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Sensors, Volume 18, Issue 11 (November 2018) – 491 articles

Cover Story (view full-size image): Any interaction of a gas molecule with a sensing system takes place at the surface of the system. Once an interaction takes place, especially if reversible, this can change the electronic properties of the surface itself. For this reason, due to their nature as “all-surface” materials, two-dimensional (2D) materials are the ultimate materials for gas sensing. In this review, the sensing properties of semiconducting 2D systems like graphene oxide, molybdenite (MoS2), and its twin 2D TMD material WS2, are reviewed along with phosphorene. Issues, prospects, and long-term visions are addressed. View this paper.
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19 pages, 2983 KiB  
Article
A Handheld Real-Time Photoacoustic Imaging System for Animal Neurological Disease Models: From Simulation to Realization
by Yu-Hang Liu, Yu Xu, Lun-De Liao, Kim Chuan Chan and Nitish V. Thakor
Sensors 2018, 18(11), 4081; https://doi.org/10.3390/s18114081 - 21 Nov 2018
Cited by 13 | Viewed by 5888
Abstract
This article provides a guide to design and build a handheld, real-time photoacoustic (PA) imaging system from simulation to realization for animal neurological disease models. A pulsed laser and array-based ultrasound (US) platform were utilized to develop the system for evaluating vascular functions [...] Read more.
This article provides a guide to design and build a handheld, real-time photoacoustic (PA) imaging system from simulation to realization for animal neurological disease models. A pulsed laser and array-based ultrasound (US) platform were utilized to develop the system for evaluating vascular functions in rats with focal ischemia or subcutaneous tumors. To optimize the laser light delivery, finite element (FE)-based simulation models were developed to provide information regarding light propagation and PA wave generation in soft tissues. Besides, simulations were also conducted to evaluate the ideal imaging resolution of the US system. As a result, a PA C-scan image of a designed phantom in 1% Lipofundin was reconstructed with depth information. Performance of the handheld PA system was tested in an animal ischemia model, which revealed that cerebral blood volume (CBV) changes at the cortical surface could be monitored immediately after ischemia induction. Another experiment on subcutaneous tumors showed the anomalous distribution of the total hemoglobin concentration (HbT) and oxygen saturation (SO2), while 3D and maximum intensity projection (MIP) PA images of the subcutaneous tumors are also presented in this article. Overall, this system shows promise for monitoring disease progression in vascular functional impairments. Full article
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Graphical abstract

Graphical abstract
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<p>The handheld, real-time photoacoustic (PA) imaging system. One custom-designed optical parametric oscillator (OPO) diode-pumped by a Nd:YAG laser at 532 nm was employed for laser illumination with a fast-wavelength-tuning function for each pulse. The customized fiber bundle was used to deliver the laser light onto the target. Light was evenly distributed into the two arms with a rectangular output size of 16.5 mm × 0.8 mm. A 128-channel research ultrasound (US) platform with a high-frequency array transducer was used for recording the generated PA signal. For proof of simulation results, the PA probe (i.e., fiber bundle with US transducer array) was mounted on the linear and rotation stages to adjust the interval and angle of the two arms. For phantom and in vivo studies (e.g., small animal experiments), the XYZ 3-axis scanning stage with frame holder was used to precisely/automatically image the region of interest (ROI) of the target in either the X or Y direction.</p>
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<p>Simulation of the ultrasound (US) response using Vantage software. (<b>A</b>) The simulated spatial resolutions of one ideal single point scatter at 10-mm depth and the center of the detecting area. The full width at half maximum (FWHM) of this scatter image was calculated as 166.6 μm (axial) and 186.2 μm (lateral). (<b>B</b>) The lateral point spread function (PSF) map of US imaging. The ideal scatter was swept through the entire imaging area (i.e., width: −6 to +6 mm; depth: 3 to 15 mm). The lateral PSF response ranged from 176.4 to 308.7 μm. The imaging resolution decreased when the scatter was at deeper or edge regions. Thus, enough light should be delivered to the depth of 11 mm to acquire PA imaging with consistent imaging quality. (<b>C</b>) Two ideal single point scatters were placed in the imaging area with different intervals to assess the lateral resolution. The two scatters were still distinguishable at a depth of 9 mm when the interval between the two scatters was 150 µm. Note that the scale bar in the 3 mm (depth) subfigure is also applied to other subfigures shown in (<b>C</b>).</p>
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<p>Schematic and simulation results of fluence in 1% Lipofundin. The simulated laser source was a short pulsed (5 ns pulse width), high-energy (20 mJ per pulse) laser, with Gaussian energy distributed temporally. The output beam dimensions of each fiber bundle arm were 16.5 mm × 0.8 mm. (<b>A</b>) The simulation configuration for light transmission in 1% Lipofundin. <math display="inline"><semantics> <mi mathvariant="sans-serif">α</mi> </semantics></math> is the incident angle of the light sources. Z0 is the transport mean free path. Within the distance of Z0, photons propagate in their original directions with negligible scattering events. The Interval is the distance between the two arms of fiber bundle, while the *Interval is the distance between the central points of the red lines (i.e., the light starts to propagate in the diffusive regime). The detector is a 128-channel transducer array. The interval and incident angle are the two main factors included for evaluation. (<b>B</b>) The changes in light fluence with respect to different incident angles. The interval was fixed at 14 mm. The light fluence decreased with increasing incident angles. However, the differences in light fluence were minimal when the angle ranged from 15 degrees to 35 degrees. (<b>C</b>) The changes in light fluence with respect to different intervals between the two arms of the fiber bundle. The incident angle was fixed at 35 degrees. The fluence value decreased dramatically with an increasing interval from 10 mm to 30 mm. The fluence difference was also reduced as the light propagated deeper into the medium. The results indicated that two arms need to be placed as close together as possible to reach a higher fluence value.</p>
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<p>Simulated photoacoustic (PA) response using COMSOL. (<b>A</b>) The simulated PA signals with different intervals between the two arms of the fiber bundle. The intensity of the PA signal with a 14-mm interval was 3.35-fold larger than the PA signal with a 22-mm interval. (<b>B</b>) The X-Z plane time sequence snapshots of the PA wave field at 4 representative time points in 1% Lipofundin. When the target received a Gaussian light pulse (peak time at 30 ns), the PA wave started to generate from the surface of the target and propagated outward in the scattering medium.</p>
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<p>Evaluation of the fluence intensity change in 1% Lipofundin at different depths. (<b>A</b>) The angle changed from 0 degree to 60 degrees, while the interval between the two output arms was fixed at 14 mm. (<b>B</b>) The interval ranged from 10 mm to 26 mm, while the angle was fixed at 35 degrees for the fluence evaluation. According to (<b>A</b>) and (<b>B</b>), the angles of the fiber bundle would not largely affect the fluence intensity, while a shorter interval (separation) of the two arms could greatly influence the intensity in scattering medium. Thus, we designed the interval of the two arms to be as short as possible for the system, while the angle was chosen as 35 degrees for experiments in both water and scattering medium.</p>
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<p>Photoacoustic (PA) image assessment based on the phantom targets. (<b>A</b>,<b>B</b>) The proof of spatial resolution of the PA imaging system using two thin hairs in 1% Lipofundin. A 3D-printed holder with M4 screws in (<b>A</b>) could be used to fix the fiber bundle and transducer array on the scanning stage or directly used for handheld applications. The dimensions of the handheld probe are 4 cm × 5 cm × 8 cm. (<b>C</b>,<b>D</b>) A photo and color-coded PA image of the designed phantom with depth information. A photo of the designed phantom is shown in (<b>C</b>). The scanning stage was used to scan the entire phantom with a 50-µm step size in the Y direction for acquiring multiple B-scan images, and these images were then reconstructed for the C-scan image as shown in (<b>D</b>). Note that the interrupted image at the cross point of the pencil lead and thread occurred at a shallower depth (shallower than 8.5 mm). Here, we show the reconstructed PA image between 8.5 mm to 11 mm only due to the dark-field illumination scheme of this PA imaging system.</p>
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<p>Performance evaluation of the photoacoustic (PA) imaging system based on the PTI model. (<b>A</b>) The photos of the selected cortical blood vessel pre- and post-PTI within the cranial window. The yellow dashed line indicates the location of the PA B-scan image, while the black rectangle is the ischemic region. (<b>B</b>) Co-registered PA-US B-scan images. The cerebral blood volume (CBV) of the selected cerebral blood vessel shown in the post-PTI image was substantially lower than that shown in the pre-PTI image. (<b>C</b>) TTC staining results with and without PTI are presented to support the results of the in vivo PA image.</p>
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<p>Subcutaneous tumor evaluation using the developed PA imaging system. (<b>A</b>) The tumor in the right hind leg was imaged by our PA imaging system. The upper subfigure shows that the tumor region (indicated by red dashed line) was scanned in the scanning direction (blue arrow) to acquire multiple X-Z plane PA B-scan images. The lower subfigure demonstrates the reconstructed 5-slice CBV C-scan images in the X-Y plane at different depths. (<b>B</b>,<b>C</b>) The TopView (X-Y plane) and SideView (Y-Z plane) MIP images of the subcutaneous tumor, respectively. Tumor regions are indicated by yellow dashed lines. (<b>D</b>,<b>E</b>) 3D reconstructed US and PA images of the subcutaneous tumor, respectively. (<b>F</b>) A normalized total hemoglobin concentration (HbT)-ultrasound (US) co-registered X-Z plane B-scan image. The distribution of HbT is highly correlated with the subcutaneous tumor profile acquired by US imaging. (<b>G</b>) A normalized oxygen saturation (SO<sub>2</sub>) image of the subcutaneous tumor. Note that the yellow and red dashed lines indicate the tumor regions in (<b>F</b>) and (<b>G</b>), respectively.</p>
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15 pages, 2060 KiB  
Article
Correntropy Based Divided Difference Filtering for the Positioning of Ships
by Xi Liu, Badong Chen, Shiyuan Wang and Shaoyi Du
Sensors 2018, 18(11), 4080; https://doi.org/10.3390/s18114080 - 21 Nov 2018
Cited by 4 | Viewed by 3356
Abstract
In this paper, robust first and second-order divided difference filtering algorithms based on correntropy are proposed, which not only retain the advantages of divided difference filters, but also exhibit robustness in the presence of non-Gaussian noises, especially when the measurements are contaminated by [...] Read more.
In this paper, robust first and second-order divided difference filtering algorithms based on correntropy are proposed, which not only retain the advantages of divided difference filters, but also exhibit robustness in the presence of non-Gaussian noises, especially when the measurements are contaminated by heavy-tailed noises. The proposed filters are then applied to the problem of ship positioning. In order to improve the accuracy and reliability of ship positioning, the positioning method combines the Dead Reckoning (DR) algorithm and the Global Positioning System (GPS). Experimental results of an illustrative example show the superior performance of the new algorithms when applied to ship positioning. Full article
(This article belongs to the Section Remote Sensors)
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<p>The flow of the CDD1 filter algorithm.</p>
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<p><math display="inline"><semantics> <mi>MSE</mi> </semantics></math>s of <math display="inline"><semantics> <mi>φ</mi> </semantics></math> with first-order approximate filters in non-Gaussian noises.</p>
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<p><math display="inline"><semantics> <mi>MSE</mi> </semantics></math>s of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> with first-order approximate filters in non-Gaussian noises.</p>
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<p><math display="inline"><semantics> <mi>MSE</mi> </semantics></math>s of <math display="inline"><semantics> <mi>φ</mi> </semantics></math> with second-order approximate filters in non-Gaussian noises.</p>
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<p><math display="inline"><semantics> <mi>MSE</mi> </semantics></math>s of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> with second-order approximate filters in non-Gaussian noises.</p>
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12 pages, 4158 KiB  
Article
A 40-MHz Ultrasound Transducer with an Angled Aperture for Guiding Percutaneous Revascularization of Chronic Total Occlusion: A Feasibility Study
by Junsu Lee and Jin Ho Chang
Sensors 2018, 18(11), 4079; https://doi.org/10.3390/s18114079 - 21 Nov 2018
Cited by 12 | Viewed by 4778
Abstract
Complete blockage of a coronary artery, called chronic total occlusion (CTO), frequently occurs due to atherosclerosis. To reopen the obstructed blood vessels with a stent, guidewire crossing is performed with the help of angiography that can provide the location of CTO lesions and [...] Read more.
