successfully. Finally, we analyze the two-dimensional (2D) spatial resolution of GB-AA-SAR, and verify the effectiveness of the proposed algorithm through numerical simulation experiments. Full article
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<p>Ground-based arc array synthetic aperture radar (GB-AA-SAR) system: (<b>a</b>) Arc antenna array; (<b>b</b>) Configuration of GB-AA-SAR system.</p>
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<p>Observation geometric model of GB-AA-SAR.</p>
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<p>Geometric model for azimuth sampling analysis.</p>
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<p>Wavenumber domain imaging processing flow chart.</p>
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<p>Azimuth angle resolution of GB-AA-SAR: (<b>a</b>) Azimuth angle resolution for different arc radii; (<b>b</b>) Azimuth angle resolution for different antenna beamwidths.</p>
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<p>Two-dimensional (2D) imaging results of a point target and their respective azimuth profiles with different algorithms: (<b>a</b>,<b>d</b>) the proposed algorithm; (<b>b</b>,<b>e</b>) back projection algorithm; (<b>c</b>,<b>f</b>) omega-k algorithm.</p>
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<p>Distribution of the point targets.</p>
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<p>Imaging results of the targets with different distances and different azimuth angles. (<b>a</b>) Point targets at (600 m, 0°); (<b>b</b>) Point targets at (10 m, 0°); (<b>c</b>) Point targets at (600 m, 30°); (<b>d</b>) Point targets at (600 m, 45°).</p>
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6832 KiB  
Article
Integrity Testing of Pile Cover Using Distributed Fibre Optic Sensing
by Yi Rui, Cedric Kechavarzi, Frank O’Leary, Chris Barker, Duncan Nicholson and Kenichi Soga
Sensors 2017, 17(12), 2949; https://doi.org/10.3390/s17122949 - 19 Dec 2017
Cited by 41 | Viewed by 9340
Abstract
The integrity of cast-in-place foundation piles is a major concern in geotechnical engineering. In this study, distributed fibre optic sensing (DFOS) cables, embedded in a pile during concreting, are used to measure the changes in concrete curing temperature profile to infer concrete cover [...] Read more.
The integrity of cast-in-place foundation piles is a major concern in geotechnical engineering. In this study, distributed fibre optic sensing (DFOS) cables, embedded in a pile during concreting, are used to measure the changes in concrete curing temperature profile to infer concrete cover thickness through modelling of heat transfer processes within the concrete and adjacent ground. A field trial was conducted at a high-rise building construction site in London during the construction of a 51 m long test pile. DFOS cables were attached to the reinforcement cage of the pile at four different axial directions to obtain distributed temperature change data along the pile. The monitoring data shows a clear development of concrete hydration temperature with time and the pattern of the change varies due to small changes in concrete cover. A one-dimensional axisymmetric heat transfer finite element (FE) model is used to estimate the pile geometry with depth by back analysing the DFOS data. The results show that the estimated pile diameter varies with depth in the range between 1.40 and 1.56 m for this instrumented pile. This average pile diameter profile compares well to that obtained with the standard Thermal Integrity Profiling (TIP) method. A parametric study is conducted to examine the sensitivity of concrete and soil thermal properties on estimating the pile geometry. Full article
(This article belongs to the Special Issue Sensors and Sensor Networks for Structural Health Monitoring)
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<p>Brillouin gain spectrum and frequency shift caused by a change in temperature.</p>
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<p>Geometry and instrumentation of the test pile: (<b>a</b>) plan view, (<b>b</b>) cross-section.</p>
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<p>Construction details of the temperature cable (with permission from Kechavarzi et al. [<a href="#B28-sensors-17-02949" class="html-bibr">28</a>]).</p>
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<p>Lowering of reinforcement cages and installation of fibre optic cables.</p>
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<p>Longitudinal temperature profiles at (<b>a</b>) 4 h (<b>b</b>) 14 h (<b>c</b>) 1.4 days (<b>d</b>) 14 days.</p>
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<p>DFOS temperature development over time at two depths: (<b>a</b>) 15 m; (<b>b</b>) 35 m.</p>
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<p>Conceptual relationship between DFOS temperature and pile radius on a typical cross section.</p>
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<p>1D axisymmetric heat transfer finite element model.</p>
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<p>Calibration of finite element model.</p>
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<p>Temperature changes measured with Cable T-1 at different depth: (<b>a</b>) 10 m; (<b>b</b>) 20 m; (<b>c</b>) 30 m; (<b>d</b>) 40 m.</p>
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<p>Predicted pile radius in four different axial directions along pile length.</p>
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<p>Predicted pile shape.</p>
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<p>Pile diameter obtained by different test methods.</p>
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<p>Changes in pile diameter with variation of: (<b>a</b>) thermal conductivity of soil; (<b>b</b>) thermal conductivity of concrete pile.</p>
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4401 KiB  
Article
Blind Compensation of I/Q Impairments in Wireless Transceivers
by Mohsin Aziz, Fadhel M. Ghannouchi and Mohamed Helaoui
Sensors 2017, 17(12), 2948; https://doi.org/10.3390/s17122948 - 19 Dec 2017
Cited by 13 | Viewed by 4963
Abstract
The majority of techniques that deal with the mitigation of in-phase and quadrature-phase (I/Q) imbalance at the transmitter (pre-compensation) require long training sequences, reducing the throughput of the system. These techniques also require a feedback path, which adds more [...] Read more.
The majority of techniques that deal with the mitigation of in-phase and quadrature-phase (I/Q) imbalance at the transmitter (pre-compensation) require long training sequences, reducing the throughput of the system. These techniques also require a feedback path, which adds more complexity and cost to the transmitter architecture. Blind estimation techniques are attractive for avoiding the use of long training sequences. In this paper, we propose a blind frequency-independent I/Q imbalance compensation method based on the maximum likelihood (ML) estimation of the imbalance parameters of a transceiver. A closed-form joint probability density function (PDF) for the imbalanced I and Q signals is derived and validated. ML estimation is then used to estimate the imbalance parameters using the derived joint PDF of the output I and Q signals. Various figures of merit have been used to evaluate the efficacy of the proposed approach using extensive computer simulations and measurements. Additionally, the bit error rate curves show the effectiveness of the proposed method in the presence of the wireless channel and Additive White Gaussian Noise. Real-world experimental results show an image rejection of greater than 30 dB as compared to the uncompensated system. This method has also been found to be robust in the presence of practical system impairments, such as time and phase delay mismatches. Full article
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<p>Block diagram of transceiver system with modulator’s and demodulator’s imperfections.</p>
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<p>Contour plot for the PDF of Wideband Code Division Multiple Access (WCDMA) 1111 (<b>a</b>) input and (<b>b</b>) imbalanced output <span class="html-italic">I</span>/<span class="html-italic">Q</span> signals using the Kernel Density Estimation (KDE) method. A deviation from the input signal’s PDF can be seen due to <span class="html-italic">I</span>/<span class="html-italic">Q</span> imbalance.</p>
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<p>Contour plot for the PDF of imbalanced (<span class="html-italic">g</span> = 0.92 dB, <span class="html-italic">g</span>′ = 0.5 dB and <span class="html-italic">θ</span> = −6 degrees, <span class="html-italic">θ</span>′ = −2 degrees) signal using the KDE based method and the derived expression (17) for noiseless case. The derived PDF follows the PDF of the imbalanced signal obtained using the KDE method.</p>
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<p>Kullback-Leibler (KL) divergence between KDE based estimate of <math display="inline"> <semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>y</mi> </mstyle> <mi>R</mi> </msub> </mrow> </semantics> </math> and derived PDF for WCDMA 1111 signal. The dotted line shows the KL divergence for the noiseless case while the solid line includes the effect of noise.</p>
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<p>Normalized mean squared error (NMSE) vs. SNR using the proposed mitigation approach for <span class="html-italic">g</span> = <span class="html-italic">g</span>′ = 0.92 dB and <span class="html-italic">θ</span> = <span class="html-italic">θ</span>′ = −6 degrees.</p>
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<p>Mirror frequency Imaging (MFI) due to <span class="html-italic">I</span>/<span class="html-italic">Q</span> imbalance. (<b>a</b>) MFI for symmetric 1111 signal for which the power in the image band cannot be seen using PSD; (<b>b</b>) MFI for asymmetric signals and the power in the image band is visible.</p>
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<p>Power spectral density (PSD) of WCDMA 1101 (S1, S2 and S3) signals before and after correction, using the proposed method for <span class="html-italic">g</span> = 0.92 dB, <span class="html-italic">g</span>′ = 0.5 dB and <span class="html-italic">θ</span> = −6 degrees, <span class="html-italic">θ</span>′ = −2 degrees under 20 dB SNR.</p>
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<p>Bit error rate (BER) vs. SNR for the proposed model for 16 QAM orthogonal frequency division multiplexing (OFDM) signal with 1024 subcarriers for <span class="html-italic">g</span> = <span class="html-italic">g</span>′ = 0.92 dB and <span class="html-italic">θ</span> = <span class="html-italic">θ</span>′ = −6 degrees.</p>
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<p>Contour plot for the PDF of 16-QAM OFDM <span class="html-italic">I</span>/<span class="html-italic">Q</span> signals using the KDE method under multipath channel and 20 dB SNR. The colored contours show the variations in PDF due to Tx and Rx <span class="html-italic">I</span>/<span class="html-italic">Q</span> imbalance only under Additive White Gaussian Noise (AWGN). The black contours show the PDF of the output signal under the influence of <span class="html-italic">I</span>/<span class="html-italic">Q</span> imbalance and channel after gain and phase synchronization.</p>
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<p>BER vs. SNR for the proposed model for 16 QAM OFDM signal with 1024 subcarriers for <span class="html-italic">g</span> = <span class="html-italic">g</span>′ = 0.5 dB and <span class="html-italic">θ</span> = <span class="html-italic">θ</span>′ = −2 degrees.</p>
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<p>Constellation of 16 QAM signals under 20 dB SNR (<b>a</b>) transmitted signal (<b>b</b>) received signal and (<b>c</b>) corrected signal for <span class="html-italic">g</span> = <span class="html-italic">g</span>′ = 0.92 dB and <span class="html-italic">θ</span> = <span class="html-italic">θ</span>′ = −6 degrees; (<b>d</b>) Received and (<b>e</b>) corrected signal for <span class="html-italic">g</span> = 0.92 dB, <span class="html-italic">g</span>′ = 0.5 dB and <span class="html-italic">θ</span> = −6 degrees, <span class="html-italic">θ</span>′ = −2 degrees.</p>
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<p>Measurement setup for evaluating the performance of proposed methodology.</p>
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<p>Measured output and post compensated signal.</p>
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<p>Constellation of 16 QAM (<b>a</b>) transmitted and received signals and (<b>b</b>) transmitted and corrected signals.</p>
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5647 KiB  
Review
Porous TiO2-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis
by Vardan Galstyan
Sensors 2017, 17(12), 2947; https://doi.org/10.3390/s17122947 - 19 Dec 2017
Cited by 64 | Viewed by 9869
Abstract
Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO2, with its excellent physical and [...] Read more.
Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO2, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO2-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO2 may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material. Full article
(This article belongs to the Collection Gas Sensors)
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<p>The framework of the proposed CCS.</p>
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<p>The schematic representation of the proposed CCS to improve the process safety and the quality of life. (<b>a</b>) represents an area of a smart city (<b>b</b>) based on the cyber home, cyber industry, cyber mobile and cyber society domains. The industrial sector, the hydrogen fuel stations, the streets, the hydrogen powered cars and the public buildings are equipped with the chemical gas sensors for the outdoor and indoor monitoring.</p>
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<p>CCS applications coupling the cyber and object domain for the security of public transit and transport services. Trucks, buses, trains and train stations, airports, planes, luggage stores and luggage check instruments are all equipped with the chemical sensors.</p>
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<p>(<b>a</b>–<b>g</b>) the design and architecture of medical CCS for the breath analysis. (<b>h</b>) A smart toilet for the analysis of urine.</p>
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<p>The schematics of the ALD system.</p>
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<p>Schematic of an electrochemical anodization system.</p>
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<p>(<b>a</b>) 2D AFM topography of the alumina substrate. (<b>b</b>) Surface morphology of Nb-TiO<sub>2</sub> nanotubes, (<b>c</b>) magnification of (<b>b</b>), (<b>d</b>) cross-sectional view of the anodized layer (<b>e</b>) EDX spectrum confirming the presence of 4.5 ± 0.5 wt % of Nb with respect to Ti, (<b>f</b>) the bottom-view of the tubular layer. (<b>g</b>,<b>h</b>) AFM images of the single nanotubes: (<b>g</b>) 3D topography and (<b>h</b>) the associated phase signal. Reproduced with permission from [<a href="#B20-sensors-17-02947" class="html-bibr">20</a>]. Copyright (2014) Elsevier B.V.</p>
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<p>Schematic illustration of a hydrothermal growth system.</p>
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<p>Scanning electron microscopy (SEM) of titania nanotube arrays grown on un-activated carbon fibers (TNTUCFs) with different TiO<sub>2</sub> loadings (TNTUCF-5, TNTUCF-7.5, TNTUCF-10, TNTUCF-12.5, TNTUCF-15) and TiO<sub>2</sub>-coated UCF (TUCF). Reproduced with permission from [<a href="#B69-sensors-17-02947" class="html-bibr">69</a>].</p>
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<p>(<b>a</b>) The structural and the band model of oxide material showing the role of pores contact regions in determining the conductance over the TiO<sub>2</sub> due to the adsorption/desorption process of oxidizing and reducing gases. (<b>b</b>) The model illustrating the band bending in the metal oxide material due to the ionosorption of oxygen on the material surface. E<sub>C</sub>, E<sub>V</sub>, and E<sub>F</sub> denote the energy of the conduction band, valence band, and the Fermi level, respectively.</p>
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<p>The design of a chemiresistive transducer. (<b>a</b>) The top-view of transducer: The porous structure and the interdigitated electrodes obtained on the porous array to read-out the signal. (<b>b</b>) The bottom-view of transducer with the heater deposited on the backside of substrate.</p>
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<p>Comparison of the responses of pristine and Pd-functionalized TiO<sub>2</sub> nanotubes to different gases. Reproduced with permission from [<a href="#B115-sensors-17-02947" class="html-bibr">115</a>].</p>
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<p>The response of niobium-containing TiO<sub>2</sub> nanotubes towards 500 ppm of H<sub>2</sub>, 500 ppm of CO, 50 ppm of acetone and 50 ppm of ethanol at different operating temperatures (100, 200, 300, 400, 500 °C) with 40%RH @20 °C. Reproduced with permission from [<a href="#B20-sensors-17-02947" class="html-bibr">20</a>]. Copyright (2014) Elsevier B.V.</p>
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<p>Dynamical response of Nb-doped TiO<sub>2</sub> nanotubes towards 100 ppm of ethanol, carbon monoxide and acetone at a working temperature of 400 °C and 40%RH@20 °C for different internal tube diameters. Reproduced with permission from [<a href="#B21-sensors-17-02947" class="html-bibr">21</a>]. Copyright (2015) Elsevier Inc.</p>
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980 KiB  
Article
An Enhanced Three-Factor User Authentication Scheme Using Elliptic Curve Cryptosystem for Wireless Sensor Networks
by Chenyu Wang, Guoai Xu and Jing Sun
Sensors 2017, 17(12), 2946; https://doi.org/10.3390/s17122946 - 19 Dec 2017
Cited by 65 | Viewed by 6148
Abstract
As an essential part of Internet of Things (IoT), wireless sensor networks (WSNs) have touched every aspect of our lives, such as health monitoring, environmental monitoring and traffic monitoring. However, due to its openness, wireless sensor networks are vulnerable to various security threats. [...] Read more.
As an essential part of Internet of Things (IoT), wireless sensor networks (WSNs) have touched every aspect of our lives, such as health monitoring, environmental monitoring and traffic monitoring. However, due to its openness, wireless sensor networks are vulnerable to various security threats. User authentication, as the first fundamental step to protect systems from various attacks, has attracted much attention. Numerous user authentication protocols armed with formal proof are springing up. Recently, two biometric-based schemes were proposed with confidence to be resistant to the known attacks including offline dictionary attack, impersonation attack and so on. However, after a scrutinization of these two schemes, we found them not secure enough as claimed, and then demonstrated that these schemes suffer from various attacks, such as offline dictionary attack, impersonation attack, no user anonymity, no forward secrecy, etc. Furthermore, we proposed an enhanced scheme to overcome the identified weaknesses, and proved its security via Burrows–Abadi–Needham (BAN) logic and the heuristic analysis. Finally, we compared our scheme with other related schemes, and the results showed the superiority of our scheme. Full article
(This article belongs to the Special Issue Security, Trust and Privacy for Sensor Networks)
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<p>WSNs system architecture.</p>
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<p>Proposed scheme.</p>
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2275 KiB  
Article
Pre-Scheduled and Self Organized Sleep-Scheduling Algorithms for Efficient K-Coverage in Wireless Sensor Networks
by Prasan Kumar Sahoo, Hiren Kumar Thakkar and I-Shyan Hwang
Sensors 2017, 17(12), 2945; https://doi.org/10.3390/s17122945 - 19 Dec 2017
Cited by 11 | Viewed by 3610
Abstract
The K-coverage configuration that guarantees coverage of each location by at least K sensors is highly popular and is extensively used to monitor diversified applications in wireless sensor networks. Long network lifetime and high detection quality are the essentials of such K [...] Read more.
