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Sensors, Volume 15, Issue 5 (May 2015) – 130 articles , Pages 9610-12102

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3785 KiB  
Article
LED-Absorption-QEPAS Sensor for Biogas Plants
by Michael Köhring, Stefan Böttger, Ulrike Willer and Wolfgang Schade
Sensors 2015, 15(5), 12092-12102; https://doi.org/10.3390/s150512092 - 22 May 2015
Cited by 44 | Viewed by 9158
Abstract
A new sensor for methane and carbon dioxide concentration measurements in biogas plants is presented. LEDs in the mid infrared spectral region are implemented as low cost light source. The combination of quartz-enhanced photoacoustic spectroscopy with an absorption path leads to a sensor [...] Read more.
A new sensor for methane and carbon dioxide concentration measurements in biogas plants is presented. LEDs in the mid infrared spectral region are implemented as low cost light source. The combination of quartz-enhanced photoacoustic spectroscopy with an absorption path leads to a sensor setup suitable for the harsh application environment. The sensor system contains an electronics unit and the two gas sensors; it was designed to work as standalone device and was tested in a biogas plant for several weeks. Gas concentration dependent measurements show a precision better than 1% in a range between 40% and 60% target gas concentration for both sensors. Concentration dependent measurements with different background gases show a considerable decrease in cross sensitivity against the major components of biogas in direct comparison to common absorption based sensors. Full article
(This article belongs to the Section Physical Sensors)
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Graphical abstract

Graphical abstract
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<p>Sketch of the experimental setup for absorption-QEPAS. The detector cell around the off-beam resonator is not illustrated to allow insight in the resonator setup.</p>
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<p>Typical resonance curve of the QTF within the methane QEPAS cell. The inset shows a photograph of a QTF with and without housing.</p>
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<p>Picture of the biogas detection system with electronics unit and both absorption-QEPAS sensors (<b>Left</b>); and photograph of the interior of the methane sensor (<b>Right</b>).</p>
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<p>Concentration dependent measurement for the CH<sub>4</sub> absorption-QEPAS sensor. The dots represent the averaged sensor signal at each gas concentration; the red line shows an exponential fit to these values. The inset depicts the time dependent raw data.</p>
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<p>Concentration dependent measurement for the CO<sub>2</sub> absorption-QEPAS sensor. The dots represent the averaged sensor signal at each gas concentration; the red line shows an exponential fit to these values. The inset depicts the time dependent raw data.</p>
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<p>Measurement precision of both sensors, in dependence of the target gas concentration. The dots represent the measurement precision calculated from the measured data; the red lines show an exponential fit for interpolation.</p>
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<p>Concentration dependent cross sensitivity measurements for the QEPAS (<b>a</b>) and the conventional (<b>b</b>) methane sensor; dry nitrogen, dry CO<sub>2</sub> and humidified CO<sub>2</sub> were used as background gases.</p>
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<p>Concentration deviation in % of the target gas concentration for the QEPAS sensor with humidified CO<sub>2</sub> (1) and dry N<sub>2</sub> (2) and for the conventional sensor with humidified CO<sub>2</sub> (3) and dry N<sub>2</sub> (4) in comparison to the dry CO<sub>2</sub> background gas measurements.</p>
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1344 KiB  
Article
Iterative Precise Conductivity Measurement with IDEs
by Jaromír Hubálek
Sensors 2015, 15(5), 12080-12091; https://doi.org/10.3390/s150512080 - 22 May 2015
Cited by 7 | Viewed by 7053
Abstract
The paper presents a new approach in the field of precise electrolytic conductivity measurements with planar thin- and thick-film electrodes. This novel measuring method was developed for measurement with comb-like electrodes called interdigitated electrodes (IDEs). Correction characteristics over a wide range of specific [...] Read more.
The paper presents a new approach in the field of precise electrolytic conductivity measurements with planar thin- and thick-film electrodes. This novel measuring method was developed for measurement with comb-like electrodes called interdigitated electrodes (IDEs). Correction characteristics over a wide range of specific conductivities were determined from an interface impedance characterization of the thick-film IDEs. The local maximum of the capacitive part of the interface impedance is used for corrections to get linear responses. The measuring frequency was determined at a wide range of measured conductivity. An iteration mode of measurements was suggested to precisely measure the conductivity at the right frequency in order to achieve a highly accurate response. The method takes precise conductivity measurements in concentration ranges from 10−6 to 1 M without electrode cell replacement. Full article
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Graphical abstract

Graphical abstract
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<p>Nyquist plots of interface impedance, theoretical locus (blue curve) and real locus (red curve). The real locus shows parasitic capacitance overlapping double layer capacitance. Randles equivalent circuit is drawn in the inset of graph. Additional parasitic capacitance is involved for real behavior circuit of interdigitated electrodes (IDEs).</p>
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<p>Conductivity measurements with CyberScan PC6500. Two electrodes with cell constant 1 and 10 cm<sup>-1</sup> calibrated at two points. Measured conductivity <span class="html-italic">versus</span> calibrated samples are plotted. Inset of figure presets relative error of measured conductivity. The error is high out of the cell conductivity range.</p>
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<p>Impedance characteristics of IDEs according to different measured specific conductivity.</p>
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<p>The correction factor of the cell constant experimentally determined from the measurement of the screen printed IDEs.</p>
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<p>(<b>a</b>) Impedance in logarithmic Nyquist plot of different electrolyte concentrations measured by IDEs, parameters: 1 mm spacing, 1 mm electrode width, 7 mm length, six fingers; (<b>b</b>) Complex plain presenting impedance taken at local maximum for different conductivity/concentration of measured electrolyte. Reprinted from [<a href="#B25-sensors-15-12080" class="html-bibr">25</a>], Copyright (2015), with permission from Elsevier.</p>
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<p>3D plot demonstrating cell constant deviation according to the measuring frequency and measured conductivity. Unaccepted deviations are detected at low conductivity measured at high frequencies and high conductivity measured at low frequencies.</p>
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<p>Linear dependence of frequency at local maxima on measured specific conductivity obtained from measuring various specific conductivities by IDEs [<a href="#B25-sensors-15-12080" class="html-bibr">25</a>]. Parameters for Equation (5) are determined from linear regression (inset of figure).</p>
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<p>The 3D graph presents relation between real specific conductivity which is going to be determined, and responded specific conductivity, which differ according to the measuring frequency applied to IDEs. Demonstration of the iterative algorithm running in three steps of approximation to the measured real specific conductivity is represented by red arrows.</p>
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<p>Verification of cell constant deviation when the developed method is used for measurement.</p>
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2019 KiB  
Article
Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images
by Wonseok Kang, Soohwan Yu, Seungyong Ko and Joonki Paik
Sensors 2015, 15(5), 12053-12079; https://doi.org/10.3390/s150512053 - 22 May 2015
Cited by 9 | Viewed by 7271
Abstract
In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the [...] Read more.