Complete blockage of a coronary artery, called chronic total occlusion (CTO), frequently occurs due to atherosclerosis. To reopen the obstructed blood vessels with a stent, guidewire crossing is performed with the help of angiography that can provide the location of CTO lesions and the image of guidewire tip. Since angiography is incapable of imaging inside a CTO lesion, the surgeons are blind during guidewire crossing. For this reason, the success rate of guidewire crossing relies upon the proficiency of the surgeon, which is considerably reduced from 69.0% to 32.5% if extensive calcification, not penetrated by a guidewire, exists in CTO lesions. In this paper, a recently developed 40-MHz forward-looking intravascular ultrasound (FL–IVUS) transducer to visualize calcification within CTO lesions is reported. This transducer consists of a single element angled aperture and a guidewire passage. The aperture is spherically deformed to have a focal length of 3 mm in order to improve spatial resolution of FL–IVUS images. The angle between the beam direction and the axis of rotation is designed to be 30° to effectively visualize calcification within a CTO lesion as well as the blood vessel wall. The experimental results demonstrated that the developed FL–IVUS transducer facilitates visualization of calcification within CTO lesions and makes it possible to help the surgeon make decisions about whether to push the guidewire in order to cross the lesion or to change the surgical procedure. Full article
(This article belongs to the Special Issue Ultrasound Transducers)
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<p>Conceptual illustration of (<b>a</b>) the proposed forward-looking intravascular ultrasound (FL–IVUS) transducer consisting of a single-element angled aperture and a guidewire passage, (<b>b</b>) the conical imaging plane along the vertical direction of the blood vessel, and (<b>c</b>) the FL–IVUS image expected to be displayed on a monitor. <span class="html-italic">α</span> and <span class="html-italic">θ</span> indicate the slant height and semi-vertical angle of the conical imaging plane. <span class="html-italic">β</span> is the closest distance between the imaging plane and the guidewire position plane. <span class="html-italic">d</span><sub>0</sub> and <span class="html-italic">d<sub>W</sub></span> are the distances from the vertex of the cone to the aperture surface and to the center of the guidewire passage, respectively.</p>
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<p>Pulse-echo response (solid line) and its frequency spectrum (dashed line) obtained by simulation using a PiezoCAD program (Sonic Concept, Woodinville, WA, USA).</p>
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<p>Photographs of the finished FL–IVUS transducer and the zoomed-in version of the white solid circle.</p>
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<p>Measured pulse–echo response (solid line) and frequency spectrum (dashed line) of the developed FL–IVUS transducer.</p>
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<p>(<b>a</b>) Ultrasound B-mode image of a 25 μm wire acquired at a focal depth of 3 mm, (<b>b</b>) the axial and (<b>c</b>) lateral beam profiles measured from the wire image indicated by a white dotted circle.</p>
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<p>(<b>a</b>) Photograph of the custom blood-vessel-mimicking phantom and (<b>b</b>–<b>d</b>) FL–IVUS images inside the hole of the phantom (see the white solid circle in (<b>a</b>)). The white solid arrows in (<b>a</b>) indicate hyperechogenic regions (i.e., the calcification-mimicking parts), and the white arrow head is the boundary between the water and the tissue-mimicking phantom. The space between the white bars on the IVUS images indicates 1 mm in the slant height. The hole in (<b>a</b>) is 8 mm in diameter.</p>
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<p>(<b>a</b>,<b>c</b>) Photographs of the custom blood-vessel-mimicking phantoms and (<b>b</b>,<b>d</b>) FL–IVUS images inside the hole of the each phantom. The white solid arrows in (<b>a</b>,<b>b</b>) indicate hyperechogenic regions (i.e., the calcification-mimicking parts), and the white arrow heads in (<b>a</b>,<b>b</b>) are the boundary between the water and the tissue-mimicking phantom. The space between the white bars on the IVUS images indicates 1 mm in the slant height. The holes in (<b>a</b>,<b>c</b>) are 5 mm in diameter.</p>
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11 pages, 4158 KiB  
Article
Improving the Error of Time Differences of Arrival on Partial Discharges Measurement in Gas-Insulated Switchgear
by Jun Jiang, Kai Wang, Chaohai Zhang, Min Chen, Hong Zheng and Ricardo Albarracín
Sensors 2018, 18(11), 4078; https://doi.org/10.3390/s18114078 - 21 Nov 2018
Cited by 11 | Viewed by 5319
Abstract
Partial Discharge (PD) detection based on Ultra-High-Frequency (UHF) measurements in Gas-Insulated Switchgear (GIS) is often used for fault location based on extraction of Time Differences of Arrival (TDoA), and the core technique is to obtain the precise time difference of each UHF signal. [...] Read more.
Partial Discharge (PD) detection based on Ultra-High-Frequency (UHF) measurements in Gas-Insulated Switchgear (GIS) is often used for fault location based on extraction of Time Differences of Arrival (TDoA), and the core technique is to obtain the precise time difference of each UHF signal. Usually, TDoA extraction algorithms can be categorized as cross-correlation function method (CCF), minimum energy method (ME), and threshold value method (TV) are not qualified to analyze the time difference with high accuracy and efficiency, especially the complicated UHF PD signals in the field. In this paper, multiple tests were carried out based on the real GIS UHF signals. Three typical algorithms (CCF, ME, and TV) were used to extract and calculate the TDoA of UHF signals. Afterwards, depending on the disassembly of equipment, the accuracy and effective range of the algorithms are analyzed by means of error and variance. To minimize the error and the variance, an average method with the combination (CA) and portfolio of traditional algorithms is proposed and verified in different situations. The results demonstrate that the improved algorithm could increase the accuracy of time difference extraction, less than 4.0%. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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<p>Installation of Ultra-High-Frequency (UHF) sensors on 1100 kV Gas-Insulated Switchgear (GIS) in the field. (<b>a</b>) Overall installation; (<b>b</b>) Partial details.</p>
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<p>Layout of online monitoring system layout diagram.</p>
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<p>Phase-Resolved Pulse Sequence of the real Partial Discharge (PD) activity in Gas-Insulated Switchgear (GIS).</p>
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<p>Schematic definition of the initial peak wave.</p>
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<p>Waveform of cross-correlation function.</p>
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<p>The curve about minimum energy method of waveform 1.</p>
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<p>The curve about minimum energy method of waveform 2.</p>
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<p>The curve about threshold value method of waveform 1.</p>
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<p>The curve about threshold value method of waveform 2.</p>
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<p>Fault location of PD activity in GIS.</p>
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<p>Algorithm interpretation of CA.</p>
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<p>Extraction algorithm of TDoA analysis and comparison.</p>
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10 pages, 2535 KiB  
Letter
Frequency Offset Tolerant Synchronization Signal Design in NB-IoT
by Jun Zou and Chen Xu
Sensors 2018, 18(11), 4077; https://doi.org/10.3390/s18114077 - 21 Nov 2018
Cited by 11 | Viewed by 4439
Abstract
Timing detection is the first step and very important in wireless communication systems. Timing detection performance is usually affected by the frequency offset. Therefore, it is a challenge to design the synchronization signal in massive narrowband Internet of Things (NB-IoT) scenarios where the [...] Read more.
Timing detection is the first step and very important in wireless communication systems. Timing detection performance is usually affected by the frequency offset. Therefore, it is a challenge to design the synchronization signal in massive narrowband Internet of Things (NB-IoT) scenarios where the frequency offset is usually large due to the low cost requirement. In this paper, we firstly proposed a new general synchronization signal structure with a couple of sequences which are conjugated to remove the potential timing error that arises from large frequency offset. Then, we analyze the suitable sequence for our proposed synchronization signal structure and discuss a Zadoff–Chu (ZC) sequence with root 1 as an example. Finally, the simulation results demonstrate that our proposed synchronization signal can work well when the frequency offset is large. It means that our proposed synchronization signal design is very suitable for the massive NB-IoT. Full article
(This article belongs to the Section Internet of Things)
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<p>Illustration of our proposed conjugated-sequences-based primary synchronization signal (PSS) structure.</p>
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<p>Illustration of the maximum correlator output of Zadoff–Chu (ZC) sequences with different roots (<math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>131</mn> </mrow> </semantics></math>).</p>
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<p>Illustration of the correlator output of the ZC sequence with root <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> under different timing offsets.</p>
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<p>Illustration of the main lobes of the correlator output around the maximum frequency offset <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>λ</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>λ</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics></math> &gt; 0 in this example). The dash line is the copy of the solid one with the same color.</p>
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<p>Timing detection error rate of the M-part correlator and the differential correlator with different frequency offsets.</p>
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<p>Timing detection error rate of our proposed synchronization signal with different frequency offsets.</p>
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15 pages, 4540 KiB  
Article
Sensitive and Reproducible Gold SERS Sensor Based on Interference Lithography and Electrophoretic Deposition
by June Sik Hwang and Minyang Yang
Sensors 2018, 18(11), 4076; https://doi.org/10.3390/s18114076 - 21 Nov 2018
Cited by 25 | Viewed by 7160
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a promising analytical tool due to its label-free detection ability and superior sensitivity, which enable the detection of single molecules. Since its sensitivity is highly dependent on localized surface plasmon resonance, various methods have been applied for electric [...] Read more.
Surface-enhanced Raman spectroscopy (SERS) is a promising analytical tool due to its label-free detection ability and superior sensitivity, which enable the detection of single molecules. Since its sensitivity is highly dependent on localized surface plasmon resonance, various methods have been applied for electric field-enhanced metal nanostructures. Despite the intensive research on practical applications of SERS, fabricating a sensitive and reproducible SERS sensor using a simple and low-cost process remains a challenge. Here, we report a simple strategy to produce a large-scale gold nanoparticle array based on laser interference lithography and the electrophoretic deposition of gold nanoparticles, generated through a pulsed laser ablation in liquid process. The fabricated gold nanoparticle array produced a sensitive, reproducible SERS signal, which allowed Rhodamine 6G to be detected at a concentration as low as 10−8 M, with an enhancement factor of 1.25 × 105. This advantageous fabrication strategy is expected to enable practical SERS applications. Full article
(This article belongs to the Special Issue Applications of Raman Spectroscopy in Sensors)
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<p>Schematic diagram of the fabrication process of the gold (Au) nanoparticle (NP) array: (<b>a</b>) a pristine electrode, (<b>b</b>) spin-coated photoresist (PR) layer on the electrode, (<b>c</b>) the PR nanohole patterned layer by laser interference lithography, (<b>d</b>) electrophoretic deposition (EPD) of Au NPs, (<b>e</b>) deposited Au NPs in the PR template, and (<b>f</b>) PR etching to form the Au NP array.</p>
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<p>Scanning electron microscopy (SEM) image of (<b>a</b>) the PR line pattern and (<b>b</b>) the PR hole pattern on the indium tin oxide (ITO) and SEM images of the substrate, following (<b>c</b>) Au NP EPD and (<b>d</b>) PR etching.</p>
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<p>(<b>a</b>) Transmission electron microscopy (TEM) image, (<b>b</b>) size distribution, (<b>c</b>) selected area electron diffraction (SAED) pattern, (<b>d</b>) absorbance, and (<b>e</b>) zeta potential of the Au NP solution from PLAL.</p>
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<p>SEM image of the substrate after Au NP EPD: 5 V/cm with (<b>a</b>) 10 min, (<b>b</b>) 30 min, and (<b>c</b>) 60 min; and 10 V/cm with (<b>d</b>) 10 min, (<b>e</b>) 30 min, and (<b>f</b>) 60 min. Insets: The corresponding local magnification.</p>
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<p>SEM image of the PR nanohole template with (<b>a</b>) 700 nm, (<b>b</b>) 900 nm, and (<b>c</b>) 1 μm period conditions, and (<b>d</b>–<b>f</b>) the substrate after Au EPD and following PR etching.</p>
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<p>(<b>a</b>) Surface enhanced Raman spectroscopy (SERS) spectra and (<b>b</b>) the intensity bar diagram of Rhodamine 6G (R6G) 1 μM, depending on the different periods of the Au NP array.</p>
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<p>Surface morphologies and SERS performance of deposited Au NPs with and without the patterned PR layer. Insets: SEM image of the corresponding EPD conditions. To compare the SERS performance, the Raman intensity of R6G 1 μM was measured, as shown in the intensity bar diagram of each sample. We applied 5 V/cm for the EPD process.</p>
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<p>(<b>a</b>) SERS spectra and (<b>b</b>) Raman intensities of R6G at different concentrations. Raman intensities at 1363 cm<sup>−1</sup> were evaluated. Inset: a linear relationship between the intensities and logarithmic concentrations of R6G.</p>
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<p>Reproducibility of the SERS signals on the Au NP array substrate. SERS intensity distribution of the Raman peaks at (<b>a</b>) 611 cm<sup>−1</sup>, (<b>b</b>) 771 cm<sup>−1</sup>, (<b>c</b>) 1363 cm<sup>−1</sup>, and (<b>d</b>) 1650 cm<sup>−1</sup>. (<b>e</b>) SERS spectra of R6G 1 μM obtained from 25 different substrates.</p>
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27 pages, 1615 KiB  
Article
Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
by Ali Balador, Anis Kouba, Dajana Cassioli, Fotis Foukalas, Ricardo Severino, Daria Stepanova, Giovanni Agosta, Jing Xie, Luigi Pomante, Maurizio Mongelli, Pierluigi Pierini, Stig Petersen and Timo Sukuvaara
Sensors 2018, 18(11), 4075; https://doi.org/10.3390/s18114075 - 21 Nov 2018
Cited by 16 | Viewed by 7245
Abstract
Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide [...] Read more.
Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide an overview of five Co-CPS use cases, as introduced in our SafeCOP EU project, and analyze their safety design requirements. Next, we provide a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies. We also investigate to what extent they address the non-functional requirements in terms of safety, security and real time, in the different application domains of each use case. Finally, we discuss general recommendations about the use of different wireless communication technologies showing their potentials in the selected real-world use cases. The discussion is provided under consideration in the 5G standardization process within 3GPP, whose current efforts are inline to current gaps in wireless communications protocols for Co-CPSs including many future use cases. Full article
(This article belongs to the Special Issue Advances on Vehicular Networks: From Sensing to Autonomous Driving)
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<p>Layout of the use case “Autonomous Hospital Beds”.</p>
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<p>During the bathymetry measurements operation, the boats and the unmanned surface vehicle (USV) communicate wirelessly for coordination.</p>
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<p>Vehicle control loss warning.</p>
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<p>Operational structure of the use case “Vehicles and Roadside Unit (RSU) interaction” with communication to the cloud using 3G/4G, and IEEE 802.11p and 3G/4G for communication between vehicles and roadside units.</p>
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<p>Typical scenario for a V2I cooperation system for traffic management.</p>
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<p>System architecture for the traffic management application through V2I cooperation.</p>
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16 pages, 1953 KiB  
Article
Stackelberg Dynamic Game-Based Resource Allocation in Threat Defense for Internet of Things
by Bingjie Liu, Haitao Xu and Xianwei Zhou
Sensors 2018, 18(11), 4074; https://doi.org/10.3390/s18114074 - 21 Nov 2018
Cited by 12 | Viewed by 4372
Abstract
With the rapid development of the Internet of Things, there are a series of security problems faced by the IoT devices. As the IoT devices are generally devices with limited resources, how to effectively allocate the restricted resources facing the security problems is [...] Read more.
With the rapid development of the Internet of Things, there are a series of security problems faced by the IoT devices. As the IoT devices are generally devices with limited resources, how to effectively allocate the restricted resources facing the security problems is the key issue at present. In this paper, we study the resource allocation problem in threat defense for the resource-constrained IoT system, and propose a Stackelberg dynamic game model to get the optimal allocated resources for both the defender and attackers. The proposed Stackelberg dynamic game model is composed by one defender and many attackers. Given the objective functions of the defender and attackers, we analyze both the open-loop Nash equilibrium and feedback Nash equilibrium for the defender and attackers. Then both the defender and attackers can control their available resources based on the Nash equilibrium solutions of the dynamic game. Numerical simulation results show that correctness and effeteness of the proposed model. Full article
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things)
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<p>(<b>a</b>) Optimal strategy of the attackers; (<b>b</b>) optimal strategy of the defender.</p>
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<p>Optimal strategy of the attacker with different <math display="inline"><semantics> <mrow> <msubsup> <mi>u</mi> <mn>0</mn> <mrow/> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> over time.</p>
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<p>(<b>a</b>) Risk level variation for a system with one attacker; (<b>b</b>) risk level variation for a system with different numbers of attackers.</p>
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<p>Risk level with a large number of attackers under open-loop control.</p>
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<p>(<b>a</b>) Optimal strategy of the attacker; (<b>b</b>) optimal strategy of the defender.</p>
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<p>(<b>a</b>) Risk level variation for a system with one attacker; (<b>b</b>) risk level variation for a system with different numbers of attackers.</p>
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<p>Risk level with a large number of attackers under feedback control.</p>
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<p>The time complexity of the proposed game model.</p>
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30 pages, 5749 KiB  
Article
Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning
by Marcelo N. de Sousa and Reiner S. Thomä
Sensors 2018, 18(11), 4073; https://doi.org/10.3390/s18114073 - 21 Nov 2018
Cited by 33 | Viewed by 6832
Abstract
A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel [...] Read more.
A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description. Full article
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<p>TDOA Estimation based on the Complex Baseband (CBB) signals arriving at Sensors 1 and 2.</p>
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<p>Location error caused by multipath situation in TDOA measurements.</p>
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<p>TDOA processing in Sensor 1 and 2.</p>
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<p>Error caused by NLOS situation in TDOA systems.</p>
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<p>The error produced in TDOA location by multipath.</p>
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<p>Main steps of the proposed method.</p>
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<p>Multipath fingerprint database using ray-tracing simulation.</p>
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<p>Channel impulse estimation in each TDOA sensor.</p>
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<p>Schematic representation of walls and edges in ray-tracing simulation.</p>
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<p>Performance in estimation of (<math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>τ</mi> <mi>i</mi> </msub> </mrow> </semantics></math>) for CIR.</p>
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<p>TDOA location scenario in NLOS.</p>
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<p>Machine learning ray-tracing dataset refinement.</p>
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<p>Neural network for position estimation.</p>
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<p>Machine learning framework based on RT simulation.</p>
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<p>Location error caused by multipath situation in TDOA location.</p>
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<p>Complex base band signal measured from UAV.</p>
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<p>Ray-tracing fingerprints dataset.</p>
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<p>RT simulation output.</p>
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<p>Ray-tracing delay fingerprint Sensor 1.</p>
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<p>Ray-tracing amplitude fingerprint Sensor 1.</p>
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<p>Multipath fingerprint of Ray 1 in Sensor 1.</p>
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<p>Testing the dataset with delay–distance and power–distance mapping.</p>
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<p>Error analysis of training data and test dataset.</p>
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<p>Position estimation error in NLOS.</p>
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<p>Final position estimation error with machine learning RT.</p>
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19 pages, 1725 KiB  
Article
Optimal Routing for Time-Driven EH-WSN under Regular Energy Sources
by Sebastià Galmés
Sensors 2018, 18(11), 4072; https://doi.org/10.3390/s18114072 - 21 Nov 2018
Cited by 7 | Viewed by 3277
Abstract
The recent provision of energy-harvesting capabilities to wireless sensor networks (WSN) has entailed the redefinition of design objectives. Specifically, the traditional goal of maximizing network lifetime has been replaced by optimizing network performance, namely delay and throughput. The present paper contributes to this [...] Read more.
The recent provision of energy-harvesting capabilities to wireless sensor networks (WSN) has entailed the redefinition of design objectives. Specifically, the traditional goal of maximizing network lifetime has been replaced by optimizing network performance, namely delay and throughput. The present paper contributes to this reformulation by considering the routing problem for the class of time-driven energy-harvesting WSN (EH-WSN) under regular or quasi-periodic energy sources. In particular, this paper shows that the minimum hop count (MHC) criterion maximizes the average duty cycle that can be sustained by nodes in this type of scenarios. This is a primary objective in EH-WSN, since large duty cycles lead to enhanced performance. Based on a previous result, a general expression is first obtained that gives mathematical form to the relationship between duty cycle and traffic load for any node in a time-driven EH-WSN fed by a regular energy source. This expression reveals that the duty cycle achievable by a node decreases as its traffic load increases. Then, it is shown that MHC minimizes the average traffic load over the network, and thus it maximizes the average duty cycle of nodes. This result is numerically validated via simulation by comparison with other well-known routing strategies. Accordingly, this paper suggests assigning top priority to the MHC criterion in the development of routing protocols for time-driven EH-WSN under regular energy sources. Full article
(This article belongs to the Section Sensor Networks)
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<p>Uncoupled duty cycles between a transmitter node (T) and a receiver node (R) in asynchronous communication.</p>
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<p>Operation of Low Power Listening (LPL) in TinyOS sensor nodes: Node A transmits a packet to node B, which receives and forwards this packet.</p>
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<p>Relative error between exact and approximate energy consumption models for TinyOS sensor nodes.</p>
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<p>Layer decomposition of a connected network. Lines represent feasible links for the given transmission range. Only inter-layer links are drawn. BS denotes the base station.</p>
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<p>Converting an inter-layer connection into an intra-layer (1) or a backward inter-layer (2) connection.</p>
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<p>Evolution of the average traffic load with regard to the network size, for the routing metrics considered in the analysis. The surprisingly small value obtained for MHC is a consequence of the large network densities managed in the simulation.</p>
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<p>Evolution of the average duty cycle in terms of the network size, for different routing criteria.</p>
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9 pages, 2365 KiB  
Article
Ordinary Optical Fiber Sensor for Ultra-High Temperature Measurement Based on Infrared Radiation
by Qijing Lin, Na Zhao, Kun Yao, Zhuangde Jiang, Bian Tian, Peng Shi and Feng Chen
Sensors 2018, 18(11), 4071; https://doi.org/10.3390/s18114071 - 21 Nov 2018
Cited by 9 | Viewed by 4734
Abstract
An ordinary optical fiber ultra-high temperature sensor based on infrared radiation with the advantages of simple structure and compact is presented. The sensing system consists of a detection fiber and a common transmission fiber. The detector fiber is formed by annealing a piece [...] Read more.
An ordinary optical fiber ultra-high temperature sensor based on infrared radiation with the advantages of simple structure and compact is presented. The sensing system consists of a detection fiber and a common transmission fiber. The detector fiber is formed by annealing a piece of ordinary fiber at high temperature twice, which changes the properties of the fiber and breaks the temperature limit of ordinary fiber. The transmission fiber is a bending insensitive optical fiber. A static calibration system was set up to determine the performance of the sensor and three heating experiments were carried out. The temperature response sensitivities were 0.010 dBm/K, 0.009 dBm/K and 0.010 dBm/K, respectively, which indicate that the sensor has good repeatability. The sensor can withstand a high temperature of 1823 K for 58 h with an error of less than 1%. The main reason why the developed ordinary optical fiber sensor can work steadily for a long time at high temperature is the formation of β-cristobalite, which is stable at high-temperature. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic diagram of a optical fiber infrared radiation sensor.</p>
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<p>Static calibration experiment system of optical fiber infrared radiation sensor.</p>
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<p>The output light intensity of the sensor collected by the spectrometer at different temperatures.</p>
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<p>The temperature response curves of the optical fiber infrared radiation sensor in the three heating processes.</p>
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<p>The temperature response of the optical fiber infrared radiation sensor keeping the temperature of 1823 K for 24 h and 34 h.</p>
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<p>X-ray diffraction patterns of the crystallized optical fiber after two annealing treatments.</p>
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<p>The crystal transformation in the fiber during the process of heating and cooling.</p>
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<p>The crystalline fiber has been generated after annealing.</p>
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20 pages, 5196 KiB  
Article
An Integrated Machine Learning Algorithm for Separating the Long-Term Deflection Data of Prestressed Concrete Bridges
by Xijun Ye, Xueshuai Chen, Yaxiong Lei, Jiangchao Fan and Liu Mei
Sensors 2018, 18(11), 4070; https://doi.org/10.3390/s18114070 - 21 Nov 2018
Cited by 19 | Viewed by 4838
Abstract
Deflection is one of the key indexes for the safety evaluation of bridge structures. In reality, due to the changing operational and environmental conditions, the deflection signals measured by structural health monitoring systems are greatly affected. These ambient changes in the system often [...] Read more.