The K-coverage configuration that guarantees coverage of each location by at least K sensors is highly popular and is extensively used to monitor diversified applications in wireless sensor networks. Long network lifetime and high detection quality are the essentials of such K-covered sleep-scheduling algorithms. However, the existing sleep-scheduling algorithms either cause high cost or cannot preserve the detection quality effectively. In this paper, the Pre-Scheduling-based K-coverage Group Scheduling (PSKGS) and Self-Organized K-coverage Scheduling (SKS) algorithms are proposed to settle the problems in the existing sleep-scheduling algorithms. Simulation results show that our pre-scheduled-based KGS approach enhances the detection quality and network lifetime, whereas the self-organized-based SKS algorithm minimizes the computation and communication cost of the nodes and thereby is energy efficient. Besides, SKS outperforms PSKGS in terms of network lifetime and detection quality as it is self-organized. Full article
(This article belongs to the Section Sensor Networks)
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<p>An illustration of the pre-scheduled grid-based algorithm. (<b>a</b>) The division of sensing range of sensor <span class="html-italic">s</span> into multiple Grid Points (GPs), in which grid point <span class="html-italic">n</span> is also covered by sensors <span class="html-italic">A</span>, <span class="html-italic">B</span> and <span class="html-italic">C</span>. (<b>b</b>) Sensors <span class="html-italic">A</span>, <span class="html-italic">B</span>, <span class="html-italic">C</span> and <span class="html-italic">S</span> establish the duty cycle for grid point <span class="html-italic">n</span>.</p>
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<p>The duty cycle of sensor <span class="html-italic">S</span> is integrated from the scheduling of all GPs located within the sensing range of sensor <span class="html-italic">S</span>.</p>
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<p>An illustration of PSKGS sleep scheduling for <span class="html-italic">K</span> = 1 by group of sensors. (<b>a</b>) <span class="html-italic">A</span>, <span class="html-italic">B</span>, and <span class="html-italic">C</span>; (<b>b</b>) <span class="html-italic">D</span>, <span class="html-italic">E</span>, and <span class="html-italic">F</span>; (<b>c</b>) <span class="html-italic">H</span>, <span class="html-italic">I</span>, and <span class="html-italic">G</span>.</p>
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<p>Example of the formation of groups of node A. (<b>a</b>) Pivot node selection; (<b>b</b>) First reference node selection; (<b>c</b>) Subsequent reference node selection.</p>
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<p>The working schedule of a sensor in WSN.</p>
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<p>A sleep scheduling in Pre-Scheduling-based <span class="html-italic">K</span>-coverage Group Scheduling (PSKGS) for <span class="html-italic">K</span> = 3, (<b>a</b>) A theoretical representation; (<b>b</b>) an example representation.</p>
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<p>(<b>a</b>) The eligibility of <span class="html-italic">s</span> is determined by tracing point <span class="html-italic">p</span>, which is intersected by <math display="inline"> <semantics> <msub> <mi>R</mi> <mrow> <mi>N</mi> <mi>B</mi> <mi>s</mi> </mrow> </msub> </semantics> </math>, <span class="html-italic">A</span> and <span class="html-italic">B</span>. (<b>b</b>) The intersection point of <math display="inline"> <semantics> <msub> <mi>R</mi> <mrow> <mi>N</mi> <mi>B</mi> <mi>s</mi> </mrow> </msub> </semantics> </math> is out of the monitored area.</p>
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<p>(<b>a</b>) For <span class="html-italic">s</span>, the area covered by its <math display="inline"> <semantics> <msub> <mi>R</mi> <mrow> <mi>N</mi> <mi>B</mi> <mi>s</mi> </mrow> </msub> </semantics> </math> (i.e., <span class="html-italic">A</span>) is larger than that of its <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>−</mo> <mn>2</mn> <msub> <mi>R</mi> <mrow> <mi>N</mi> <mi>B</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> (i.e., <span class="html-italic">a</span>); (<b>b</b>) The coverage degree of <span class="html-italic">s</span> is 1, if <span class="html-italic">s</span> has only <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>−</mo> <mn>2</mn> <msub> <mi>R</mi> <mrow> <mi>N</mi> <mi>B</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math>.</p>
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<p>(<b>a</b>) The Candidate Intersection Point (<span class="html-italic">CIP</span>) denoted as <span class="html-italic">i</span> of sensor <span class="html-italic">s</span> is covered by <span class="html-italic">candidate <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>−</mo> <mn>2</mn> <msub> <mi>R</mi> <mrow> <mi>N</mi> <mi>B</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> a</span> and <span class="html-italic">b</span>. (<b>b</b>) The points <span class="html-italic">m</span> and <span class="html-italic">n</span> surrounds the lower coverage regions of <span class="html-italic">s</span>.</p>
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<p>The performance comparison of the intersection-based [<a href="#B4-sensors-17-02945" class="html-bibr">4</a>], grid-based [<a href="#B5-sensors-17-02945" class="html-bibr">5</a>], Coverage Contribution Area (CCA) [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and proposed PSKGS with respect to the average coverage degree for <span class="html-italic">K</span> = 1.</p>
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<p>The performance comparison of the intersection-based [<a href="#B4-sensors-17-02945" class="html-bibr">4</a>], grid-based [<a href="#B5-sensors-17-02945" class="html-bibr">5</a>], CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and proposed PSKGS with respect to the computational cost for <span class="html-italic">K</span> = 3.</p>
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<p>The performance comparison of the Coverage Configuration Protocol (CCP) [<a href="#B6-sensors-17-02945" class="html-bibr">6</a>], the <span class="html-italic">K</span>-Perimeter-Covered (KPC) algorithm [<a href="#B7-sensors-17-02945" class="html-bibr">7</a>], CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and the proposed Self-Organized <span class="html-italic">K</span>-coverage Scheduling (SKS) under different <span class="html-italic">K</span>-coverage degrees with respect to the computational cost.</p>
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<p>The probability of node false positive of 300 sensors under different detection threshold <math display="inline"> <semantics> <mi>θ</mi> </semantics> </math>.</p>
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<p>The # of Active Sensors (ASs) in CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>], PSKGS and SKS in a round.</p>
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<p>The Average Coverage Degree (ACD) of SKS, PSKGS and CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] in a round.</p>
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<p>Probability of system false positives of (<b>a</b>) PSKGS, CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and Centralized and Clustered <span class="html-italic">K</span>-Coverage Protocol (CCKCP) [<a href="#B32-sensors-17-02945" class="html-bibr">32</a>] and (<b>b</b>) SKS, CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and CCKCP [<a href="#B32-sensors-17-02945" class="html-bibr">32</a>], with the detection threshold <math display="inline"> <semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and the data aggregation ratio <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics> </math>.</p>
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<p>Probability of system false negatives of (<b>a</b>) PSKGS, CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and CCKCP [<a href="#B32-sensors-17-02945" class="html-bibr">32</a>] and (<b>b</b>) SKS, CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and CCKCP [<a href="#B32-sensors-17-02945" class="html-bibr">32</a>], with the detection threshold <math display="inline"> <semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and the data aggregation ratio <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics> </math>.</p>
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<p>The network lifetime comparison of (<b>a</b>) PSKGS, CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and CCKCP [<a href="#B32-sensors-17-02945" class="html-bibr">32</a>] and (<b>b</b>) SKS, CCA [<a href="#B31-sensors-17-02945" class="html-bibr">31</a>] and CCKCP [<a href="#B32-sensors-17-02945" class="html-bibr">32</a>].</p>
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5020 KiB  
Article
Sensitivity Analysis of Different Shapes of a Plastic Optical Fiber-Based Immunosensor for Escherichia coli: Simulation and Experimental Results
by Domingos M. C. Rodrigues, Rafaela N. Lopes, Marcos A. R. Franco, Marcelo M. Werneck and Regina C. S. B. Allil
Sensors 2017, 17(12), 2944; https://doi.org/10.3390/s17122944 - 19 Dec 2017
Cited by 21 | Viewed by 5101
Abstract
Conventional pathogen detection methods require trained personnel, specialized laboratories and can take days to provide a result. Thus, portable biosensors with rapid detection response are vital for the current needs for in-loco quality assays. In this work the authors analyze the characteristics of [...] Read more.