In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures. Full article
(This article belongs to the Special Issue UAV Sensors for Environmental Monitoring)
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<p>The multispectral imaging process.</p>
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<p>Block-diagram of the proposed super-resolution method.</p>
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<p>Block-diagram of the proposed fusion-based high-resolution (HR) color image reconstruction process.</p>
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<p>Five multispectral test images.</p>
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<p>Results of resolution enhancement by enlarging a simulated low-resolution (LR) multispectral image: (<b>a</b>) cropped original HR image in <a href="#f4-sensors-15-12053" class="html-fig">Figure 4a</a>; (<b>b</b>) the four-times down-sampled LR image; results of: (<b>c</b>) cubic-spline interpolation [<a href="#b3-sensors-15-12053" class="html-bibr">3</a>]; (<b>d</b>) interpolation-based SR [<a href="#b4-sensors-15-12053" class="html-bibr">4</a>]; (<b>e</b>) interpolation-based SR [<a href="#b5-sensors-15-12053" class="html-bibr">5</a>]; (<b>f</b>) interpolation-based SR [<a href="#b6-sensors-15-12053" class="html-bibr">6</a>]; (<b>g</b>) example-based SR [<a href="#b7-sensors-15-12053" class="html-bibr">7</a>]; (<b>h</b>) patch-based SR [<a href="#b9-sensors-15-12053" class="html-bibr">9</a>]; (<b>i</b>) patch-based SR [<a href="#b10-sensors-15-12053" class="html-bibr">10</a>]; (<b>j</b>) patch-based SR [<a href="#b11-sensors-15-12053" class="html-bibr">11</a>]; (<b>k</b>) patch-based SR [<a href="#b12-sensors-15-12053" class="html-bibr">12</a>] and (<b>l</b>) the proposed method.</p>
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<p>Results of resolution enhancement by enlarging a simulated LR multispectral image: (<b>a</b>) cropped original HR image in <a href="#f4-sensors-15-12053" class="html-fig">Figure 4b</a>; (<b>b</b>) the four-times down-sampled LR image; results of: (<b>c</b>) cubic-spline interpolation [<a href="#b3-sensors-15-12053" class="html-bibr">3</a>], (<b>d</b>) interpolation-based SR [<a href="#b4-sensors-15-12053" class="html-bibr">4</a>], (<b>e</b>) interpolation-based SR [<a href="#b5-sensors-15-12053" class="html-bibr">5</a>], (<b>f</b>) interpolation-based SR [<a href="#b6-sensors-15-12053" class="html-bibr">6</a>], (<b>g</b>) example-based SR [<a href="#b7-sensors-15-12053" class="html-bibr">7</a>], (<b>h</b>) patch-based SR [<a href="#b9-sensors-15-12053" class="html-bibr">9</a>], (<b>i</b>) patch-based SR [<a href="#b10-sensors-15-12053" class="html-bibr">10</a>], (<b>j</b>) patch-based SR [<a href="#b11-sensors-15-12053" class="html-bibr">11</a>], (<b>k</b>) patch-based SR [<a href="#b12-sensors-15-12053" class="html-bibr">12</a>] and (<b>l</b>) the proposed method.</p>
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<p>Results of resolution enhancement by enlarging a simulated LR multispectral image: (<b>a</b>) cropped original HR image in <a href="#f4-sensors-15-12053" class="html-fig">Figure 4c</a>; (<b>b</b>) the four-times down-sampled LR image; results of: (c) cubic-spline interpolation [<a href="#b3-sensors-15-12053" class="html-bibr">3</a>], (<b>d</b>) interpolation-based SR [<a href="#b4-sensors-15-12053" class="html-bibr">4</a>], (<b>e</b>) interpolation-based SR [<a href="#b5-sensors-15-12053" class="html-bibr">5</a>], (<b>f</b>) interpolation-based SR [<a href="#b6-sensors-15-12053" class="html-bibr">6</a>], (<b>g</b>) example-based SR [<a href="#b7-sensors-15-12053" class="html-bibr">7</a>], (<b>h</b>) patch-based SR [<a href="#b9-sensors-15-12053" class="html-bibr">9</a>], (<b>i</b>) patch-based SR [<a href="#b10-sensors-15-12053" class="html-bibr">10</a>], (<b>j</b>) patch-based SR [<a href="#b11-sensors-15-12053" class="html-bibr">11</a>], (<b>k</b>) patch-based SR [<a href="#b12-sensors-15-12053" class="html-bibr">12</a>] and (<b>l</b>) the proposed method.</p>