Deflection is one of the key indexes for the safety evaluation of bridge structures. In reality, due to the changing operational and environmental conditions, the deflection signals measured by structural health monitoring systems are greatly affected. These ambient changes in the system often cover subtle changes in the vibration signals caused by damage to the system. The deflection signals of prestressed concrete (PC) bridges are regarded as the superposition of different effects, including concrete shrinkage, creep, prestress loss, material deterioration, temperature effects, and live load effects. According to multiscale analysis theory of the long-term deflection signal, in this paper, an integrated machine learning algorithm that combines a Butterworth filter, ensemble empirical mode decomposition (EEMD), principle component analysis (PCA), and fast independent component analysis (FastICA) is proposed for separating the individual deflection components from a measured single channel deflection signal. The proposed algorithm consists of four stages: (1) the live load effect, which is a high-frequency signal, is separated from the raw signal by a Butterworth filter; (2) the EEMD algorithm is used to extract the intrinsic mode function (IMF) components; (3) these IMFs are utilized as input in the PCA model and some uncorrelated and dominant basis components are extracted; and (4) FastICA is applied to derive the independent deflection component. The simulated results show that each individual deflection component can be successfully separated when the noise level is under 10%. Verified by a practical application, the algorithm is feasible for extracting the structural deflection (including concrete shrinkage, creep, and prestress loss) only caused by structural damage or material deterioration. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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<p>Flowchart of the proposed algorithm.</p>
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<p>ICA model.</p>
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<p>Hanxi Bridge: finite element model in Midas/Civil.</p>
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<p>The grade-I lane load of the highway.</p>
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<p>Simplified fatigue-load vehicle model. (<b>a</b>) Model <span class="html-italic">M</span><sub>1</sub><span class="html-italic">.</span> (<b>b</b>) Model <span class="html-italic">M</span><sub>2</sub><span class="html-italic">.</span></p>
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<p>Time history and frequency spectrum of live load deflection. (<b>a</b>) Time history data. (<b>b</b>) Frequency spectrum.</p>
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<p>Characteristics of the deflection of temperature effect <span class="html-italic">D<sub>T</sub>.</span> (<b>a</b>) Three years’ time history of temperature effect deflection <span class="html-italic">D<sub>T</sub></span>. (<b>b</b>) Power spectrum of <span class="html-italic">D<sub>T</sub></span>. The sampling frequency is regarded as 1 Hz.</p>
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<p>Deflection effect of shrinkage and creep.</p>
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<p>Deflection effect of the prestress loss (<span class="html-italic">D<sub>p</sub></span>).</p>
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<p>Total deflection of different effects with 10% noise level (not including live load effect).</p>
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<p>EEMD process.</p>
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<p>Three basis vectors after PCA.( U1, U2 and U3 are the first three eigenvectors)</p>
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<p>Individual deflection component after separation. (<b>a</b>) Daily temperature deflection effect (<span class="html-italic">D<sub>T</sub></span><sub>1</sub>). (<b>b</b>) Annual temperature deflection effect (<span class="html-italic">D<sub>T</sub></span><sub>2</sub>). (<b>c</b>) Structural deflection <span class="html-italic">D<sub>V</sub></span> (including <span class="html-italic">D<sub>S</sub>, D<sub>C</sub>,</span> and <span class="html-italic">D<sub>P</sub></span>).</p>
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<p>Individual deflection component after separation. (<b>a</b>) Daily temperature deflection effect (<span class="html-italic">D<sub>T</sub></span><sub>1</sub>). (<b>b</b>) Annual temperature deflection effect (<span class="html-italic">D<sub>T</sub></span><sub>2</sub>). (<b>c</b>) Structural deflection <span class="html-italic">D<sub>V</sub></span> (including <span class="html-italic">D<sub>S</sub>, D<sub>C</sub>,</span> and <span class="html-italic">D<sub>P</sub></span>).</p>
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<p>Deployment of the liquid level sensing system (LLSS).</p>
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<p>Raw real-time monitored deflection data for half an hour.</p>
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<p>Long-term monitored deflection data of one year (averaged every two hours).</p>
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<p>Separated daily temperature deflection effect. (<b>a</b>) Time history data. (<b>b</b>) Power spectrogram.</p>
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<p>Separated annual temperature deflection effect. (<b>a</b>) Time history data. (<b>b</b>) Power spectrogram.</p>
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<p>Separated structural deflection effect (including <span class="html-italic">D<sub>S</sub>, D<sub>C</sub>,</span> and <span class="html-italic">D<sub>P</sub></span>). (<b>a</b>) Time history data. (<b>b</b>) Power spectrogram.</p>
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<p>Separated structural deflection effect (including <span class="html-italic">D<sub>S</sub>, D<sub>C</sub>,</span> and <span class="html-italic">D<sub>P</sub></span>). (<b>a</b>) Time history data. (<b>b</b>) Power spectrogram.</p>
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12 pages, 13062 KiB  
Article
Gaussian Process Based Bayesian Inference System for Intelligent Surface Measurement
by Ming Jun Ren, Chi Fai Cheung and Gao Bo Xiao
Sensors 2018, 18(11), 4069; https://doi.org/10.3390/s18114069 - 21 Nov 2018
Cited by 6 | Viewed by 3907
Abstract
This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to [...] Read more.
This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces. Full article
(This article belongs to the Collection Multi-Sensor Information Fusion)
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<p>An example of GP modelling and prediction.</p>
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<p>Schematic diagram of GP-BIS.</p>
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<p>Design of composite covariance kernel functions.</p>
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<p>Measurement of a designed complex surfaces on multi-sensor instrument. (<b>a</b>) machined workpiece; (<b>b</b>) Benchmarking form error; (<b>c</b>) multi-sensor fused form error.</p>
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10 pages, 2543 KiB  
Article
Designing a Robust Kelvin Probe Setup Optimized for Long-Term Surface Photovoltage Acquisition
by Elke Beyreuther, Stefan Grafström and Lukas M. Eng
Sensors 2018, 18(11), 4068; https://doi.org/10.3390/s18114068 - 21 Nov 2018
Cited by 3 | Viewed by 4178
Abstract
We introduce a robust low-budget Kelvin probe design that is optimized for the long-term acquisition of surface photovoltage (SPV) data, especially developed for highly resistive systems, which exhibit—in contrast to conventional semiconductors—very slow photoinduced charge relaxation processes in the range of hours and [...] Read more.
We introduce a robust low-budget Kelvin probe design that is optimized for the long-term acquisition of surface photovoltage (SPV) data, especially developed for highly resistive systems, which exhibit—in contrast to conventional semiconductors—very slow photoinduced charge relaxation processes in the range of hours and days. The device provides convenient optical access to the sample, as well as high mechanical and electrical stability due to off-resonance operation, showing a noise band as narrow as 1 mV. Furthermore, the acquisition of temperature-dependent SPV transients necessary for SPV-based deep-level transient spectroscopy becomes easily possible. The performance of the instrument is demonstrated by recording long-term SPV transients of the ultra-slowly relaxing model oxide strontium titanate (SrTiO 3 ) over 20 h. Full article
(This article belongs to the Section Physical Sensors)
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<p>Comparative plot of the capacitor plate oscillation <math display="inline"><semantics> <mrow> <mfrac> <msub> <mi>d</mi> <mn>1</mn> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo form="prefix">sin</mo> <mrow> <mo>(</mo> <mi>ω</mi> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (see the configuration sketched in the inset and blue curve), the induced alternating current according to Equation (<a href="#FD2-sensors-18-04068" class="html-disp-formula">2</a>) (black curve), as well as its first two harmonics (gray curves, cf. Equations (<a href="#FD5-sensors-18-04068" class="html-disp-formula">5</a>) and (<a href="#FD6-sensors-18-04068" class="html-disp-formula">6</a>), and see also <a href="#sensors-18-04068-f002" class="html-fig">Figure 2</a>) with numerical values taken from the real experimental setup described in <a href="#sec3-sensors-18-04068" class="html-sec">Section 3</a>: <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mfrac> <mi>π</mi> <mn>4</mn> </mfrac> <mrow> <mo>(</mo> <mn>5</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math><math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>m<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>; <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> V; <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>2</mn> <mo>·</mo> <mi>π</mi> <mo>·</mo> <mn>175</mn> </mrow> </semantics></math> Hz; <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m; <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m.</p>
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<p>(<b>a</b>) Plot of the Kelvin probe current <span class="html-italic">I</span> as given in Equation (<a href="#FD2-sensors-18-04068" class="html-disp-formula">2</a>) for three different ratios <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> </mrow> </semantics></math> with numerical values given already in <a href="#sensors-18-04068-f001" class="html-fig">Figure 1</a>. (<b>b</b>) Logarithmic plot of the region of the amplitude maximum of <span class="html-italic">I</span> showing the decisive influence of the ratio <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> </mrow> </semantics></math> on both the maximum induced current (which can vary over several orders of magnitude) and its asymmetry. Panels (<b>c</b>) and (<b>d</b>) illustrate the decomposition of <span class="html-italic">I</span> into harmonics: while for a comparatively large <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> </mrow> </semantics></math> value, as depicted in (<b>c</b>), more than two harmonics would be needed to rebuild the current, for moderate values as shown in (<b>d</b>)—which corresponds approximately to our real experimental conditions—already the first harmonic represents the current well.</p>
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<p>Mechanical parts: (A) base plate; (B) sample fixed on a (C) block with several (E) bores for optional heating cartridges and temperature sensors; (D) translation stage for sample movement; Parts (F) and (G) represent the probe mount, with (F) being fixed and (G) tiltable around two axes; (H) piezoelectric bimorph clamped between (J) rubber rings within the metallic housing (G) for efficient decoupling of the piezo excitation signal from the probe current; (K) hollow cylinder glued to the piezo, carrying the (L) quartz window with the (M) metal film that serves as the probe. Note that the sketch, which is mainly a cross-section, is not to scale. In a true cross-section, only one of the two probe adjustment screws would be visible, since they are placed at diagonal corners of part (F). The whole construction is surrounded by a grounded metal box (not shown), which serves for both electrical shielding and stray light protection. An entrance slit provides optical access. For photographs of the device, refer to <a href="#app1-sensors-18-04068" class="html-app">Figures S2 and S3</a> in the <a href="#app1-sensors-18-04068" class="html-app">Supplementary Materials</a>.</p>
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<p>Electrical circuitry: The piezoelectric disk bender is excited by a sinusoidal voltage, while the first harmonic of the preamplified probe current is analyzed by a lock-in amplifier. The in-phase part of this first harmonic serves as the input signal for the integrating controller, whose output is fed back to the sample rear contact in order to nullify the probe current. This voltage then corresponds to the contact potential difference between the probe and sample surface. For SPV measurements, light from an illumination setup is coupled through the probe onto the sample. Note that the wiring for the piezo voltage is led through the box at the opposite site of the other signals to prevent crosstalk.</p>
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<p>(<b>a</b>) Temporal development of the SPV of a SrTiO<math display="inline"><semantics> <msub> <mrow/> <mn>3</mn> </msub> </semantics></math> single crystal under on- and off-switching of 600-nm illumination (photon flux: ∼10<math display="inline"><semantics> <mrow> <msup> <mrow/> <mn>13</mn> </msup> <mspace width="0.166667em"/> </mrow> </semantics></math>s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; intensity: 20 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>W illuminating the whole area under the 5-mm-diameter probe, corresponding to 0.1 mW/cm<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, a regime where heating effects can be neglected; see the estimation in [<a href="#B33-sensors-18-04068" class="html-bibr">33</a>]) with grey regions indicating darkness periods and light red regions symbolizing illumination periods, respectively. Diagrams (<b>a</b>–<b>d</b>) show detailed plots of the “light-on” photoresponse, zooming into different time regimes, and reveal the simultaneous presence of several carrier exchange processes with different time constants. The insets in (<b>c</b>) and (<b>d</b>) demonstrate noise bands smaller than 1 mV for both the dark and the illuminated case, being at the resolution limit of the 16-bit A/D converter. Diagrams (<b>e</b>) and (<b>f</b>) visualize the SPV’s relaxation behavior after switching off the illumination. Note that multiple carrier exchange processes are visible, as well, and no recovery to the initial value of <math display="inline"><semantics> <msubsup> <mi>U</mi> <mrow> <mi>C</mi> <mi>P</mi> <mi>D</mi> </mrow> <mrow> <mi>d</mi> <mi>a</mi> <mi>r</mi> <mi>k</mi> </mrow> </msubsup> </semantics></math>, which would correspond to reaching the zero line in the above SPV diagrams, can be observed within the given 20 h.</p>
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23 pages, 8184 KiB  
Article
Accuracy Assessment of Semi-Automatic Measuring Techniques Applied to Displacement Control in Self-Balanced Pile Capacity Testing Appliance
by Zbigniew Muszyński, Jarosław Rybak and Paulina Kaczor
Sensors 2018, 18(11), 4067; https://doi.org/10.3390/s18114067 - 21 Nov 2018
Cited by 16 | Viewed by 3926
Abstract
Static load tests of foundation piles are the basic method for the designing or verification of adopted design solutions which concern the foundation of a building structure. Preparation of a typical test station using the so-called inverted beam method is very expensive and [...] Read more.