Conventional pathogen detection methods require trained personnel, specialized laboratories and can take days to provide a result. Thus, portable biosensors with rapid detection response are vital for the current needs for in-loco quality assays. In this work the authors analyze the characteristics of an immunosensor based on the evanescent field in plastic optical fibers with macro curvature by comparing experimental with simulated results. The work studies different shapes of evanescent-wave based fiber optic sensors, adopting a computational modeling to evaluate the probes with the best sensitivity. The simulation showed that for a U-Shaped sensor, the best results can be achieved with a sensor of 980 µm diameter by 5.0 mm in curvature for refractive index sensing, whereas the meander-shaped sensor with 250 μm in diameter with radius of curvature of 1.5 mm, showed better sensitivity for either bacteria and refractive index (RI) sensing. Then, an immunosensor was developed, firstly to measure refractive index and after that, functionalized to detect Escherichia coli. Based on the results with the simulation, we conducted studies with a real sensor for RI measurements and for Escherichia coli detection aiming to establish the best diameter and curvature radius in order to obtain an optimized sensor. On comparing the experimental results with predictions made from the modelling, good agreements were obtained. The simulations performed allowed the evaluation of new geometric configurations of biosensors that can be easily constructed and that promise improved sensitivity. Full article
(This article belongs to the Special Issue Nanostructured Hybrid Materials Based Opto-Electronics Sensors)
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Figure 1
<p>(<b>A</b>) Sensor head with two sensors, the reference and the functionalized with antibody. (<b>B</b>) Schematic diagram of the optoelectronic setup.</p>
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<p>(<b>A</b>) Simulation model of a fiber (in yellow) implemented in BeamPROP with a length of approximately 15 mm and radius of curvature of 5.0 mm and surrounding liquids of different refractive indices (red layer). (<b>B</b>) Light power distribution inside the fiber. The x-axis of the graph on the right represents the total light power in arbitrary units at each section along the z-axis of the fiber.</p>
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<p>Simulation model of a fiber implemented in BeamPROP in order to simulate a gradual increase of bacteria density (blue rectangles with RI = 1.39) adhered to the fiber surface. The yellow area is the fiber core and the red area the surrounding water.</p>
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<p>Results of the modelling of U-Shaped sensor for the 980 μm diameter POF. The output power varies as a function of the refractive index for U-shaped sensors with radii of curvature between 2 mm and 10 mm.</p>
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<p>Behavior of U-Shaped sensor with φ = 980 µm for radius of 4.0 mm and 5.0 mm and the sensor with φ = 250 µm for radius of 1.5 mm and 4.0 mm under different RI. The first term of the regression equations is the sensitivity.</p>
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<p>(<b>A</b>) Experimental results and simulation of U-Shaped sensors with 980 μm in diameter and radius of 4.0 and 5.0 mm. (<b>B</b>) Experimental results and simulation of sensors with 250 μm in diameter and radius of 1.5 and 4.0 mm. The measurement uncertainty is 0.005 RIU (refractive index unit).</p>
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<p>Simulations for φ = 980 µm with R = 5.0 mm and φ = 250 µm with R = 1.5 mm of U-Shaped sensors for a crescent bacteria density along the fiber surface.</p>
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<p>Experimental response of three 980 μm diameter with 5.0 mm curvature radius of U-Shaped sensors for a 10<sup>8</sup> CFU/mL suspension of E. coli in saline solution.</p>
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<p>Four pictures with different magnification taken from the sensors under the electron scanning microscope. In (<b>A</b>) the picture shows the reference U-Shaped sensor without functionalization under a scale of 500 μm. In (<b>B</b>) the U-Shaped sensor functionalized in a suspension of 10<sup>8</sup> CFU/mL of <span class="html-italic">Escherichia coli</span> with a scale of 300 μm. The small dot are the adhered bacteria. (<b>C</b>) Bacteria seen at the sensor surface under a scale of 50 μm. (<b>D</b>) Under a scale of 20 μm, it is possible to notice individual bacteria distributed along the sensor surface. Each bacterium measures about 2 µm in length by 1 µm in diameter.</p>
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<p>Comparison of the experimental results of the 980 μm-5.0 mm of U-Shaped sensor for <span class="html-italic">E. coli</span> with simulated results.</p>
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<p>Coil-shaped and a meander-shaped probe models used for simulation.</p>
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<p>Comparing the simulated sensitivity between U-Shaped, Coil- and Meander-shaped with φ = 980 µm, R = 5.0 mm (<b>A</b>) and φ = 250 µm, R = 1.5 mm sensor (<b>B</b>) for RI sensing.</p>
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<p>Comparing the sensitivity between U-Shaped, Coil and Meander-shaped with φ = 980 µm, R = 5.0 mm (<b>A</b>) and φ = 250 µm, R = 1.5 mm sensor (<b>B</b>) for bacteria sensing.</p>
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3411 KiB  
Article
Identification of Tequila with an Array of ZnO Thin Films: A Simple and Cost-Effective Method
by Pedro Estanislao Acuña-Avila, Raúl Calavia, Enrique Vigueras-Santiago and Eduard Llobet
Sensors 2017, 17(12), 2943; https://doi.org/10.3390/s17122943 - 19 Dec 2017
Cited by 9 | Viewed by 4729
Abstract
An array of ZnO thin film sensors was obtained by thermal oxidation of physical vapor deposited thin Zn films. Different conditions of the thermal treatment (duration and temperature) were applied in view of obtaining ZnO sensors with different gas sensing properties. Films having [...] Read more.
An array of ZnO thin film sensors was obtained by thermal oxidation of physical vapor deposited thin Zn films. Different conditions of the thermal treatment (duration and temperature) were applied in view of obtaining ZnO sensors with different gas sensing properties. Films having undergone a long thermal treatment exhibited high responses to low ethanol concentrations, while short thermal treatments generally led to sensors with high ethanol sensitivity. The sensor array was used to distinguish among Tequilas and Agave liquor. Linear discriminant analysis and the multilayer perceptron neural network reached 100% and 86.3% success rates in the discrimination between real Tequila and Agave liquor and in the identification of Tequila brands, respectively. These results are promising for the development of an inexpensive tool offering low complexity and cost of analysis for detecting fraud in spirits. Full article
(This article belongs to the Section Chemical Sensors)
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<p>Scheme of the experimental set-up employed to study the response of the sensors towards different ethanol concentrations.</p>
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<p>Low magnification SEM micrographs of the different thin ZnO films supported on Al<sub>2</sub>O<sub>3</sub> substrates (big grains correspond to alumina). EDX results are indicated as insets to each micrograph. Panels (<b>a</b>–<b>e</b>) correspond to films obtained employing the five different thermal treatments implemented. Refer to <a href="#sensors-17-02943-t001" class="html-table">Table 1</a> for full details.</p>
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<p>Low magnification SEM micrographs of the different thin ZnO films supported on Al<sub>2</sub>O<sub>3</sub> substrates (big grains correspond to alumina). EDX results are indicated as insets to each micrograph. Panels (<b>a</b>–<b>e</b>) correspond to films obtained employing the five different thermal treatments implemented. Refer to <a href="#sensors-17-02943-t001" class="html-table">Table 1</a> for full details.</p>
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<p>Higher magnification SEM micrographs of the different thin ZnO films supported on Al<sub>2</sub>O<sub>3</sub> substrates. EDX results are indicated as insets to each micrograph. Panels (<b>a</b>–<b>e</b>) correspond to films obtained employing the five different thermal treatments implemented. Refer to <a href="#sensors-17-02943-t001" class="html-table">Table 1</a> for full details.</p>
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<p>Higher magnification SEM micrographs of the different thin ZnO films supported on Al<sub>2</sub>O<sub>3</sub> substrates. EDX results are indicated as insets to each micrograph. Panels (<b>a</b>–<b>e</b>) correspond to films obtained employing the five different thermal treatments implemented. Refer to <a href="#sensors-17-02943-t001" class="html-table">Table 1</a> for full details.</p>
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<p>XRD analysis for sample 600HS1. The upper panel shows that the high-intensity peaks correspond to the electrodes and alumina substrate of the sensor transducer. The lower panel is an enlargement to better show peaks that correspond to hexagonal zinc oxide.</p>
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<p>Successive response and recovery of the ZnO thin films to decreasing concentrations (20, 10 and 5 ppm) of ethanol diluted in dry air. All sensors were operated at 200 °C.</p>
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<p>Calibration curves for ethanol and power law curves fitting for the different ZnO thin film sensors operated at 200 °C.</p>
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<p>Average responses of the ZnO sensors for the different Tequila and Agave liquor samples. Sensor operating temperature was set to 200 °C. Beverage samples are labeled according to <a href="#sec2-sensors-17-02943" class="html-sec">Section 2</a>.</p>
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<p>Cross-validation results of a linear discriminant analysis performed on the Tequila and Agave liquor (false Tequila) database. The first two discriminant factors account for about 97% of data variance. The orange line has been added to better show that the second discriminant factor is essential for correctly discriminating real Tequilas from Agave liquor.</p>
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3137 KiB  
Article
Dual-Color Fluorescence Imaging of EpCAM and EGFR in Breast Cancer Cells with a Bull’s Eye-Type Plasmonic Chip
by Shota Izumi, Shohei Yamamura, Naoko Hayashi, Mana Toma and Keiko Tawa
Sensors 2017, 17(12), 2942; https://doi.org/10.3390/s17122942 - 19 Dec 2017
Cited by 12 | Viewed by 5537
Abstract
Surface plasmon field-enhanced fluorescence microscopic observation of a live breast cancer cell was performed with a plasmonic chip. Two cell lines, MDA-MB-231 and Michigan Cancer Foundation-7 (MCF-7), were selected as breast cancer cells, with two kinds of membrane protein, epithelial cell adhesion molecule [...] Read more.