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<p>Results of resolution enhancement by enlarging a simulated LR multispectral image: (<b>a</b>) cropped original HR image in <a href="#f4-sensors-15-12053" class="html-fig">Figure 4d</a>; (<b>b</b>) the four-times down-sampled LR image; results of: (<b>c</b>) cubic-spline interpolation [<a href="#b3-sensors-15-12053" class="html-bibr">3</a>], (<b>d</b>) interpolation-based SR [<a href="#b4-sensors-15-12053" class="html-bibr">4</a>], (<b>e</b>) interpolation-based SR [<a href="#b5-sensors-15-12053" class="html-bibr">5</a>], (<b>f</b>) interpolation-based SR [<a href="#b6-sensors-15-12053" class="html-bibr">6</a>], (<b>g</b>) example-based SR [<a href="#b7-sensors-15-12053" class="html-bibr">7</a>], (<b>h</b>) patch-based SR [<a href="#b9-sensors-15-12053" class="html-bibr">9</a>], (<b>i</b>) patch-based SR [<a href="#b10-sensors-15-12053" class="html-bibr">10</a>], (<b>j</b>) patch-based SR [<a href="#b11-sensors-15-12053" class="html-bibr">11</a>], (<b>k</b>) patch-based SR [<a href="#b12-sensors-15-12053" class="html-bibr">12</a>] and (<b>l</b>) the proposed method.</p>
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<p>Results of resolution enhancement by enlarging a simulated LR multispectral image: (<b>a</b>) cropped original HR image in <a href="#f4-sensors-15-12053" class="html-fig">Figure 4e</a>; (<b>b</b>) the four-times down-sampled LR image; results of: (<b>c</b>) cubic-spline interpolation [<a href="#b3-sensors-15-12053" class="html-bibr">3</a>], (<b>d</b>) interpolation-based SR [<a href="#b4-sensors-15-12053" class="html-bibr">4</a>], (<b>e</b>) interpolation-based SR [<a href="#b5-sensors-15-12053" class="html-bibr">5</a>], (<b>f</b>) interpolation-based SR [<a href="#b6-sensors-15-12053" class="html-bibr">6</a>], (<b>g</b>) example-based SR [<a href="#b7-sensors-15-12053" class="html-bibr">7</a>], (<b>h</b>) patch-based SR [<a href="#b9-sensors-15-12053" class="html-bibr">9</a>], (<b>i</b>) patch-based SR [<a href="#b10-sensors-15-12053" class="html-bibr">10</a>], (j) patch-based SR [<a href="#b11-sensors-15-12053" class="html-bibr">11</a>], (<b>k</b>) patch-based SR [<a href="#b12-sensors-15-12053" class="html-bibr">12</a>] and (<b>l</b>) the proposed method.</p>
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9047 KiB  
Article
Highly Sensitive Bacteria Quantification Using Immunomagnetic Separation and Electrochemical Detection of Guanine-Labeled Secondary Beads
by Harikrishnan Jayamohan, Bruce K. Gale, Bj Minson, Christopher J. Lambert, Neil Gordon and Himanshu J. Sant
Sensors 2015, 15(5), 12034-12052; https://doi.org/10.3390/s150512034 - 22 May 2015
Cited by 46 | Viewed by 10345
Abstract
In this paper, we report the ultra-sensitive indirect electrochemical detection of E. coli O157:H7 using antibody functionalized primary (magnetic) beads for capture and polyguanine (polyG) oligonucleotide functionalized secondary (polystyrene) beads as an electrochemical tag. Vacuum filtration in combination with E. coli O157:H7 specific [...] Read more.