Static load tests of foundation piles are the basic method for the designing or verification of adopted design solutions which concern the foundation of a building structure. Preparation of a typical test station using the so-called inverted beam method is very expensive and labor-intensive. The settlement values of the loaded pile are usually recorded using accurate dial gauges. These gauges are attached to a reference beam located in close proximity to the pile under test, which may cause systematic errors (difficult to detect) caused by the displacement of the adopted reference beam. The application of geodetic methods makes it possible to maintain an independent, external reference system, and to verify the readouts from dial gauges. The article presents an innovative instrumentation for a self-balanced stand for the static load test made from a closed-end, double steel pipe. Instead of typical, precise geometric leveling, the semi-automatic measuring techniques were used: motorized total station measurement and terrestrial laser scanning controlled by a computer. The processing of the acquired data made it possible to determine the vertical displacements of both parts of the examined pile and compare displacements with the results from the dial gauges. On the basis of the excess of the collected observations, it was possible to assess the accuracy, which confirmed the usefulness of measuring techniques under study. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning)
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<p>The basic idea of the SLT appliance with anchoring piles.</p>
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<p>The SLT appliance with kentledge formed by water tanks.</p>
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<p>The SLT appliance with wooden reference beams.</p>
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<p>The SLT appliance with steel reference beams.</p>
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<p>The idea of the innovative self-balanced pile testing method.</p>
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<p>The layout of the instrument positions and measured points.</p>
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<p>The geodetic instruments (total station Trimble S3 and laser scanner Leica ScanStation C10) with some reference points and self-balanced testing appliance.</p>
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<p>The self-balanced testing appliance: (<b>a</b>) checkpoints and dial gauges with their numbering; (<b>b</b>) point cloud colored by the intensity of reflection.</p>
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<p>The settlement of the piston (pile foot) represented by point no. 1.</p>
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<p>The settlement of the piston (pile foot) represented by point no. 3.</p>
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<p>The uplift of the pile’s shaft represented by point no. 2.</p>
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<p>The uplift of the pile’s shaft represented by point no. 4.</p>
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<p>The differences of vertical displacement values between the results obtained from the total station S3 and the laser scanner C10.</p>
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<p>The relative displacement of the pile shaft and the piston (pile foot) calculated for a pair of checkpoints no. 1 and no. 4.</p>
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<p>The relative displacement of pile shaft and the piston (pile foot) calculated for a pair of checkpoints no. 2 and no. 3.</p>
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21 pages, 864 KiB  
Article
Evaluation of Strategies for the Development of Efficient Code for Raspberry Pi Devices
by Javier Corral-García, José-Luis González-Sánchez and Miguel-Ángel Pérez-Toledano
Sensors 2018, 18(11), 4066; https://doi.org/10.3390/s18114066 - 21 Nov 2018
Cited by 6 | Viewed by 4141
Abstract
The Internet of Things (IoT) is faced with challenges that require green solutions and energy-efficient paradigms. Architectures (such as ARM) have evolved significantly in recent years, with improvements to processor efficiency, essential for always-on devices, as a focal point. However, as far as [...] Read more.
The Internet of Things (IoT) is faced with challenges that require green solutions and energy-efficient paradigms. Architectures (such as ARM) have evolved significantly in recent years, with improvements to processor efficiency, essential for always-on devices, as a focal point. However, as far as software is concerned, few approaches analyse the advantages of writing efficient code when programming IoT devices. Therefore, this proposal aims to improve source code optimization to achieve better execution times. In addition, the importance of various techniques for writing efficient code for Raspberry Pi devices is analysed, with the objective of increasing execution speed. A complete set of tests have been developed exclusively for analysing and measuring the improvements achieved when applying each of these techniques. This will raise awareness of the significant impact the recommended techniques can have. Full article
(This article belongs to the Special Issue Green Communications and Networking for IoT)
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<p>Percentages of improvement in the runtime achieved by writing efficient code (without compiler optimization).</p>
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<p>Percentages of improvement in the runtime achieved by writing efficient code (with compiler optimization level 3).</p>
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<p>Cascaded function calls. Percentage of improvement in the runtime according to the number of calls to the function (number of iterations of the loop) in RPi 3B+.</p>
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<p>Row-major accessing. Percentage of improvement in the runtime according to the matrix size in RPi 3B+.</p>
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<p>Loop count down. Percentages of improvement in the runtime according to the array size (number of elements) in RPi 3B+.</p>
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<p>Loop unrolling in RPi 3B+. Percentages of improvement in the runtime according to the number of iterations. (<b>a</b>) Array size of 50 elements. (<b>b</b>) Array size of 100 elements. (<b>c</b>) Array size of 200 elements. (<b>d</b>) Array size of 300 elements.</p>
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<p>Linear search with for loops. Percentages of improvement in the runtime according to the array size (number of elements) in RPi 3B+.</p>
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13 pages, 1705 KiB  
Article
Characterization of Sicilian Honeys Pollen Profiles Using a Commercial E-Tongue and Melissopalynological Analysis for Rapid Screening: A Pilot Study
by Ambra R. Di Rosa, Anna M. F. Marino, Francesco Leone, Giuseppe G. Corpina, Renato P. Giunta and Vincenzo Chiofalo
Sensors 2018, 18(11), 4065; https://doi.org/10.3390/s18114065 - 21 Nov 2018
Cited by 23 | Viewed by 4066
Abstract
Honey is usually classified as “unifloral” or “multifloral”, depending on whether a dominating pollen grain, originating from only one particular plant, or no dominant pollen type in the sample is found. Unifloral honeys are usually more expensive and appreciated than multifloral honeys, which [...] Read more.
Honey is usually classified as “unifloral” or “multifloral”, depending on whether a dominating pollen grain, originating from only one particular plant, or no dominant pollen type in the sample is found. Unifloral honeys are usually more expensive and appreciated than multifloral honeys, which highlights the importance of honey authenticity. Melissopalynological analysis is used to identify the botanical origin of honey, counting down the number of pollens grains of a honey sample, and calculating the respective percentages of the nectariferous pollens. In addition, sensory properties are also very important for honey characterization, and electronic senses emerged as useful tools for honey authentication. In this work, a comparison of the results obtained from melissopalynological analysis with those provided by a potentiometric electronic tongue is given, resulting in a 100% match between the two techniques. Full article
(This article belongs to the Section Chemical Sensors)
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<p>Drawing pins indicates the areas from where the honey samples have been acquired.</p>
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<p>Principal component analysis (PCA) obtained for the different honey varieties. The coloured straight lines indicates the boundaries of each group.</p>
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<p>Soft Independent Modeling Class Analogy (SIMCA) model for Chestnut honeys.</p>
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<p>Soft Independent Modeling Class Analogy (SIMCA) model for Eucalyptus honeys.</p>
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<p>SIMCA model for (<b>a</b>) Sulla honeys and (<b>b</b>) Citrus honeys.</p>
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<p>Pollen grains for (<b>a</b>) <span class="html-italic">Castanea</span>, (<b>b</b>) <span class="html-italic">Eucalyptus</span>, (<b>c</b>) <span class="html-italic">Hedysarium</span> and (<b>d</b>) <span class="html-italic">Citrus</span>.</p>
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9 pages, 2158 KiB  
Article
Comparison of CO2 Vertical Profiles in the Lower Troposphere between 1.6 µm Differential Absorption Lidar and Aircraft Measurements Over Tsukuba
by Yasukuni Shibata, Chikao Nagasawa, Makoto Abo, Makoto Inoue, Isamu Morino and Osamu Uchino
Sensors 2018, 18(11), 4064; https://doi.org/10.3390/s18114064 - 21 Nov 2018
Cited by 7 | Viewed by 5865
Abstract
A 1.6 μm differential absorption Lidar (DIAL) system for measurement of vertical CO2 mixing ratio profiles has been developed. A comparison of CO2 vertical profiles measured by the DIAL system and an aircraft in situ sensor in January 2014 over the [...] Read more.
A 1.6 μm differential absorption Lidar (DIAL) system for measurement of vertical CO2 mixing ratio profiles has been developed. A comparison of CO2 vertical profiles measured by the DIAL system and an aircraft in situ sensor in January 2014 over the National Institute for Environmental Studies (NIES) in Tsukuba, Japan, is presented. The DIAL measurement was obtained at an altitude range of between 1.56 and 3.60 km with a vertical resolution of 236 m (below 3 km) and 590 m (above 3 km) at an average error of 1.93 ppm. An in situ sensor for cavity ring-down spectroscopy of CO2 was installed in an aircraft. CO2 mixing ratio measured by DIAL and the aircraft sensor ranged from 398.73 to 401.36 ppm and from 399.08 to 401.83 ppm, respectively, with an average difference of −0.94 ± 1.91 ppm below 3 km and −0.70 ± 1.98 ppm above 3 km between the two measurements. Full article
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<p>Schematic illustration of the differential absorption Lidar (DIAL) system.</p>
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<p>Block diagram of the 1.6 µm DIAL system for measurement of CO<sub>2</sub> mixing ratio profiles. The low-altitude mode measurements were performed using a 25-cm-diameter telescope and the high-altitude mode measurements were done using a 60-cm-diameter telescope.</p>
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<p>Observation sites where CO<sub>2</sub> DIAL (<b>right</b>) and aircraft measurements (<b>left</b>) were made on 12 January 2014 over Tsukuba.</p>
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<p>GPS sonde temperature (<b>left</b>) and pressure (<b>right</b>) profiles launched from Tsukuba on 12 January 2014.</p>
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<p>On-line and off-line return signals with high-altitude mode and low-altitude mode. The range resolution is 7.5 m.</p>
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<p>Comparison of CO<sub>2</sub> vertical mixing ratio profiles observed by DIAL and aircraft. dz: vertical resolution.</p>
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18 pages, 2552 KiB  
Article
A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability
by Zheng Li, Jingbin Liu, Fan Yang, Xiaoguang Niu, Leilei Li, Zemin Wang and Ruizhi Chen
Sensors 2018, 18(11), 4063; https://doi.org/10.3390/s18114063 - 21 Nov 2018
Cited by 21 | Viewed by 5238
Abstract
Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, [...] Read more.
Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, a classical Wi-Fi-based indoor positioning method, consists of two phases: radio map learning and position inference. Thus far, the application of Bayesian fingerprinting positioning is limited due to its poor usability; radio map learning requires an adequate number of received signal strength indication (RSSI) observables at each reference point, long-term fieldwork, and high development and maintenance costs. In this paper, based on a statistical analysis of actual RSSI observables, a Weibull–Bayesian density model is proposed to represent the probability density of Wi-Fi RSSI observables. The Weibull model, which is parameterized with three parameters that can be calculated with fewer samples, can calculate the probability density with a higher accuracy than the traditional histogram method. Furthermore, the parameterized Weibull model can simplify the radio map by storing only three parameters that can restore the whole probability density, i.e., it is not necessary to store the probability distribution based on traditionally separated RSSI bins. Bayesian positioning inference is performed in the positioning phase using probability density rather than the traditional probability distribution of predefined RSSI bins. The proposed method was implemented on an Android smartphone, and the performance was evaluated in different indoor environments. Results revealed that the proposed method enhanced the usability of Wi-Fi Bayesian fingerprinting positioning by requiring fewer RSSI observables and improved the positioning accuracy by 19–32% in different building environments compared with the classic histogram-based method, even when more samples were used. Full article
(This article belongs to the Collection Positioning and Navigation)
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<p>Comparison of probability distributions: (<b>a</b>) a typical comparison of the RSSI probability density derived with the histogram (<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>O</mi> </mrow> <mo stretchy="false">¯</mo> </mover> </mrow> </semantics></math> = 74, STD = 3.186) and Weibull signal model (cyan line, referring to the right axis); (<b>b</b>) Weibull-based probability distribution (<math display="inline"><semantics> <mrow> <mi>k</mi> </mrow> </semantics></math> = 2.5, λ = 8.4428, <span class="html-italic">θ</span> = 67) with 17,874 samples (blue line) vs. the histogram probability distribution with 17,874 samples (red line). The RSSI is in the unit of –dBm in this paper.</p>
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<p>The (<b>a</b>) positioning error and (<b>b</b>) cumulative distribution function (CDFs) of different RSSI ranges.</p>
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<p>Second-floor plan: Area (<b>A</b>) is the corridor between the students’ computer labs, which is characterized by a large flow of people, and is large, and has a complex Wi-Fi signal environment. Area (<b>B</b>) is a large, spacious lobby with fewer Wi-Fi signals. Area (<b>C</b>) is the corridor between the teacher’s office characterized by a simple physical environment and a simple Wi-Fi signal environment. The area in the first picture (where there is no reference point) is private and cannot be tested.</p>
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<p>Fourth-floor plan: Area (<b>A</b>) is a large conference room that is irregular and has fewer Wi-Fi signals. Areas (<b>B</b>,<b>C</b>) are different types of corridors with simple physical environments and simple Wi-Fi signal environments. The area in the first picture (where there is no reference point) is private and cannot be tested.</p>
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<p>Comparison of probability distributions: (<b>a</b>) Weibull-based probability distribution with 20 samples (cyan line) vs. Weibull-based probability distribution with 30 samples (blue line) vs. histogram probability distribution with 30 samples (green line) vs. histogram probability distribution with all samples (red line). (<b>b</b>) Weibull-based probability distribution with 20 samples (cyan line) vs. Weibull-based probability distribution with 30 samples (blue line) vs. histogram probability distribution with 30 samples (green line) vs. Weibull-based probability distribution with all samples (red line).</p>
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<p>(<b>a</b>) Probability densities estimated using all samples (red line) and each probability density function (PDF) estimated with the Weibull signal model for all sessions with sets of 30 RSSI measurement samples (cluster of green lines). (<b>b</b>) The cyan dashed line connects the mean of the value of all sessions plus the variance of the value of all sessions (magenta triangles), the mean of the value of all sessions (blue stars), and the mean of the value of all sessions minus the variance of the value of all sessions (inverted magenta triangles); the red line is the baseline distribution.</p>
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<p>The (<b>a</b>,<b>b</b>) CDFs of the three algorithms on the second floor.</p>
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<p>The (<b>a</b>,<b>b</b>) CDFs of the three algorithms on the fourth floor.</p>
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14 pages, 4567 KiB  
Article
Validation of Finite Element Model by Smart Aggregate-Based Stress Monitoring
by Haibin Zhang, Shuang Hou and Jinping Ou
Sensors 2018, 18(11), 4062; https://doi.org/10.3390/s18114062 - 21 Nov 2018
Cited by 4 | Viewed by 4088
Abstract
Concrete compressive strength is an important parameter of material properties for assessing seismic performance of reinforced concrete (RC) structures, which has a certain level of uncertainty due to its inherent variability. In this paper, the method of concrete strength validation of finite element [...] Read more.
Concrete compressive strength is an important parameter of material properties for assessing seismic performance of reinforced concrete (RC) structures, which has a certain level of uncertainty due to its inherent variability. In this paper, the method of concrete strength validation of finite element model using smart aggregate (SA)-based stress monitoring is proposed. The FE model was established using Open System for Earthquake Engineering Simulation (OpenSEES) platform. The concrete strengths obtained from the material test, peak stress of SA, and estimated concrete strength based on SA stress were employed in FE models. The lateral displacement monitored by Liner variable differential transformer and vertical axial load monitored by load cell in the experiment are applied in the model. By comparing the global response (i.e., lateral reaction force and hysteretic loop), local response (i.e., concrete stress, rebar strain, and cross-section moment) and corresponding root-mean-square error obtained from experiment and numerical analysis, the capabilities of validation of FE model using SA-based stress monitoring method were demonstrated. Full article
(This article belongs to the Special Issue Recent Advances of Piezoelectric Transducers and Applications)
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<p>Illustration and photo of the SA.</p>
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<p>(<b>a</b>) Finite element model; and (<b>b</b>) division of cross section and SA position of RC column.</p>
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<p>Envelope curve for rebar stress versus loaded-end slip relationship.</p>
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<p>Material constitutive models: (<b>a</b>) steel; and (<b>b</b>) concrete.</p>
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<p>Concrete stress distribution and rectangular stress block.</p>
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<p>Loading scheme for: (<b>a</b>) lateral direction; and (<b>b</b>) vertical direction.</p>
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<p>The time-history of the lateral load.</p>
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<p>Hysteretic responses of the specimens: (<b>a</b>) hysteretic loops; and (<b>b</b>) envelop curves.</p>
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<p>Stress time-history for experiment and finite element analysis at: (<b>a</b>) Point I; (<b>b</b>) Point II; (<b>c</b>) Point III; (<b>d</b>) Point IV; and (<b>e</b>) Point V.</p>
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<p>Strain time-history for different finite element model at different position along lateral loading direction: (<b>a</b>) −149 mm; and (<b>b</b>) 149 mm.</p>
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<p>Time-history of the cross-section moment for experimental and analytical results.</p>
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<p>Root-mean-square error of lateral load.</p>
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<p>Root-mean-square error of concrete stress at the location of: (<b>a</b>) Point I; (<b>b</b>) Point V; (<b>c</b>) Point II, (<b>d</b>) Point IV; and (<b>e</b>) Point III.</p>
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<p>Root-mean-square error of rebar strain at different position along lateral loading direction: (<b>a</b>) −149 mm; and (<b>b</b>) 149 mm.</p>
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<p>Root-mean-square error of cross-section moment.</p>
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12 pages, 3820 KiB  
Article
A Tellurium Oxide Microcavity Resonator Sensor Integrated On-Chip with a Silicon Waveguide
by Henry C. Frankis, Daniel Su, Dawson B. Bonneville and Jonathan D. B. Bradley
Sensors 2018, 18(11), 4061; https://doi.org/10.3390/s18114061 - 21 Nov 2018
Cited by 6 | Viewed by 5473
Abstract
We report on thermal and evanescent field sensing from a tellurium oxide optical microcavity resonator on a silicon photonics platform. The on-chip resonator structure is fabricated using silicon-photonics-compatible processing steps and consists of a silicon-on-insulator waveguide next to a circular trench that is [...] Read more.
We report on thermal and evanescent field sensing from a tellurium oxide optical microcavity resonator on a silicon photonics platform. The on-chip resonator structure is fabricated using silicon-photonics-compatible processing steps and consists of a silicon-on-insulator waveguide next to a circular trench that is coated in a tellurium oxide film. We characterize the device’s sensitivity by both changing the temperature and coating water over the chip and measuring the corresponding shift in the cavity resonance wavelength for different tellurium oxide film thicknesses. We obtain a thermal sensitivity of up to 47 pm/°C and a limit of detection of 2.2 × 10−3 RIU for a device with an evanescent field sensitivity of 10.6 nm/RIU. These results demonstrate a promising approach to integrating tellurium oxide and other novel microcavity materials into silicon microphotonic circuits for new sensing applications. Full article
(This article belongs to the Special Issue Resonator Sensors 2018)
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<p>(<b>a</b>) Top view drawing of the resonator sensor showing the pulley-coupled silicon bus waveguide (red) and the TeO<sub>2</sub> microcavity (green). (<b>b</b>) Cross-section schematic of the device through the section indicated by the dashed line in (<b>a</b>), showing the silicon bus waveguide and the TeO<sub>2</sub> resonator layer coated into the trench. (<b>c</b>) Calculated fundamental transverse electric (TE) polarized electric field mode profiles for the TeO<sub>2</sub> resonator and silicon bus waveguide in the region indicated by the dashed line in (<b>b</b>). (<b>d</b>) Focused ion beam (FIB) scanning electron microscope (SEM) cross-section image of a fabricated device showing the realized structure.</p>
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<p>Diagram of the optical setup used to characterize the devices.</p>
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<p>Resonance spectra measured at temperatures ranging from 20 to 40 °C for the microcavity with a 1100-nm-thick TeO<sub>2</sub> film, showing shifting of the resonance wavelength.</p>
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<p>Measured wavelength shift versus temperature for 480-, 900-, and 1100-nm-thick TeO<sub>2</sub> cavities, fitted to have thermal sensitivities of 28, 47, and 30 pm/°C, respectively.</p>
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<p>Resonance spectra measured in air (pink) and after covering the chip in water (blue) for (<b>a</b>) 900-nm-thick and (<b>b</b>) 1100-nm-thick TeO<sub>2</sub> resonators.</p>
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<p>Measured resonance shift vs. cladding refractive index for a 900-nm-thick TeO<sub>2</sub> microcavity coated in solutions with varying concentrations of glycerol and water.</p>
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<p>Cavity resonance modes around 1600 nm measured in air (fit with red line) and after coating the chip in water (fit with blue line) for (<b>a</b>) 900-nm-thick and (<b>b</b>) 1100-nm-thick TeO<sub>2</sub> resonators, demonstrating resonance broadening in water.</p>
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<p>Simulated (<b>a</b>) wavelength shift and (<b>b</b>) RIU sensitivity vs. evanescent medium refractive index for cavities with TeO<sub>2</sub> coating thicknesses ranging from 0.5 to 1.1 μm.</p>
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17 pages, 2530 KiB  
Article
Evaluation of Object Surface Edge Profiles Detected with a 2-D Laser Scanning Sensor
by Tingting Yan, Xiaochan Wang, Heping Zhu and Peter Ling
Sensors 2018, 18(11), 4060; https://doi.org/10.3390/s18114060 - 21 Nov 2018
Cited by 7 | Viewed by 4650
Abstract
Canopy edge profile detection is a critical component of plant recognition in variable-rate spray control systems. The accuracy of a high-speed 270° radial laser sensor was evaluated in detecting the surface edge profiles of six complex-shaped objects. These objects were toy balls with [...] Read more.