Surface plasmon field-enhanced fluorescence microscopic observation of a live breast cancer cell was performed with a plasmonic chip. Two cell lines, MDA-MB-231 and Michigan Cancer Foundation-7 (MCF-7), were selected as breast cancer cells, with two kinds of membrane protein, epithelial cell adhesion molecule (EpCAM) and epidermal growth factor receptor (EGFR), observed in both cells. The membrane proteins are surface markers used to differentiate and classify breast cancer cells. EGFR and EpCAM were detected with Alexa Fluor® 488-labeled anti-EGFR antibody (488-EGFR) and allophycocyanin (APC)-labeled anti-EpCAM antibody (APC-EpCAM), respectively. In MDA-MB231 cells, three-fold plus or minus one and seven-fold plus or minus two brighter fluorescence of 488-EGFR were observed on the 480-nm pitch and the 400-nm pitch compared with that on a glass slide. Results show the 400-nm pitch is useful. Dual-color fluorescence of 488-EGFR and APC-EpCAM in MDA-MB231 was clearly observed with seven-fold plus or minus two and nine-fold plus or minus three, respectively, on the 400-nm pitch pattern of a plasmonic chip. Therefore, the 400-nm pitch contributed to the dual-color fluorescence enhancement for these wavelengths. An optimal grating pitch of a plasmonic chip improved a fluorescence image of membrane proteins with the help of the surface plasmon-enhanced field. Full article
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<p>(<b>a</b>) Bright-field microscope image of arrangement for two types of 100 μm φ-bull’s eye patterns with 400 nm pitch and 480 nm pitch grating. Bar corresponds to 100 μm. Atomic force microscopy (AFM) images of a periodic structure on a bull’s eye-pattern and their contour of the cross-section images; (<b>b</b>) with 480 nm pitch, and (<b>c</b>) with 400 nm pitch.</p>
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<p>Bright-field images (<b>a</b>–<b>c</b>) and fluorescence images of 488-epidermal growth factor receptor (EGFR) (<b>d</b>–<b>f</b>) in MDA-MB-231 cells. The left, center, and right columns show images on the glass slide, and the 480-nm pitch and 400 nm pitch bull’s eye-plasmonic chips, respectively. The 488-EGFR images shown in (<b>d</b>–<b>f</b>) were adjusted to the same scale between minimum to maximum brightness corresponding to 3000 counts. Bar corresponds to 10 µm.</p>
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<p>Bright-field images (<b>a</b>–<b>c</b>) and fluorescence images of 488-EGFR (<b>d</b>–<b>f</b>) in MCF-7 cells. The left, center, and right columns show images on the glass slide, the 480-nm pitch, and the 400-nm pitch bull’s eye-plasmonic chip, respectively. The 488-EGFR images shown in (<b>d</b>–<b>f</b>) were adjusted to the same scale between minimum to maximum brightness corresponding to 3000 counts. Bar corresponds to 10 µm.</p>
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<p>Bright-field images (<b>a</b>–<b>c</b>) and fluorescence images of 488-EGFR (<b>d</b>–<b>f</b>) in MCF-7 cells. The left, center, and right columns show images on the glass slide, the 480-nm pitch, and the 400-nm pitch bull’s eye-plasmonic chip, respectively. The 488-EGFR images shown in (<b>d</b>–<b>f</b>) were adjusted to the same scale between minimum to maximum brightness corresponding to 3000 counts. Bar corresponds to 10 µm.</p>
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<p>Bright-field images (<b>a</b>,<b>d</b>), fluorescence images of 488-EGFR (<b>b</b>,<b>e</b>), and fluorescence images of allophycocyanin-labeled anti-epithelial cell adhesion molecule antibody (APC-EpCAM), (<b>c</b>,<b>f</b>) in MDA-MB-231 cells. The upper and lower columns show images on the glass slide and the 400-nm pitch bull’s eye-plasmonic chip, respectively. The 488-EGFR and APC-EpCAM images shown in (<b>b</b>,<b>e</b>) or (<b>c</b>,<b>f</b>) were adjusted to the same scales between minimum and maximum brightness corresponding to 3000 and 2000 counts, respectively. Bar corresponds to 10 µm.</p>
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2275 KiB  
Article
A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection
by Abdullah M. Iliyasu and Chastine Fatichah
Sensors 2017, 17(12), 2935; https://doi.org/10.3390/s17122935 - 19 Dec 2017
Cited by 35 | Viewed by 5031
Abstract
A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for [...] Read more.
A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e., global best particles) that represent a pruned down collection of seven features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: the All-features (i.e., classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e., QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in the experimental scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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<p>A single cell cervical smear image: (<b>a</b>) superficial squamous, (<b>b</b>) intermediate squamous, (<b>c</b>) columnar, (<b>d</b>) mild dysplasia, (<b>e</b>) moderate dysplasia, (<b>f</b>) severe dysplasia, (<b>g</b>) carcinoma in situ [<a href="#B2-sensors-17-02935" class="html-bibr">2</a>,<a href="#B9-sensors-17-02935" class="html-bibr">9</a>,<a href="#B10-sensors-17-02935" class="html-bibr">10</a>,<a href="#B11-sensors-17-02935" class="html-bibr">11</a>,<a href="#B12-sensors-17-02935" class="html-bibr">12</a>,<a href="#B13-sensors-17-02935" class="html-bibr">13</a>].</p>
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<p>Illustration of particle encoded as a binary string where the bit value ‘1’ denotes a selected feature and ’0’ denotes a non-selected feature.</p>
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<p>Layout of proposed quantum hybrid (Q-Fuzzy) technique to select and classify cells in smeared cervical images.</p>
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<p>Flowchart depicting process of feature selection using the proposed Q-Fuzzy approach.</p>
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<p>Sample cervical smear images and manual segmentation (ground truth) images [<a href="#B2-sensors-17-02935" class="html-bibr">2</a>,<a href="#B11-sensors-17-02935" class="html-bibr">11</a>,<a href="#B12-sensors-17-02935" class="html-bibr">12</a>,<a href="#B13-sensors-17-02935" class="html-bibr">13</a>,<a href="#B15-sensors-17-02935" class="html-bibr">15</a>,<a href="#B16-sensors-17-02935" class="html-bibr">16</a>,<a href="#B17-sensors-17-02935" class="html-bibr">17</a>].</p>
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5374 KiB  
Article
An Indoor Location-Based Control System Using Bluetooth Beacons for IoT Systems
by Jun-Ho Huh and Kyungryong Seo
Sensors 2017, 17(12), 2917; https://doi.org/10.3390/s17122917 - 19 Dec 2017
Cited by 118 | Viewed by 10485
Abstract
The indoor location-based control system estimates the indoor position of a user to provide the service he/she requires. The major elements involved in the system are the localization server, service-provision client, user application positioning technology. The localization server controls access of terminal devices [...] Read more.