In this paper, we report the ultra-sensitive indirect electrochemical detection of E. coli O157:H7 using antibody functionalized primary (magnetic) beads for capture and polyguanine (polyG) oligonucleotide functionalized secondary (polystyrene) beads as an electrochemical tag. Vacuum filtration in combination with E. coli O157:H7 specific antibody modified magnetic beads were used for extraction of E. coli O157:H7 from 100 mL samples. The magnetic bead conjugated E. coli O157:H7 cells were then attached to polyG functionalized secondary beads to form a sandwich complex (magnetic bead/E. coli secondary bead). While the use of magnetic beads for immuno-based capture is well characterized, the use of oligonucleotide functionalized secondary beads helps combine amplification and potential multiplexing into the system. The antibody functionalized secondary beads can be easily modified with a different antibody to detect other pathogens from the same sample and enable potential multiplexing. The polyGs on the secondary beads enable signal amplification up to 10\(^{8}\) guanine tags per secondary bead (\(7.5\times10^{6}\) biotin-FITC per secondary bead, 20 guanines per oligonucleotide) bound to the target (E. coli). A single-stranded DNA probe functionalized reduced graphene oxide modified glassy carbon electrode was used to bind the polyGs on the secondary beads. Fluorescent imaging was performed to confirm the hybridization of the complex to the electrode surface. Differential pulse voltammetry (DPV) was used to quantify the amount of polyG involved in the hybridization event with tris(2,2'-bipyridine)ruthenium(II) (Ru(bpy)\(_{3}^{2+}\)) as the mediator. The amount of polyG signal can be correlated to the amount of E. coli O157:H7 in the sample. The method was able to detect concentrations of E. coli O157:H7 down to 3 CFU/100 mL, which is 67 times lower than the most sensitive technique reported in literature. The signal to noise ratio for this work was 3. We also demonstrate the use of the protocol for detection of E. coli O157:H7 seeded in waste water effluent samples. Full article
(This article belongs to the Special Issue Biosensors for Pathogen Detection)
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<p>Working principle of the <span class="html-italic">E. coli</span> detection mechanism.</p>
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<p>Review of recent point-of-use methods used for detection of proteins and DNA sequences.</p>
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<p>Mechanism of indirect sensing of <span class="html-italic">E. coli</span> O157:H7 using IMS and subsequent signal amplification using polyG functionalized secondary beads.</p>
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<p>Schematic of GCE preparation for capture of the magnetic bead/<span class="html-italic">E. coli</span>/secondary bead complexes.</p>
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<p>CV curve of graphene oxide electrodeposition on a GCE showing one anodic peak -I and two cathodic peaks -II and III.</p>
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<p>Fluorescent microscopy images of (a) Bound magnetic bead/<span class="html-italic">E. coli</span>/secondary bead complexes on RGO-GCE; (<b>b</b>) negative control 1 (DI water as starting sample- no <span class="html-italic">E. coli</span> present); and (<b>c</b>) negative control 2 (polyGs absent on the magnetic bead/<span class="html-italic">E. coli</span>/secondary bead complexes).</p>
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<p>Absolute DPV signals (S1) corresponding to an order of magnitude change in concentration of <span class="html-italic">E.coli</span> O157:H7 from 3 to 300 CFUs. EC measurement condition: pulse size: 20 mV, scan rate: 5 mV/s, scan range 0.5 V to 1.2 V (vs. Ag/AgCl reference electrode). Supporting electrolyte: 0.2 M acetate buffer solution (pH 5) containing 5 μM <math display="inline"> <semantics id="sm6"> <mrow> <mtext>Ru</mtext> <msubsup> <mrow> <mrow> <mo>(</mo> <mrow> <mtext>bpy</mtext></mrow> <mo>)</mo></mrow></mrow> <mn>3</mn> <mrow> <mn>2</mn> <mo>+</mo></mrow></msubsup></mrow></semantics></math>.</p>
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<p>Relative DPV signals (S1-S5) corresponding to varying concentrations of <span class="html-italic">E. coli</span> O157:H7 in seeded 100 mL PBS buffer samples.</p>
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<p>Electrochemical signal corresponding to <span class="html-italic">E. coli</span> O157:H7 in waste water effluent samples. Negative control is in the form of DI water without any <span class="html-italic">E. coli</span> O157:H7 in it. EC measurement condition: pulse size: 20 mV, scan rate: 5 mV/s, scan range 0.5 V to 1.2 V (<span class="html-italic">vs</span>. Ag/AgCl reference electrode). Supporting electrolyte: 0.2 M acetate buffer solution (pH 5) containing 5 μM <math display="inline"> <semantics id="sm7"> <mrow> <mtext>Ru</mtext> <msubsup> <mrow> <mrow> <mo>(</mo> <mrow> <mtext>bpy</mtext></mrow> <mo>)</mo></mrow></mrow> <mn>3</mn> <mrow> <mn>2</mn> <mo>+</mo></mrow></msubsup></mrow></semantics></math>.</p>
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1898 KiB  
Article
Gated Silicon Drift Detector Fabricated from a Low-Cost Silicon Wafer
by Hideharu Matsuura, Shungo Sakurai, Yuya Oda, Shinya Fukushima, Shohei Ishikawa, Akinobu Takeshita and Atsuki Hidaka
Sensors 2015, 15(5), 12022-12033; https://doi.org/10.3390/s150512022 - 22 May 2015
Cited by 4 | Viewed by 7089
Abstract
Inexpensive high-resolution silicon (Si) X-ray detectors are required for on-site surveys of traces of hazardous elements in food and soil by measuring the energies and counts of X-ray fluorescence photons radially emitted from these elements. Gated silicon drift detectors (GSDDs) are much cheaper [...] Read more.