Canopy edge profile detection is a critical component of plant recognition in variable-rate spray control systems. The accuracy of a high-speed 270° radial laser sensor was evaluated in detecting the surface edge profiles of six complex-shaped objects. These objects were toy balls with a pink smooth surface, light brown rectangular cardboard boxes, black and red texture surfaced basketballs, white smooth cylinders, and two different sized artificial plants. Evaluations included reconstructed three-dimensional (3-D) images for the object surfaces with the data acquired from the laser sensor at four different detection heights (0.25, 0.50, 0.75, and 1.00 m) above each object, five sensor travel speeds (1.6, 2.4, 3.2, 4.0, and 4.8 km h−1), and 8 to 15 horizontal distances to the sensor ranging from 0 to 3.5 m. Edge profiles of the six objects detected with the laser sensor were compared with images taken with a digital camera. The edge similarity score (ESS) was significantly affected by the horizontal distances of the objects, and the influence became weaker when the objects were placed closer to each other. The detection heights and travel speeds also influenced the ESS slightly. The overall average ESS ranged from 0.38 to 0.95 for all the objects under all the test conditions, thereby providing baseline information for the integration of the laser sensor into future development of greenhouse variable-rate spray systems to improve pesticide, irrigation, and nutrition application efficiencies through watering booms. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning)
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<p>The 270° radial range laser sensor mounted on a constant-speed track to detect six rows of different objects (artificial plant 1, artificial plant 2, basketball, rectangular box, toy ball, and cylinder).</p>
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<p>Geometry analysis of the laser beam points transmitted on the object surface on one side of the sensor.</p>
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<p>Calculated detection resolution of the laser sensor along the horizontal direction (DRH) with Equation (1) for different horizontal distances and detection heights.</p>
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<p>Processing edge similarity score (ESS) for paired images of eight 0.5 m evenly spaced objects obtained from the camera (upper) and laser sensor (lower): (<b>a</b>) toy ball, (<b>b</b>) basketball, (<b>c</b>) rectangular box, (<b>d</b>) cylinder, (<b>e</b>) artificial plant 1, and (<b>f</b>) artificial plant 2. The reconstructed images from the laser sensor were taken at 3.2 km h<sup>−1</sup> travel speed and 0.5 m detection height. Different colors represent different vertical distances between the object surfaces and the laser sensor.</p>
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<p>Processing edge similarity score (ESS) for paired images of eight 0.5 m evenly spaced objects obtained from the camera (upper) and laser sensor (lower): (<b>a</b>) toy ball, (<b>b</b>) basketball, (<b>c</b>) rectangular box, (<b>d</b>) cylinder, (<b>e</b>) artificial plant 1, and (<b>f</b>) artificial plant 2. The reconstructed images from the laser sensor were taken at 3.2 km h<sup>−1</sup> travel speed and 0.5 m detection height. Different colors represent different vertical distances between the object surfaces and the laser sensor.</p>
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<p>Processing ESS for paired images of eight 0.25 m evenly spaced objects obtained from the camera (upper) and laser sensor (lower): (<b>a</b>) toy ball, (<b>b</b>) artificial plant 1. The reconstructed images from the laser sensor were taken at 3.2 km h<sup>−1</sup> travel speed and 0.5 m detection height. Different colors represent different vertical distances between the object surfaces and the laser sensor.</p>
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13 pages, 2849 KiB  
Article
Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation
by Benny Chitambira, Simon Armour, Stephen Wales and Mark Beach
Sensors 2018, 18(11), 4059; https://doi.org/10.3390/s18114059 - 20 Nov 2018
Cited by 3 | Viewed by 3344
Abstract
This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, [...] Read more.
This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better. Full article
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<p>Multipath rays for a BS-MS point-to-point link.</p>
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<p>Base station deployment showing the coverage radius.</p>
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<p>Ground reflected multipath (red dashed path).</p>
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<p>Obtaining the second coordinate for LOS scenarios [<a href="#B3-sensors-18-04059" class="html-bibr">3</a>].</p>
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<p>Outlier removal.</p>
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<p>(<b>a</b>) Dense urban area/city center (sampled color-coded positions: same color means positions with same received signal power). (<b>b</b>) Park/farmland, showing trees and open areas [<a href="#B3-sensors-18-04059" class="html-bibr">3</a>].</p>
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<p>(<b>a</b>) Localisation performance for the two environments. (<b>b</b>) Sensitivity to measurement errors.</p>
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<p>Direct method vs. TOA/AOA.</p>
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<p>TDOA vs. Direct method in an urban environment.</p>
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<p>TDOA vs. Direct method in a LOS environment.</p>
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12 pages, 1684 KiB  
Article
Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment
by Hua Liu and Qihao Weng
Sensors 2018, 18(11), 4058; https://doi.org/10.3390/s18114058 - 20 Nov 2018
Cited by 29 | Viewed by 4181
Abstract
There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. [...] Read more.
There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. This study simulated LSTs based on observed Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST imagery with Spatial and Temporal Adaptive Reflectance Fusion Model, and investigated the downscaling effect of LST image fusion at 15, 30, 60, 90, 120, 250, 500, and 1000 m spatial resolutions. The study area partially covered the City of Los Angeles, California, USA, and surrounding areas. The reference images (observed ASTER and MODIS LST imagery) were acquired on 04/03/2007 and 07/01/2007, with simulated LSTs produced for 4/28/2007. Three image resampling methods (Cubic Convolution, Bilinear Interpolation, and Nearest Neighbor) were used during the downscaling and upscaling processes, and the resulting LST simulations were compared. Results indicated that the observed ASTER LST and simulated ASTER LST images (date 04/28/2007, spatial resolution 90 m) had high agreement in terms of spatial variations and basic statistics based on a comparison between the observed and simulated ASTER LST maps. Urban developed lands possessed higher LSTs with lighter tones and mountainous areas showed dark tones with lower LSTs. The Cubic Convolution and Bilinear Interpolation resampling methods yielded better results over Nearest Neighbor resampling method across the scales from 15 to 1000 m. The simulated LSTs with image fusion can be used as valuable inputs in heat related studies that require frequent LST measurements with fine spatial resolutions, e.g., seasonal movements of urban heat islands, monthly energy budget assessment, and temperature-driven epidemiology. The observation of scale-independency of the proposed image fusion method can facilitate with image selections of LST studies at various locations. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Land-Cover and Land-Use Changes)
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<p>Geographical location of the study area.</p>
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<p>Simulated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-like land surface temperatures (LST) image on date 04/28/2007 (90 m spatial resolution).</p>
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<p>A comparison between observed (<b>left</b>) and simulated (<b>middle</b>) ASTER LST image on date 04/28/2007. The map (<b>right</b>) shows the difference between observed and simulated images. Spatial resolution: 90 m.</p>
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<p>Scatter plots between observed and simulated ASTER LST datasets at mountain and urban areas on date 04/28/2007. Temperature units: K. Spatial resolution: 15 m.</p>
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<p>Simulated LST images across the scales.</p>
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13 pages, 1118 KiB  
Article
NLOS Identification in WLANs Using Deep LSTM with CNN Features
by Viet-Hung Nguyen, Minh-Tuan Nguyen, Jeongsik Choi and Yong-Hwa Kim
Sensors 2018, 18(11), 4057; https://doi.org/10.3390/s18114057 - 20 Nov 2018
Cited by 21 | Viewed by 4778
Abstract
Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This [...] Read more.
Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model. Full article
(This article belongs to the Section Sensor Networks)
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<p>The experiment setup.</p>
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<p>The overall framework of the proposed CNNLSTM.</p>
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<p>Proposed one-dimensional CNN model.</p>
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<p>The structure of the LSTM.</p>
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<p>Performance convergence versus epochs.</p>
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<p>Performances of the models depend on the number of packets.</p>
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<p>ROC curves of LOS identification using the proposed CNNLSTM and other methods.</p>
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<p>tSNE representation of 5000 training samples for (<b>a</b>) proposed CNNLSTM and (<b>b</b>) conventional LSTM.</p>
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14 pages, 464 KiB  
Article
Message Integration Authentication in the Internet-of-Things via Lattice-Based Batch Signatures
by Xiuhua Lu, Wei Yin, Qiaoyan Wen, Kaitai Liang, Liqun Chen and Jiageng Chen
Sensors 2018, 18(11), 4056; https://doi.org/10.3390/s18114056 - 20 Nov 2018
Cited by 4 | Viewed by 3344
Abstract
The internet-of-things (also known as IoT) connects a large number of information-sensing devices to the Internet to collect all kinds of information needed in real time. The reliability of the source of a large number of accessed information tests the processing speed of [...] Read more.
The internet-of-things (also known as IoT) connects a large number of information-sensing devices to the Internet to collect all kinds of information needed in real time. The reliability of the source of a large number of accessed information tests the processing speed of signatures. Batch signature allows a signer to sign a group of messages at one time, and signatures’ verification can be completed individually and independently. Therefore, batch signature is suitable for data integration authentication in IoT. An outstanding advantage of batch signature is that a signer is able to sign as many messages as possible at one time without worrying about the size of signed messages. To reduce complexity yielded by multiple message signing, a binary tree is usually leveraged in the construction of batch signature. However, this structure requires a batch residue, making the size of a batch signature (for a group of messages) even longer than the sum of single signatures. In this paper, we make use of the intersection method from lattice to propose a novel generic method for batch signature. We further combine our method with hash-and-sign paradigm and Fiat–Shamir transformation to propose new batch signature schemes. In our constructions, a batch signature does not need a batch residue, so that the size of the signature is relatively smaller. Our schemes are securely proved to be existential unforgeability against adaptive chosen message attacks under the small integer solution problem, which shows great potential resisting quantum computer attacks. Full article
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things)
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<p>The Schematic of the Binary Tree.</p>
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<p>The Schematic of the Intersection Method.</p>
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<p>Data Flow Diagram in Wireless Body Sensor Network.</p>
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41 pages, 12398 KiB  
Article
Adding Active Slot Joint Larger Broadcast Radius for Fast Code Dissemination in WSNs
by Wei Yang, Wei Liu, Zhiwen Zeng, Anfeng Liu, Guosheng Huang, Neal N. Xiong and Zhiping Cai
Sensors 2018, 18(11), 4055; https://doi.org/10.3390/s18114055 - 20 Nov 2018
Cited by 13 | Viewed by 4580
Abstract
By using Software Defined Network (SDN) technology, senor nodes can get updated program code which can provide new features, so it has received extensive attention. How to effectively spread code to each node fast is a challenge issue in wireless sensor networks (WSNs). [...] Read more.