The indoor location-based control system estimates the indoor position of a user to provide the service he/she requires. The major elements involved in the system are the localization server, service-provision client, user application positioning technology. The localization server controls access of terminal devices (e.g., Smart Phones and other wireless devices) to determine their locations within a specified space first and then the service-provision client initiates required services such as indoor navigation and monitoring/surveillance. The user application provides necessary data to let the server to localize the devices or allow the user to receive various services from the client. The major technological elements involved in this system are indoor space partition method, Bluetooth 4.0, RSSI (Received Signal Strength Indication) and trilateration. The system also employs the BLE communication technology when determining the position of the user in an indoor space. The position information obtained is then used to control a specific device(s). These technologies are fundamental in achieving a “Smart Living”. An indoor location-based control system that provides services by estimating user’s indoor locations has been implemented in this study (First scenario). The algorithm introduced in this study (Second scenario) is effective in extracting valid samples from the RSSI dataset but has it has some drawbacks as well. Although we used a range-average algorithm that measures the shortest distance, there are some limitations because the measurement results depend on the sample size and the sample efficiency depends on sampling speeds and environmental changes. However, the Bluetooth system can be implemented at a relatively low cost so that once the problem of precision is solved, it can be applied to various fields. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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<p>A basic unit space with beacons.</p>
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<p>Indoor space partitioning for a large space.</p>
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<p>An example of trilateration.</p>
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<p>Selecting the target points for trilateration.</p>
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<p>The COG of a Polygon.</p>
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<p>The schematic of indoor location-based control system.</p>
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<p>The operation process of the localization server.</p>
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<p>A basic screen of the camera-based monitoring program.</p>
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<p>Implementation of the screen settings for camera-based monitoring program.</p>
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<p>Execution of user application (Scenario 1).</p>
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<p>The coordinate system and location beacons deployment plot of the testing space.</p>
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<p>Actual deployment status of location beacons in the testing space.</p>
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<p>Bluetooth module (HM-10) and location beacon experiment tool (Using Scenario 1).</p>
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<p>Distribution graph for the estimated locations.</p>
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<p>TI CC2540 module (Using Scenario 2).</p>
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<p>Position measuring application (Scenario 2). (<b>a</b>) User Interface (1); (<b>b</b>) User Interface (1).</p>
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<p>Trapezoid integration.</p>
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<p>A method of measuring position of equipment.</p>
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<p>An Android application that measures distance to a Beacon (Scenario 2).</p>
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<p>Signal changes after using a smooth filter.</p>
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<p>A concept diagram of the indoor positioning system application.</p>
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5145 KiB  
Article
Microstrip Patch Sensor for Salinity Determination
by Kibae Lee, Arshad Hassan, Chong Hyun Lee and Jinho Bae
Sensors 2017, 17(12), 2941; https://doi.org/10.3390/s17122941 - 18 Dec 2017
Cited by 29 | Viewed by 4989
Abstract
In this paper, a compact microstrip feed inset patch sensor is proposed for measuring the salinities in seawater. The working principle of the proposed sensor depends on the fact that different salinities in liquid have different relative permittivities and cause different resonance frequencies. [...] Read more.
In this paper, a compact microstrip feed inset patch sensor is proposed for measuring the salinities in seawater. The working principle of the proposed sensor depends on the fact that different salinities in liquid have different relative permittivities and cause different resonance frequencies. The proposed sensor can obtain better sensitivity to salinity changes than common sensors using conductivity change, since the relative permittivity change to salinity is 2.5 times more sensitive than the conductivity change. The patch and ground plane of the proposed sensor are fabricated by conductive copper spray coating on the masks made by 3D printer. The fabricated patch and the ground plane are bonded to a commercial silicon substrate and then attached to 5 mm-high chamber made by 3D printer so that it contains only 1 mL seawater. For easy fabrication and testing, the maximum resonance frequency was selected under 3 GHz and to cover salinities in real seawater, it was assumed that the salinity changes from 20 to 35 ppt. The sensor was designed by the finite element method-based ANSYS high-frequency structure simulator (HFSS), and it can detect the salinity with 0.01 ppt resolution. The designed sensor has a resonance frequency separation of 37.9 kHz and reflection coefficients under −20 dB at the resonant frequencies. The fabricated sensor showed better performance with average frequency separation of 48 kHz and maximum reflection coefficient of −35 dB. By comparing with the existing sensors, the proposed compact and low-cost sensor showed a better detection capability. Therefore, the proposed patch sensor can be utilized in radio frequency (RF) tunable sensors for salinity determination. Full article
(This article belongs to the Section Physical Sensors)
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<p>(<b>a</b>) Conductivity of seawater according to salinity; (<b>b</b>) Permittivity of seawater according to salinity.</p>
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<p>(<b>a</b>) Geometry of the microstrip patch resonator; (<b>b</b>) Structure of the proposed salinity sensor.</p>
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<p>Top view of the feed inset patch antenna.</p>
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<p>(<b>a</b>) Fabricated sensor; (<b>b</b>) Experiment setup.</p>
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<p>Simulation results. (<b>a</b>) Frequency response; (<b>b</b>) Resonant frequency according to salinity level.</p>
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<p>Simulation results for 0.01 ppt interval variation.</p>
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<p>Frequency response according to fabrication error: (<b>a</b>) Fabrication error for patch width <span class="html-italic">W</span>; (<b>b</b>) Fabrication error for patch length <span class="html-italic">L</span>.</p>
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<p>Resonant frequency according to salinity level by fabrication error.</p>
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<p>Experimental results: (<b>a</b>) Reflection coefficients of the proposed sensor; (<b>b</b>) Resonant frequency according to salinity.</p>
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5142 KiB  
Article
Design and Performance Evaluation of an Electro-Hydraulic Camless Engine Valve Actuator for Future Vehicle Applications
by Kanghyun Nam, Kwanghyun Cho, Sang-Shin Park and Seibum B. Choi
Sensors 2017, 17(12), 2940; https://doi.org/10.3390/s17122940 - 18 Dec 2017
Cited by 9 | Viewed by 8911
Abstract
This paper details the new design and dynamic simulation of an electro-hydraulic camless engine valve actuator (EH-CEVA) and experimental verification with lift position sensors. In general, camless engine technologies have been known for improving fuel efficiency, enhancing power output, and reducing emissions of [...] Read more.
This paper details the new design and dynamic simulation of an electro-hydraulic camless engine valve actuator (EH-CEVA) and experimental verification with lift position sensors. In general, camless engine technologies have been known for improving fuel efficiency, enhancing power output, and reducing emissions of internal combustion engines. Electro-hydraulic valve actuators are used to eliminate the camshaft of an existing internal combustion engines and used to control the valve timing and valve duration independently. This paper presents novel electro-hydraulic actuator design, dynamic simulations, and analysis based on design specifications required to satisfy the operation performances. An EH-CEVA has initially been designed and modeled by means of a powerful hydraulic simulation software, AMESim, which is useful for the dynamic simulations and analysis of hydraulic systems. Fundamental functions and performances of the EH-CEVA have been validated through comparisons with experimental results obtained in a prototype test bench. Full article
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)
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<p>Scheme of the engine valve’s open and close operation.</p>
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<p>Principle of operation of hydraulic snubber in engine valve closing.</p>
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<p>Schematics of the electro-hydraulic camless engine valve actuator (EH-CEVA): (<b>a</b>) proportional hydraulic valve module; (<b>b</b>) hydraulic power pack module; (<b>c</b>) EH-CEVA module.</p>
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<p>Details of an EH-CEVA: (<b>a</b>) proportional hydraulic valve module; (<b>b</b>) hydraulic power pack module.</p>
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<p>An EH-CEVA AMESim model.</p>
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<p>Simulation results for design parameter (e.g., piston diameter) variation.</p>
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<p>Simulation results for design parameter (e.g., oil supply pressure) variation.</p>
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<p>Illustration of the control system.</p>
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<p>Experimental results for an EH-CEVA I with the 0.8 mm of snubber orifice: (<b>a</b>) valve lift profile, (<b>b</b>) zoomed in valve lift profile, (<b>c</b>) valve velocity profile, (<b>d</b>) zoomed-in valve velocity profile.</p>
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<p>Experimental results for an EH-CEVA with the 0.3 mm of snubber orifice. (blue line: raw measurement data, red line: measurement data with a low-pass filter): (<b>a</b>) valve lift profile, (<b>b</b>) zoomed in valve lift profile, (<b>c</b>) valve velocity profile, (<b>d</b>) zoomed-in valve velocity profile.</p>
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<p>Valve response at 600 rpm and 100 bar (Open loop): (<b>a</b>) Open/Close command; (<b>b</b>) Valve lift profile; (<b>c</b>) Valve velocity profile.</p>
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<p>Valve response at 2000 rpm and 100 bar (Open loop): (<b>a</b>) Open/Close command; (<b>b</b>) Valve lift profile; (<b>c</b>) Valve velocity profile.</p>
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<p>Comparison of valve responses for real EH-CEVA and AMESim model.</p>
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7610 KiB  
Article
Using the Kalman Algorithm to Correct Data Errors of a 24-Bit Visible Spectrometer
by Son Pham and Anh Dinh
Sensors 2017, 17(12), 2939; https://doi.org/10.3390/s17122939 - 18 Dec 2017
Cited by 3 | Viewed by 3722
Abstract
To reduce cost, increase resolution, and reduce errors due to changing light intensity of the VIS SPEC, a new technique is proposed which applies the Kalman algorithm along with a simple hardware setup and implementation. In real time, the SPEC automatically corrects spectral [...] Read more.