Inexpensive high-resolution silicon (Si) X-ray detectors are required for on-site surveys of traces of hazardous elements in food and soil by measuring the energies and counts of X-ray fluorescence photons radially emitted from these elements. Gated silicon drift detectors (GSDDs) are much cheaper to fabricate than commercial silicon drift detectors (SDDs). However, previous GSDDs were fabricated from \(10\)-k\(\Omega \cdot\)cm Si wafers, which are more expensive than \(2\)-k\(\Omega \cdot\)cm Si wafers used in commercial SDDs. To fabricate cheaper portable X-ray fluorescence instruments, we investigate GSDDs formed from \(2\)-k\(\Omega \cdot\)cm Si wafers. The thicknesses of commercial SDDs are up to \(0.5\) mm, which can detect photons with energies up to \(27\) keV, whereas we describe GSDDs that can detect photons with energies of up to \(35\) keV. We simulate the electric potential distributions in GSDDs with Si thicknesses of \(0.5\) and \(1\) mm at a single high reverse bias. GSDDs with one gate pattern using any resistivity Si wafer can work well for changing the reverse bias that is inversely proportional to the resistivity of the Si wafer. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan 2015)
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<p>Half of a schematic cross section of a cylindrical GSDD structure with one p-ring and seven gates. The same negative voltage was applied to the cathode, the p-ring, and all the gates.</p>
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<p>Simulated electric potential distribution in the Si substrate inside the p-ring of a 0.5-mm-thick GSDD with <span class="html-italic">R</span><sub>chip</sub> of 3.5 mm and ρ<sub>Si</sub> of 10 kΩ·cm for Gate A. A reverse bias voltage of −60 V was applied to the cathode, p-ring, and seven gates. <span class="html-italic">Q</span><sub>F</sub> was assumed to be 3 × 10<sup>10</sup> cm<sup>−2</sup>. Equipotential lines are shown at 1 V intervals.</p>
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<p>Simulated electric potential distribution in the Si substrate inside the p-ring of a 0.5-mm-thick GSDD with <span class="html-italic">R</span><sub>chip</sub> of 3.5 mm and ρ<sub>Si</sub> of 2 kΩ·cm for Gate A. A reverse bias voltage of −60 V was applied to the cathode, p-ring, and seven gates. Equipotential lines are shown at 1 V intervals.</p>
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<p>Simulated electric potential distribution in the Si substrate inside the p-ring of a 0.5-mm-thick GSDD with <span class="html-italic">R</span><sub>chip</sub> of 3.5 mm and ρ<sub>Si</sub> of 2 kΩ·cm for Gate A. A reverse bias voltage of −300 V was applied to the cathode, p-ring and seven gates. Equipotential lines are shown at 5 V intervals.</p>
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<p>Simulated electric potential distribution in the Si substrate inside the p-ring of a 0.5-mm-thick GSDD with <span class="html-italic">R</span><sub>chip</sub> of 3.5 mm and ρ<sub>Si</sub> of 2 kΩ·cm for Gate B. A reverse bias voltage of −200 V was applied to the cathode, p-ring and seven gates. Equipotential lines are shown at 2.5 V intervals.</p>
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<p>Simulated electric potential distribution in the Si substrate inside the p-ring of a 0.5-mm-thick GSDD with <span class="html-italic">R</span><sub>chip</sub> of 3.5 mm and ρ<sub>Si</sub> of 2 kΩ·cm for Gate C. A reverse bias voltage of −150 V was applied to the cathode, p-ring, and seven gates. Equipotential lines are shown at 2.5 V intervals.</p>
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<p>Simulated electric potential distributions in the Si substrate inside the p-ring of a 1-mm-thick GSDD with <span class="html-italic">R</span><sub>chip</sub> of 3.5 mm and ρ<sub>Si</sub> of 2 kΩ·cm for Gate D. A reverse bias voltage of −600 V was applied to the cathode, p-ring, and seven gates. Equipotential lines are shown at 10 V intervals.</p>
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634 KiB  
Review
Survey of WBSNs for Pre-Hospital Assistance: Trends to Maximize the Network Lifetime and Video Transmission Techniques
by Enrique Gonzalez, Raul Peña, Cesar Vargas-Rosales, Alfonso Avila and David Perez-Diaz De Cerio
Sensors 2015, 15(5), 11993-12021; https://doi.org/10.3390/s150511993 - 22 May 2015
Cited by 26 | Viewed by 17734
Abstract
This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the [...] Read more.
This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the delivery of medical care. Wireless body sensor networks (WBSNs) are a promising technology capable of improving the existing practices in condition assessment and care delivery for a patient in a medical emergency. This technology can also facilitate the early interventions of a specialist physician during the pre-hospital period. WBSNs make possible these early interventions by establishing remote communication links with video/audio support and by providing medical information such as vital signs, electrocardiograms, etc. in real time. This survey focuses on relevant issues needed to understand how to setup a WBSN for medical emergencies. These issues are: monitoring vital signs and video transmission, energy efficient protocols, scheduling, optimization and energy consumption on a WBSN. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
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Graphical abstract

Graphical abstract
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<p>Main causes of death in Mexico, 2006–2011. Source: Death registry INEGI, 2011.</p>
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<p>Wireless body sensor network communicating through a mobile phone.</p>
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<p>Wireless Monitoring Platform for Emergency Situations. (<b>A</b>) Intra-WBSN communication; (<b>B</b>) Inter-WBSN communication; (<b>C</b>) Beyond-WBSN communication.</p>
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198 KiB  
Reply
Revision of J3Gen and Validity of the Attacks by Peinado et al.
by Alberto Peinado, Jorge Munilla and Amparo Fúster-Sabater
Sensors 2015, 15(5), 11988-11992; https://doi.org/10.3390/s150511988 - 22 May 2015
Cited by 1 | Viewed by 4312
Abstract
This letter is the reply to: Remarks on Peinado et al.’s Analysis of J3Gen by J. Garcia-Alfaro, J. Herrera-Joancomartí and J. Melià-Seguí published in Sensors 2015, 15, 6217–6220. Peinado et al. cryptanalyzed the pseudorandom number generator proposed by Melià-Seguí et al., describing two [...] Read more.