By using Software Defined Network (SDN) technology, senor nodes can get updated program code which can provide new features, so it has received extensive attention. How to effectively spread code to each node fast is a challenge issue in wireless sensor networks (WSNs). In this paper, an Adding Active Slot joint Larger Broadcast Radius (AAS-LBR) scheme is proposed for fast code dissemination. The AAS-LBR scheme combines the energy of data collection and code dissemination, making full use of the remaining energy in the far-sink area to increase the active slot and the broadcast radius to speed up the code dissemination. The main contributions of the proposed AAS-LBR scheme are the following: (1) Make full use of the remaining energy of the far sink area to expand the broadcast radius, so that the node broadcasts a longer distance. The wide range of broadcasts makes the number of nodes receiving code more, which speeds up the spread of code dissemination. (2) AAS-LBR uses two improved methods to further reduce the number of broadcasts and speed up the code dissemination: (a) When constructing the broadcast backbone whose nodes dominate all nodes in network and are responsible for broadcasting code, the active slot is added to the next hop node in a pipeline style on the diffusion path, which enables the code dissemination process to continue without pause. Thus, the code can quickly spread to the entire broadcast backbone. (b) For the nodes in the non-broadcast backbone whose nodes are dominated by the broadcast backbone and only for receiving code, an active slot is added coincident with its broadcast backbone’ active slot, which can reduce the time required for code dissemination and reduce the number of broadcasts. A lot of performance analysis and simulation results show that compared to previous schemed, the AAS-LBR scheme can balance energy consumption, the transmission delay can be reduced 43.09–78.69%, the number of broadcasts can be reduced 44.51–86.18% and the energy efficiency is improved by about 24.5%. Full article
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<p>Code dissemination method in loss and low duty cycle-based WSNS.</p>
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<p>3 different code dissemination strategy diagrams.</p>
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<p>Example of periodical wake up of node <span class="html-italic">v</span>.</p>
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<p>The duty cycle of node.</p>
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<p>The codes dissemination in duty cycle based wireless sensor network.</p>
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<p>The time slot with an added active time slot.</p>
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<p>The network topology after increasing the radius.</p>
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<p>The network topology and slot status after increasing active and increasing radius.</p>
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<p>The amount of data that the node assumes decreases as the distance from the sink node decreases.</p>
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<p>The remaining energy of the node can support increasing the active slot and increasing the radius.</p>
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<p>Node that can add a time slot and is closest to the sink.</p>
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<p>(<b>a</b>) Transmission power at different distances from sink; (<b>b</b>) broadcast radius at different distances from sink; (<b>c</b>) energy consumption of transmission unit data packets.</p>
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<p>Original physical link.</p>
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<p>Physical link for AAS-LBR.</p>
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<p>Minimum Covering Node Set.</p>
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<p>Building covering sub-tree.</p>
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<p>Case 1 and case 2 in finalizing backbone.</p>
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<p>Connection T1 to T0 using one-hop and two-hop forwarders.</p>
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<p>Preliminary broadcast backbone of AAS-LBR scheme.</p>
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<p>Broadcast backbone of AAS-LBR scheme.</p>
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<p>Physical link for ABRCD.</p>
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<p>Minimum covering node set.</p>
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<p>Broadcast backbone for ABRCD.</p>
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<p>Topology diagram after adjusting node time slot.</p>
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<p>Energy consumption of nodes 1–20 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Energy consumption of nodes 20–200 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Energy consumption of nodes 400–500 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Energy consumption of nodes 1–20 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Energy consumption of nodes 20–200 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Energy consumption of nodes 400–500 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Energy consumption of nodes 1–20 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Energy consumption of nodes 20–200 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Energy consumption of nodes 400–500 m away from sink under <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>28</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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<p>Total energy consumption of three schemes under different cycles with <span class="html-italic">r</span> = 28 m.</p>
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<p>Network lifetime of three schemes under different cycles with <span class="html-italic">r</span> = 28 m.</p>
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<p>Effective energy utilization of three schemes under different cycles with <span class="html-italic">r</span> = 28 m.</p>
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<p>The impact of network size with fixed |<span class="html-italic">T</span>| = 30 on the number of broadcasts.</p>
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<p>The impact of network size with fixed <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>T</mi> <mo>|</mo> </mrow> </mrow> </semantics></math> = 90 on the number of broadcasts.</p>
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<p>The impact of network size with fixed <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>T</mi> <mo>|</mo> </mrow> </mrow> </semantics></math>= 150 on the number of broadcasts.</p>
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<p>The impact of network size with fixed value of <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>T</mi> <mo>|</mo> </mrow> </mrow> </semantics></math>= 90 on the total number of broadcast.</p>
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<p>The impact of <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>T</mi> <mo>|</mo> </mrow> </mrow> </semantics></math> with fixed network nodes = 200 with fixed on the total number of broadcast.</p>
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<p>The impact of <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>T</mi> <mo>|</mo> </mrow> </mrow> </semantics></math> with fixed network nodes = 400 with fixed on the total number of broadcast.</p>
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<p>The impact of <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>T</mi> <mo>|</mo> </mrow> </mrow> </semantics></math> with fixed network nodes = 600 with fixed on the total number of broadcast.</p>
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<p>The impact of network size with fixed value of |<span class="html-italic">T</span>| = 30 on the broadcast delay.</p>
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<p>The impact of network size with fixed value of |<span class="html-italic">T</span>| = 90 on the broadcast delay.</p>
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<p>The impact of network size with fixed value of |<span class="html-italic">T</span>| = 150 on the broadcast delay.</p>
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<p>The impact of |<span class="html-italic">T</span>| with fixed network nodes = 400 with fixed on the delay.</p>
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<p>The impact of |<span class="html-italic">T</span>| with fixed network nodes = 600 with fixed on the delay.</p>
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<p>The impact of |<span class="html-italic">T</span>| with fixed network nodes = 900 with fixed on the delay.</p>
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<p>The impact of |<span class="html-italic">T</span>| with fixed network nodes = 800 with fixed on the total number of transmissions.</p>
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16 pages, 4531 KiB  
Article
Intersection Intelligence: Supporting Urban Platooning with Virtual Traffic Lights over Virtualized Intersection-Based Routing
by José Víctor Saiáns-Vázquez, Esteban Fernando Ordóñez-Morales, Martín López-Nores, Yolanda Blanco-Fernández, Jack Fernando Bravo-Torres, José Juan Pazos-Arias, Alberto Gil-Solla and Manuel Ramos-Cabrer
Sensors 2018, 18(11), 4054; https://doi.org/10.3390/s18114054 - 20 Nov 2018
Cited by 13 | Viewed by 4927
Abstract
The advent of the autonomous car is paving the road to the realization of ideas that will help optimize traffic flows, increase safety and reduce fuel consumption, among other advantages. We present one proposal to bring together Virtual Traffics Lights (VTLs) and platooning [...] Read more.
The advent of the autonomous car is paving the road to the realization of ideas that will help optimize traffic flows, increase safety and reduce fuel consumption, among other advantages. We present one proposal to bring together Virtual Traffics Lights (VTLs) and platooning in urban scenarios, leaning on vehicle-to-vehicle (V2V) communication protocols that turn intersections into virtual containers of data. Newly-introduced protocols for the combined management of VTLs and platoons are validated by simulation, comparing a range of routing protocols for the vehicular networks with the baseline given by common deployments of traditional traffic lights ruled by state-of-the-art policies. The simulation results show that the combination of VTLs and platoons can achieve significant reductions in travel times and fuel consumption, provided that proper algorithms are used to handle the V2V communications. Full article
(This article belongs to the Special Issue Advances on Vehicular Networks: From Sensing to Autonomous Driving)
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<p>The stack of protocols of our approach.</p>
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<p>The intersection-based layout of the VaNetLayer (the circles represent vehicles).</p>
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<p>Platoon announcement along the L1VNs in the platoon’s route.</p>
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<p>The state machine of the procedure used to choose a VTL leader.</p>
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<p>Join maneuver coordinated by the VTL protocol.</p>
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<p>Architecture of our VEINS-based simulator.</p>
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21 pages, 551 KiB  
Article
Adaptive Data Synchronization Algorithm for IoT-Oriented Low-Power Wide-Area Networks
by Andrea Petroni, Francesca Cuomo, Leonisio Schepis, Mauro Biagi, Marco Listanti and Gaetano Scarano
Sensors 2018, 18(11), 4053; https://doi.org/10.3390/s18114053 - 20 Nov 2018
Cited by 12 | Viewed by 4683
Abstract
The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service [...] Read more.
The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications. Full article
(This article belongs to the Special Issue Green Communications and Networking for IoT)
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<p>Typical IoT scenario where multiple devices are directly connected to the Cloud in a LPWAN.</p>
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<p><span class="html-italic">rsync</span> procedure between DIoT and Cloud.</p>
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<p>Examples of occurrences during file synchronization.</p>
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<p>Cloud file update according to the scenario in <a href="#sensors-18-04053-f003" class="html-fig">Figure 3</a>c.</p>
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<p>Delta structure in original <span class="html-italic">rsync</span>, AC-<span class="html-italic">rdiff</span> and AH-<span class="html-italic">rdiff</span> respectively.</p>
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<p>Performance of the considered file synchronization algorithms as a function of the file update percentage. Random update distribution within the file is assumed.</p>
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<p>Performance of the considered file synchronization algorithms as a function of the file update percentage. Burst updates distribution within the file is assumed.</p>
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<p>Chunk dimension adaptation as the synchronization events occur.</p>
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<p>Power consumption, measured in milliwatt second, of a single synchronization procedure considering different LPWAN technologies and algorithms.</p>
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23 pages, 1372 KiB  
Article
A Joint Multi-Path and Multi-Channel Protocol for Traffic Routing in Smart Grid Neighborhood Area Networks
by Juan Pablo Astudillo León and Luis J. De la Cruz Llopis
Sensors 2018, 18(11), 4052; https://doi.org/10.3390/s18114052 - 20 Nov 2018
Cited by 11 | Viewed by 4573
Abstract
In order to improve the management mechanisms of the electric energy transport infrastructures, the smart grid networks have associated data networks that are responsible for transporting the necessary information between the different elements of the electricity network and the control center. Besides, they [...] Read more.
In order to improve the management mechanisms of the electric energy transport infrastructures, the smart grid networks have associated data networks that are responsible for transporting the necessary information between the different elements of the electricity network and the control center. Besides, they make possible a more efficient use of this type of energy. Part of these data networks is comprised of the Neighborhood Area Networks (NANs), which are responsible for interconnecting the different smart meters and other possible devices present at the consumers’ premises with the control center. Among the proposed network technologies for NANs, wireless technologies are becoming more relevant due to their flexibility and increasing available bandwidth. In this paper, some general modifications are proposed for the routing protocol of the wireless multi-hop mesh networks standardized by the IEEE. In particular, the possibility of using multiple paths and transmission channels at the same time, depending on the quality of service needs of the different network traffic, is added. The proposed modifications have been implemented in the ns-3 simulator and evaluated in situations of high traffic load. Simulation results show improvements in the network performance in terms of packet delivery ratio, throughput and network transit time. Full article
(This article belongs to the Special Issue Smart Grid Networks and Energy Cyber Physical Systems)
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<p>General view of the multi-path and multi-channel modules inclusion in HWMP.</p>
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<p>Modification to the acceptance criteria when the intermediary nodes receive a PREQ message. (<b>a</b>) HWMP. <math display="inline"><semantics> <msub> <mi>N</mi> <mn>3</mn> </msub> </semantics></math> receives, processes and forwards the <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>R</mi> <mi>E</mi> <msub> <mi>Q</mi> <mn>2</mn> </msub> </mrow> </semantics></math> message if and only if it has a better metric than <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>R</mi> <mi>E</mi> <msub> <mi>Q</mi> <mn>1</mn> </msub> </mrow> </semantics></math>. (<b>b</b>) Multi-Path Multi-Channel (MPC)-HWMP. <math display="inline"><semantics> <msub> <mi>N</mi> <mn>3</mn> </msub> </semantics></math> receives, processes and forwards <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>R</mi> <mi>E</mi> <msub> <mi>Q</mi> <mn>2</mn> </msub> </mrow> </semantics></math> to maintain multiple paths.</p>
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<p>Modification to the acceptance criteria when the destination node receives a PREQ message. (<b>a</b>) HWMP. The destination node replies with a unicast PREP message to the source node. (<b>b</b>) MPC-HWMP. The destination node replies to all received PREQ with unicast PREP messages to the source node through its different paths.</p>
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<p>Path request and path reply modifications. (<b>a</b>) Path request. (<b>b</b>) Path reply.</p>
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<p>Possible loop creation for low priority packets.</p>
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<p>Scenario under consideration.Smart grid architecture.</p>
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<p>Packet delivery ratio (HWMP vs. MPC-HWMP). (<b>a</b>) Traffic Type 1; (<b>b</b>) Traffic Type 2; (<b>c</b>) Traffic Type 3; (<b>d</b>) Traffic Type 4.</p>
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<p>Packet delivery ratio (HWMP vs. MPC-HWMP). (<b>a</b>) Traffic Type 1; (<b>b</b>) Traffic Type 2; (<b>c</b>) Traffic Type 3; (<b>d</b>) Traffic Type 4.</p>
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<p>Throughput (HWMP vs. MPC-HWMP). (<b>a</b>) Traffic Type 1; (<b>b</b>) Traffic Type 2; (<b>c</b>) Traffic Type 3; (<b>d</b>) Traffic Type 4.</p>
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<p>Throughput (HWMP vs. MPC-HWMP). (<b>a</b>) Traffic Type 1; (<b>b</b>) Traffic Type 2; (<b>c</b>) Traffic Type 3; (<b>d</b>) Traffic Type 4.</p>
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<p>Network transit time (HWMP vs. MPC-HWMP). (<b>a</b>) Traffic Type 1; (<b>b</b>) Traffic Type 2; (<b>c</b>) Traffic Type 3; (<b>d</b>) Traffic Type 4.</p>
Full article ">Figure 9 Cont.
<p>Network transit time (HWMP vs. MPC-HWMP). (<b>a</b>) Traffic Type 1; (<b>b</b>) Traffic Type 2; (<b>c</b>) Traffic Type 3; (<b>d</b>) Traffic Type 4.</p>
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<p>Routing table size (HWMP vs. MPC-HWMP).Smart grid architecture.</p>
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<p>Control channel utilization factor. (<b>a</b>) Lifetime of reactive routing information: 2.05 s; (<b>b</b>) lifetime of reactive routing information: 5.12 s.</p>
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