To reduce cost, increase resolution, and reduce errors due to changing light intensity of the VIS SPEC, a new technique is proposed which applies the Kalman algorithm along with a simple hardware setup and implementation. In real time, the SPEC automatically corrects spectral data errors resulting from an unstable light source by adding a photodiode sensor to monitor the changes in light source intensity. The Kalman algorithm is applied on the data to correct the errors. The light intensity instability is one of the sources of error considered in this work. The change in light intensity is due to the remaining lifetime, working time and physical mechanism of the halogen lamp, and/or battery and regulator stability. Coefficients and parameters for the processing are determined from MATLAB simulations based on two real types of datasets, which are mono-changing and multi-changing datasets, collected from the prototype SPEC. From the saved datasets, and based on the Kalman algorithm and other computer algorithms such as divide-and-conquer algorithm and greedy technique, the simulation program implements the search for process noise covariance, the correction function and its correction coefficients. These components, which will be implemented in the processor of the SPEC, Kalman algorithm and the light-source-monitoring sensor are essential to build the Kalman corrector. Through experimental results, the corrector can reduce the total error in the spectra on the order of 10 times; for certain typical local spectral data, it can reduce the error by up to 60 times. The experimental results prove that accuracy of the SPEC increases considerably by using the proposed Kalman corrector in the case of changes in light source intensity. The proposed Kalman technique can be applied to other applications to correct the errors due to slow changes in certain system components. Full article
(This article belongs to the Section Intelligent Sensors)
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<p>Block diagram of the VIS SPEC. Device 1 provides visible light and device 2 analyzes sample spectrum and sends digital data to a computer.</p>
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<p>Device 2 components: (<b>a</b>) light entrance; (<b>b</b>,<b>d</b>) slits on the two sides of each unit; (<b>c</b>) sensor 1; (<b>e</b>) grating and its close view; (<b>f</b>) gears are used to increase the scanning steps, one gear is attached to the grating; (<b>g</b>) 5 V regulator circuit supply energy for other electronics units inside the device 2, and the driver circuit using ULN2003 IC controls the stepper motor; (<b>h</b>) power supply input; (<b>i</b>) digital data output; (<b>j</b>) metal box protects inner circuits from external noise; (<b>k</b>) sensor housing; (<b>l</b>) amplifier circuits are combined two low-pass filters which filter out noise greater than 50 Hz; (<b>m</b>) wall protects the sensor 2 area from the light entrance area; (<b>n</b>) sample cuvette.</p>
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<p>Kalman filter operation loop.</p>
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<p>The simulation flowchart to find correction parameters or process noise covariance <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="bold-italic">Q</mi> <mn mathvariant="bold">1</mn> </msub> </mrow> </semantics> </math>.</p>
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<p>The diagram of the Division function.</p>
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<p>The <span class="html-italic">Finding</span> flowchart for correction parameters.</p>
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<p>The main roles of the Kalman algorithm and their correlation with other parts.</p>
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<p>Raw intensity data and its filtered data with different <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="bold-italic">Q</mi> <mn mathvariant="bold">1</mn> </msub> </mrow> </semantics> </math> values.</p>
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<p>Results of measurement data and processed data (<b>a</b>) the three plots of data in the case of <b><span class="html-italic">Q</span><sub>1</sub></b> = 0.9; and (<b>b</b>) the three plots of data in case of <b><span class="html-italic">Q</span><sub>1</sub></b> ≈ 1.27 × <math display="inline"> <semantics> <mrow> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>21</mn> </mrow> </msup> </mrow> </semantics> </math>.</p>
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<p>The plots of correction coefficients and <span class="html-italic">dX</span> of upper-subdomain data.</p>
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<p>The plots of the correction coefficients and <span class="html-italic">dX</span> of lower-subdomain data.</p>
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<p>(<b>a</b>): <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>d</mi> <msub> <mi>Y</mi> <mn>1</mn> </msub> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>d</mi> <msub> <mi>Y</mi> <mn>2</mn> </msub> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> plots of multi-changing <span class="html-italic">dataset 24</span>; (<b>b</b>): <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>d</mi> <msub> <mi>Y</mi> <mn>1</mn> </msub> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <mi>d</mi> <msub> <mi>Y</mi> <mn>2</mn> </msub> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> plots of lower-mono-changing <span class="html-italic">dataset 1</span>.</p>
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<p>Experimental data of different samples. (<b>a</b>) 0.011 g KMnO<sub>4</sub> and 30 mL distilled H<sub>2</sub>O; (<b>b</b>) air; (<b>c</b>) 0.021 g KMnO<sub>4</sub> and 25 mL distilled H<sub>2</sub>O; (<b>d</b>) distilled H<sub>2</sub>O.</p>
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5064 KiB  
Article
Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines
by Ulrike Dackermann, Yang Yu, Ernst Niederleithinger, Jianchun Li and Herbert Wiggenhauser
Sensors 2017, 17(12), 2938; https://doi.org/10.3390/s17122938 - 18 Dec 2017
Cited by 25 | Viewed by 6918
Abstract
This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband [...] Read more.
This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced), and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM) classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%. Full article
(This article belongs to the Special Issue Sensors and Sensor Networks for Structural Health Monitoring)
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<p>Testing equipment: (<b>a</b>) tactile transducer; (<b>b</b>) sensor wedge and curvature adapter; (<b>c</b>) tactile transducer mounted on sensor wedge; (<b>d</b>) Hi-Fi amplifier; (<b>e</b>) function generator; (<b>f</b>) data acquisition system; (<b>g</b>) accelerometer; (<b>h</b>) signal conditioner and (<b>i</b>) computer.</p>
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<p>Dimensions and damage configurations of the tested poles in longitudinal and cross-sectional view: (<b>a</b>) undamaged timber pole, (<b>b</b>) timber pole with internal void damage, (<b>c</b>) timber pole with external circumferential cross-section loss damage, (<b>d</b>) timber poles with half-sided cross-section loss damage, (<b>e</b>) undamaged self-compacting concrete pole, (<b>f</b>) self-compacting concrete pole with surface void damage, (<b>g</b>) self-compacting concrete pole with internal honey-comb damage, (<b>h</b>) undamaged generic concrete pole, and (<b>i</b>) generic concrete pole with surface void damage.</p>
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<p>Examples of some damaged poles: (<b>a</b>) timber pole with internal void damage; (<b>b</b>) timber pole with external circumferential cross-section loss damage; (<b>c</b>) timber pole with half-sided cross-section loss damage and (<b>d</b>) self-compacting concrete pole with surface void damage.</p>
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<p>Laboratory and field testing set-up: (<b>a</b>) laboratory testing of timber pole; (<b>b</b>) laboratory testing of concrete pole; (<b>c</b>) field testing of timber pole; (<b>d</b>) field testing of concrete pole; and (<b>e</b>) labels and dimensions of test setup.</p>
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<p>Flow-chart of feature extraction based on FFT signals and PCA.</p>
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<p>Original segmented time-domain acceleration signal from sensor ring 3 of a timber pole with internal void damage tested in the laboratory.</p>
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<p>Averaged fast Fourier transforms (FFTs) of wave signals from the three sensor rings of timber poles tested in the laboratory: (<b>a</b>) TP1; (<b>b</b>) TP2; (<b>c</b>) TP3; and (<b>d</b>) TP4.</p>
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<p>Contributions of the first ten principle components (PCs) of feature indices from timber pole specimens: (<b>a</b>) laboratory testing and (<b>b</b>) field testing.</p>
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<p>First ten PCs of FFT data of timber poles with different damage conditions: (<b>a</b>) laboratory testing and (<b>b</b>) field testing.</p>
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<p>First ten PCs of FFT data of timber poles with different damage conditions: (<b>a</b>) laboratory testing and (<b>b</b>) field testing.</p>
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<p>Principal component analysis (PCA) results of self-compacting concrete poles displaying the first two PCs: (<b>a</b>) laboratory testing and (<b>b</b>) field testing.</p>
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<p>PCA results of generic concrete poles displaying the first two PCs: (<b>a</b>) laboratory testing and (<b>b</b>) field testing.</p>
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<p>Set up and validation of multi-label classifier for damage condition assessment of pole structures: (<b>a</b>) classifier set up, and (<b>b</b>) voting strategy.</p>
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<p>Confusion matrices of support vector machine (SVM) classifiers constructed from data of timber pole laboratory testing using: (<b>a</b>) the RBF kernel function; and (<b>b</b>) the linear kernel function.</p>
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<p>Statistical indicators for all tested pole specimen of the classifiers with different kernel functions: (<b>a</b>) classification accuracy; and (<b>b</b>) Cohen’s Kappa value.</p>
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8793 KiB  
Article
Strain Gauges Based on CVD Graphene Layers and Exfoliated Graphene Nanoplatelets with Enhanced Reproducibility and Scalability for Large Quantities
by Volkan Yokaribas, Stefan Wagner, Daniel S. Schneider, Philipp Friebertshäuser, Max C. Lemme and Claus-Peter Fritzen
Sensors 2017, 17(12), 2937; https://doi.org/10.3390/s17122937 - 18 Dec 2017
Cited by 24 | Viewed by 8741
Abstract
The two-dimensional material graphene promises a broad variety of sensing activities. Based on its low weight and high versatility, the sensor density can significantly be increased on a structure, which can improve reliability and reduce fluctuation in damage detection strategies such as structural [...] Read more.