This letter is the reply to: Remarks on Peinado et al.’s Analysis of J3Gen by J. Garcia-Alfaro, J. Herrera-Joancomartí and J. Melià-Seguí published in Sensors 2015, 15, 6217–6220. Peinado et al. cryptanalyzed the pseudorandom number generator proposed by Melià-Seguí et al., describing two possible attacks. Later, Garcia-Alfaro claimed that one of this attack did not hold in practice because the assumptions made by Peinado et al. were not correct. This letter reviews those remarks, showing that J3Gen is anyway flawed and that, without further information, the interpretation made by Peinado et al. seems to be correct. Full article
(This article belongs to the Section Sensor Networks)
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<p>Generation of two random numbers in J3Gen.</p>
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2462 KiB  
Article
A Highly Sensitive Porous Silicon (P-Si)-Based Human Kallikrein 2 (hK2) Immunoassay Platform toward Accurate Diagnosis of Prostate Cancer
by Sang Wook Lee, Kazuo Hosokawa, Soyoun Kim, Ok Chan Jeong, Hans Lilja, Thomas Laurell and Mizuo Maeda
Sensors 2015, 15(5), 11972-11987; https://doi.org/10.3390/s150511972 - 22 May 2015
Cited by 10 | Viewed by 7112
Abstract
Levels of total human kallikrein 2 (hK2), a protein involved the pathology of prostate cancer (PCa), could be used as a biomarker to aid in the diagnosis of this disease. In this study, we report on a porous silicon antibody immunoassay platform for [...] Read more.
Levels of total human kallikrein 2 (hK2), a protein involved the pathology of prostate cancer (PCa), could be used as a biomarker to aid in the diagnosis of this disease. In this study, we report on a porous silicon antibody immunoassay platform for the detection of serum levels of total hK2. The surface of porous silicon has a 3-dimensional macro- and nanoporous structure, which offers a large binding capacity for capturing probe molecules. The tailored pore size of the porous silicon also allows efficient immobilization of antibodies by surface adsorption, and does not require chemical immobilization. Monoclonal hK2 capture antibody (6B7) was dispensed onto P-Si chip using a piezoelectric dispenser. In total 13 × 13 arrays (169 spots) were spotted on the chip with its single spot volume of 300 pL. For an optimization of capture antibody condition, we firstly performed an immunoassay of the P-Si microarray under a titration series of hK2 in pure buffer (PBS) at three different antibody densities (75, 100 and 145 µg/mL). The best performance of the microarray platform was seen at 100 µg/mL of the capture antibody concentration (LOD was 100 fg/mL). The platform then was subsequently evaluated for a titration series of serum-spiked hK2 samples. The developed platform utilizes only 15 µL of serum per test and the total assay time is about 3 h, including immobilization of the capture antibody. The detection limit of the hK2 assay was 100 fg/mL in PBS buffer and 1 pg/mL in serum with a dynamic range of 106 (10−4 to 102 ng/mL). Full article
(This article belongs to the Special Issue Immunosensors 2014)
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<p>Schematic of the P-Si chip immunoassay procedure, starting with the dispensing of hK2 capture antibodies onto the porous silicon surface. The porous silicon chip with physically adsorbed antibodies is placed into an assay well made of polydimethyl-polysiloxane (PDMS), and hK2-containing serum samples are added; the size of the PDMS assay is well suited for each P-Si chip and makes it easy to perform parallel pipetting. Subsequently, detection antibody (polyclonal primary and Alexa 488 labeled secondary antibody) is used for measuring fluorescent signals under a microscope.</p>
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<p>Porous silicon (P-Si) matrix used for microarrays. P-Si chips and PDMS wells (<b>a</b>) Capture antibody spotted P-Si chips were located in the wells to start the immunoassay. The scanning electron micrographs show a sequential zoom into a typical surface; (<b>b</b>) Macro-pores of micrometer size are clearly seen, combined with a micro- and nano-morphology (pore size around sub-µm to µm).</p>
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<p>Titration series of hK2 in buffer (PBS) solution at three different concentrations of the capturing antibody 6B7 (75 µg/mL, 100 µg/mL and 145 µg/mL). The LOD was found to be 1 pg/mL when the capturing antibody was 75 µg/mL and was reduced to 100 fg/mL when the capturing antibody concentrations were 100 µg/mL. The LOD became 1 pg/mL again when concentration of the antibody was 145 µg/mL.</p>
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<p>hK2-spiked human female serum analyzed with the sandwich microarray at two different capturing antibody concentrations (75 µg/mL and 100 µg/mL). The LOD was 10 pg/mL and 1 pg/mL when the concentrations of capture antibody were 75 µg/mL and 100 µg/mL, respectively. Increased assay sensitivity was observed with an elevated concentration of the capturing antibody (6B7). The negative signal also increased at the higher concentrations of the capture antibody.</p>
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<p>Immunoassay signals of the diluted serum samples. Serum samples were diluted down to 50 times and the samples were immunoassayed on microarray chips. The concentration of capture antibody was 100 µg/mL. Mean spot intensities and coefficients of variant are presented with spot images in the left panel.</p>
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<p>Cross-reaction tests of hK2 antibody spots against PSA spiked serum. Immunoassay signal of negative control (female serum sample) was compared with four PSA-spiked serum samples (5, 50 and 500 ng/mL and 5 µg/mL). HK2 capture antibody was spotted on P-Si chips at a concentration of 100 µg/mL.</p>
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1212 KiB  
Article
User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm
by Serge Thomas Mickala Bourobou and Younghwan Yoo
Sensors 2015, 15(5), 11953-11971; https://doi.org/10.3390/s150511953 - 21 May 2015
Cited by 105 | Viewed by 11531
Abstract
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, [...] Read more.
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>User activity recognition in smart home [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Architecture of hybrid method.</p>
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<p>Features of K-pattern algorithm.</p>
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<p>Architecture of tracking activity using K-pattern algorithm [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Process of forming frequent activity patterns [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Process of forming clustering [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Process of recomputing new center [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Basic taxonomy of clustering algorithm.</p>
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<p>Multilayered artificial neural network.</p>
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<p>Training smart environment for activities recognition.</p>
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<p>Temporal relations representation.</p>
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Article
A Lightweight White-Box Symmetric Encryption Algorithm against Node Capture for WSNs
by Yang Shi, Wujing Wei and Zongjian He
Sensors 2015, 15(5), 11928-11952; https://doi.org/10.3390/s150511928 - 21 May 2015
Cited by 37 | Viewed by 6651
Abstract
Wireless Sensor Networks (WSNs) are often deployed in hostile environments and, thus, nodes can be potentially captured by an adversary. This is a typical white-box attack context, i.e., the adversary may have total visibility of the implementation of the build-in cryptosystem and [...] Read more.
Wireless Sensor Networks (WSNs) are often deployed in hostile environments and, thus, nodes can be potentially captured by an adversary. This is a typical white-box attack context, i.e., the adversary may have total visibility of the implementation of the build-in cryptosystem and full control over its execution platform. Handling white-box attacks in a WSN scenario is a challenging task. Existing encryption algorithms for white-box attack contexts require large memory footprint and, hence, are not applicable for wireless sensor networks scenarios. As a countermeasure against the threat in this context, in this paper, we propose a class of lightweight secure implementations of the symmetric encryption algorithm SMS4. The basic idea of our approach is to merge several steps of the round function of SMS4 into table lookups, blended by randomly generated mixing bijections. Therefore, the size of the implementations are significantly reduced while keeping the same security efficiency. The security and efficiency of the proposed solutions are theoretically analyzed. Evaluation shows our solutions satisfy the requirement of sensor nodes in terms of limited memory size and low computational costs. Full article
(This article belongs to the Section Sensor Networks)
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<p>The flow and structure of SMS4.</p>
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<p>The structure of T-Boxes in a round.</p>
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<p>The structure of round 0 and round 1.</p>
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<p>The structure of an intermediate round.</p>
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<p>The size of static data.</p>
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<p>(<b>a</b>) The number of multi-table lookups; (<b>b</b>) The number of byte additions; (<b>c</b>) The number of bit additions; (<b>d</b>) The number of TBox lookups.</p>
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<p>Experimental results of the performance test on Intel iMote.</p>
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<p>The structure of a round of Xiao <span class="html-italic">et al</span>.’s white-box SMS4.</p>
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<p>Attack models.</p>
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<p>Non-standard states in the process of a white-box SMS4.</p>
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812 KiB  
Review
Fruit Quality Evaluation Using Spectroscopy Technology: A Review
by Hailong Wang, Jiyu Peng, Chuanqi Xie, Yidan Bao and Yong He
Sensors 2015, 15(5), 11889-11927; https://doi.org/10.3390/s150511889 - 21 May 2015
Cited by 286 | Viewed by 14868
Abstract
An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, [...] Read more.
An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified. Full article
(This article belongs to the Special Issue Sensors for Food Safety and Quality)
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1740 KiB  
Article
Surface Acoustic Wave (SAW) Resonators for Monitoring Conditioning Film Formation
by Siegfried Hohmann, Svea Kögel, Yvonne Brunner, Barbara Schmieg, Christina Ewald, Frank Kirschhöfer, Gerald Brenner-Weiß and Kerstin Länge
Sensors 2015, 15(5), 11873-11888; https://doi.org/10.3390/s150511873 - 21 May 2015
Cited by 23 | Viewed by 9012
Abstract
We propose surface acoustic wave (SAW) resonators as a complementary tool for conditioning film monitoring. Conditioning films are formed by adsorption of inorganic and organic substances on a substrate the moment this substrate comes into contact with a liquid phase. In the case [...] Read more.
We propose surface acoustic wave (SAW) resonators as a complementary tool for conditioning film monitoring. Conditioning films are formed by adsorption of inorganic and organic substances on a substrate the moment this substrate comes into contact with a liquid phase. In the case of implant insertion, for instance, initial protein adsorption is required to start wound healing, but it will also trigger immune reactions leading to inflammatory responses. The control of the initial protein adsorption would allow to promote the healing process and to suppress adverse immune reactions. Methods to investigate these adsorption processes are available, but it remains difficult to translate measurement results into actual protein binding events. Biosensor transducers allow user-friendly investigation of protein adsorption on different surfaces. The combination of several transduction principles leads to complementary results, allowing a more comprehensive characterization of the adsorbing layer. We introduce SAW resonators as a novel complementary tool for time-resolved conditioning film monitoring. SAW resonators were coated with polymers. The adsorption of the plasma proteins human serum albumin (HSA) and fibrinogen onto the polymer-coated surfaces were monitored. Frequency results were compared with quartz crystal microbalance (QCM) sensor measurements, which confirmed the suitability of the SAW resonators for this application. Full article
(This article belongs to the Special Issue Acoustic Waveguide Sensors)
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<p>SAW sensor configurations (<b>a</b>) delay line (<b>b</b>) resonator (two-port).</p>
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<p>SAW resonator type SR062 consisting of LiTaO<sub>3</sub> substrate with gold transducers.</p>