The two-dimensional material graphene promises a broad variety of sensing activities. Based on its low weight and high versatility, the sensor density can significantly be increased on a structure, which can improve reliability and reduce fluctuation in damage detection strategies such as structural health monitoring (SHM). Moreover; it initializes the basis of structure–sensor fusion towards self-sensing structures. Strain gauges are extensively used sensors in scientific and industrial applications. In this work, sensing in small strain fields (from −0.1% up to 0.1%) with regard to structural dynamics of a mechanical structure is presented with sensitivities comparable to bulk materials by measuring the inherent piezoresistive effect of graphene grown by chemical vapor deposition (CVD) with a very high aspect ratio of approximately 4.86 × 108. It is demonstrated that the increasing number of graphene layers with CVD graphene plays a key role in reproducible strain gauge application since defects of individual layers may become less important in the current path. This may lead to a more stable response and, thus, resulting in a lower scattering.. Further results demonstrate the piezoresistive effect in a network consisting of liquid exfoliated graphene nanoplatelets (GNP), which result in even higher strain sensitivity and reproducibility. A model-assisted approach provides the main parameters to find an optimum of sensitivity and reproducibility of GNP films. The fabricated GNP strain gauges show a minimal deviation in PRE effect with a GF of approximately 5.6 and predict a linear electromechanical behaviour up to 1% strain. Spray deposition is used to develop a low-cost and scalable manufacturing process for GNP strain gauges. In this context, the challenge of reproducible and reliable manufacturing and operating must be overcome. The developed sensors exhibit strain gauges by considering the significant importance of reproducible sensor performances and open the path for graphene strain gauges for potential usages in science and industry. Full article
(This article belongs to the Special Issue Sensor Technologies for Health Monitoring of Composite Structures)
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<p>(<b>a</b>) Schematic description of the main fabrication steps of mono-, bi- and multi-layer CVD graphene: (1) CVD graphene on Cu foil, (2) spin coating of polymethyl methacrylate (PMMA), (3) Cu etching, (4) lifting out of graphene-PMMA stack with a PI foil, (5) contacting by using silver paste; (<b>b</b>) fabricated strain gauge on PI substrate.</p>
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<p>Representative Raman spectra with an excitation wavelength of 532 nm measured on CVD grown graphene: (<b>a</b>) mono-layer graphene with 2D/G intensity ratio of 3.45; (<b>b</b>) multi-layer graphene (6 to 8 layers) with 2D/G intensity ratio of 0.6 purchased from ACS Materials; (<b>c</b>) SEM image displaying structure of our mono-layer graphene on a Si-SiO<sub>2</sub> wafer; (<b>d</b>) High-resolution AFM measurement displaying roughness of a mono-layer graphene on a Si-SiO<sub>2</sub> wafer.</p>
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<p>(<b>a</b>) Schematically shown spray deposition; (<b>b</b>) spray deposition device of aqueous dispersion with GNP [<a href="#B49-sensors-17-02937" class="html-bibr">49</a>].</p>
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<p>Strain gauge fabrication steps with GNP: spray deposition of GNP (1), GNP on flexible polyimide foil (2), thermal treatment and evaporation of electrical contacts (3), GNP strain gauges after encapsulation (4) [<a href="#B49-sensors-17-02937" class="html-bibr">49</a>].</p>
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<p>(<b>a</b>) SEM image of GNP coating (top view); (<b>b</b>) SEM image of GNP coating (52° inclined view); (<b>c</b>) numerical 2D-model with high density of randomly distributed GNP and the main conductive paths (highlighted in yellow-red) [<a href="#B49-sensors-17-02937" class="html-bibr">49</a>].</p>
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<p>(<b>a</b>,<b>c</b>) Electromechanical responses of mono- and multi-layer CVD graphene in comparison to metallic strain gauges for five load cycles; (<b>b</b>,<b>d</b>) determination of the first eigenfrequency during load test (using a cut-off frequency filter for frequencies lower than 3 Hz).</p>
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<p>Gauge factors (including number of measurements, the mean and the standard deviation) of mono- (<b>a</b>), bi- (<b>b</b>) and multi-layer (<b>c</b>) CVD graphene for tensile and compressive loads on the cantilever beam.</p>
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<p>(<b>a</b>) Comparison of GF for seven strain gauges from the same production batch (data statistics mean and standard deviation (std)); (<b>b</b>) exemplary electromechanical response of a GNP strain gauge (sensor number seven); (<b>c</b>) representative element of the coating with a GNP concentration of Vc = 40%; (<b>d</b>) electromechanical behavior of the numerical 2D-model for the representative element far beyond the percolation threshold [<a href="#B49-sensors-17-02937" class="html-bibr">49</a>].</p>
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<p>Changes in the current paths (from left to right) within the identical rectangular network by increasing the potential barrier: λ = [0.04 (<b>a</b>); 0.23 (<b>b</b>); 0.95 (<b>c</b>); 2.14 (<b>d</b>); 3.81 (<b>e</b>); 8.57 (<b>f</b>)] eV.</p>
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<p>GF (<b>a</b>) and the initial resistance (<b>b</b>) as a function of the potential barrier.</p>
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<p>Pneumatic/Hydro plan of the spray deposition system for GNP strain gauges fabrication (A: compressed air supply, B: pressure regulator, C: magnetic valve for air flow controlling, D: vessel for dispersion, E: manual valve, F: manual valve to remove the dispersion from the vessel, G: manual vessel for liquid supply, H: magnetic valve for liquid flow controlling, I: distribution valve, J: nozzle).</p>
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<p>Representative Raman spectra with an excitation wavelength of 532 nm measured for unstrained (with 2D/G intensity ratio of 0.32) and strained (1%) state of a GNP film.</p>
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10076 KiB  
Article
Hazardous Object Detection by Using Kinect Sensor in a Handle-Type Electric Wheelchair
by Jeyeon Kim, Takaaki Hasegawa and Yuta Sakamoto
Sensors 2017, 17(12), 2936; https://doi.org/10.3390/s17122936 - 18 Dec 2017
Cited by 4 | Viewed by 5263
Abstract
To ensure the safety of a handle-type electric wheelchair (hereinafter, electric wheelchair), this paper describes the applicability of using a Kinect sensor. Ensuring the mobility of elderly people is a particularly important issue to be resolved. An electric wheelchair is useful as a [...] Read more.
To ensure the safety of a handle-type electric wheelchair (hereinafter, electric wheelchair), this paper describes the applicability of using a Kinect sensor. Ensuring the mobility of elderly people is a particularly important issue to be resolved. An electric wheelchair is useful as a means of transportation for elderly people. Considering that the users of electric wheelchairs are elderly people, it is important to ensure the safety of electric wheelchairs at night. To ensure the safety of an electric wheelchair at night, we constructed a hazardous object detection system using commercially available and inexpensive Kinect sensors and examined the applicability of the system. We examined warning timing with consideration to the cognition, judgment, and operation time of elderly people. Based on this, a hazardous object detection area was determined. Furthermore, the detection of static and dynamic hazardous objects was carried out at night and the results showed that the system was able to detect with high accuracy. We also conducted experiments related to dynamic hazardous object detection during daytime. From the above, it showed that the system could be applicable to ensuring the safety of the handle-type electric wheelchair. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan 2017)
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<p>Ensuring the self-supportable mobility of elderly people and the vitality of society.</p>
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<p>Image of the system.</p>
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<p>Static hazardous object.</p>
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<p>Method of estimating the relative position from the electric wheelchair to the hazardous object.</p>
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<p>Measures against error caused by a change of road gradient conversion.</p>
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<p>Installation of the Kinect, and the static hazardous object detection area.</p>
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<p>Static hazardous objects in experiments.</p>
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<p>Experimental methodology.</p>
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<p>Method of acquiring the relative position of static and dynamic hazardous object from the electric wheelchair.</p>
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<p>Installation of Camera A.</p>
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<p>Experimental scene during nighttime.</p>
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<p>Distribution of estimation error of static hazardous object.</p>
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<p>Reason of average error in the static hazardous object.</p>
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<p>Example of the static hazardous object detection.</p>
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<p>Estimation error in the dynamic hazardous object detection.</p>
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<p>Example of the dynamic hazardous object detection.</p>
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<p>Reason of the average error in the dynamic hazardous object.</p>
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<p>Results of the hazardous object detection in driving experiments on a slope.</p>
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<p>Example of the dynamic hazardous object detection by optical flow.</p>
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<p>Method of estimating the position (<span class="html-italic">D<sub>x</sub></span>, <span class="html-italic">D<sub>y</sub></span>) of the dynamic hazardous object.</p>
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<p>Experimental scene in daylight.</p>
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<p>Distribution of measurement error in the dynamic hazardous object.</p>
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<p>An example of dynamic hazardous object detection in daylight.</p>
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