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<p>Flow cell connecting the SAW resonator to the driving electronics and the peripheral fluidic system. (<b>a</b>) Open flow cell (top view), without cover; (<b>b</b>) Flow cell closed with cover (side view).</p>
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<p>Flow injection analysis system for the SAW resonator measurement setup. Carrier medium was driven by a pump through the reference flow cell or through a valve connected with the measurement flow cell. The solid lines in the valve represent the load mode, in which the sample loop is loaded while the measurement cell is rinsed with carrier medium. The dotted lines represent the inject mode in which the carrier medium moves the sample through the measurement cell.</p>
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<p>Flow system used for the QCM sensor measurement setup. Either carrier medium or sample was driven by a pump through the flow cells.</p>
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<p>Conditioning film monitoring with SAW resonators coated with parylene C: Adsorption of plasma proteins HSA (red curves) and fibrinogen (blue curves). Samples contained 250 µg/mL protein in PBS and were injected into a PBS carrier stream. Injection started 1 min after start of the measurement (see arrow). Gray curves represent the signals obtained with the reference resonators, which were rinsed with carrier medium PBS throughout the complete measurement.</p>
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<p>Conditioning film monitoring with QCM sensors coated with parylene C: Adsorption of plasma proteins HSA (red curves) and fibrinogen (blue curves). Samples contained 250 µg/mL protein in PBS. The PBS carrier stream was switched to sample solution 3 min after start of the measurement (see arrow). (<b>a</b>) Frequency; (<b>b</b>) Dissipation.</p>
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<p>Conditioning film monitoring with (<b>a</b>) SAW resonators and (<b>b</b>) QCM sensors coated with parylene C: normalized frequency curves (dashed lines) obtained by adsorption of plasma proteins HSA (red curves) and fibrinogen (blue curves). Solid lines represent average curves. Samples contained 250 µg/mL protein in PBS. The PBS carrier stream was switched to sample solution (a) 1 min and (b) 3 min after start of the measurement (see arrows).</p>
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1168 KiB  
Article
Branch-Based Centralized Data Collection for Smart Grids Using Wireless Sensor Networks
by Kwangsoo Kim and Seong-il Jin
Sensors 2015, 15(5), 11854-11872; https://doi.org/10.3390/s150511854 - 21 May 2015
Cited by 16 | Viewed by 6028
Abstract
A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses [...] Read more.
A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses on a centralized data collection problem of how to collect every power usage from every meter without collisions in an environment in which the time synchronization among smart meters is not guaranteed. To solve the problem, we divide a tree that a sensor network constructs into several branches. A conflict-free query schedule is generated based on the branches. Each power usage is collected according to the schedule. The proposed method has important features: shortening query processing time and avoiding collisions between a query and query responses. We evaluate this method using the ns-2 simulator. The experimental results show that this method can achieve both collision avoidance and fast query processing at the same time. The success rate of data collection at a sink node executing this method is 100%. Its running time is about 35 percent faster than that of the round-robin method, and its memory size is reduced to about 10% of that of the depth-first search method. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Architecture of the advanced metering infrastructure (AMI) system.</p>
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<p>Example of a sensor network.</p>
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<p>Transmissions in breadth-first search.</p>
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<p>Generating and processing branches.</p>
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<p>Query and response flows.</p>
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<p>Algorithms for sink, internal and leaf node.</p>
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<p>Results of the proposed method and <a href="#FD11" class="html-disp-formula">Equation (5)</a>. (<b>a</b>) Tree structure; (<b>b</b>) Execution results of algorithms; (<b>c</b>) Number of leaf nodes, descendants and transmitted messages at each level.</p>
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<p>Success rate.</p>
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<p>Memory size.</p>
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4998 KiB  
Article
Electronic Properties of DNA-Based Schottky Barrier Diodes in Response to Alpha Particles
by Hassan Maktuff Jaber Al-Ta'ii, Vengadesh Periasamy and Yusoff Mohd Amin
Sensors 2015, 15(5), 11836-11853; https://doi.org/10.3390/s150511836 - 21 May 2015
Cited by 14 | Viewed by 6886
Abstract
Detection of nuclear radiation such as alpha particles has become an important field of research in recent history due to nuclear threats and accidents. In this context; deoxyribonucleic acid (DNA) acting as an organic semiconducting material could be utilized in a metal/semiconductor Schottky [...] Read more.
Detection of nuclear radiation such as alpha particles has become an important field of research in recent history due to nuclear threats and accidents. In this context; deoxyribonucleic acid (DNA) acting as an organic semiconducting material could be utilized in a metal/semiconductor Schottky junction for detecting alpha particles. In this work we demonstrate for the first time the effect of alpha irradiation on an Al/DNA/p-Si/Al Schottky diode by investigating its current-voltage characteristics. The diodes were exposed for different periods (0–20 min) of irradiation. Various diode parameters such as ideality factor, barrier height, series resistance, Richardson constant and saturation current were then determined using conventional, Cheung and Cheung’s and Norde methods. Generally, ideality factor or n values were observed to be greater than unity, which indicates the influence of some other current transport mechanism besides thermionic processes. Results indicated ideality factor variation between 9.97 and 9.57 for irradiation times between the ranges 0 to 20 min. Increase in the series resistance with increase in irradiation time was also observed when calculated using conventional and Cheung and Cheung’s methods. These responses demonstrate that changes in the electrical characteristics of the metal-semiconductor-metal diode could be further utilized as sensing elements to detect alpha particles. Full article
(This article belongs to the Special Issue Next-Generation Nucleic Acid Sensors)
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<p>(<b>a</b>) Cross sectional and (<b>b</b>) top view of two Al/DNA/Si/Al surface-type Schottky diodes fabricated for further electrical characterization.</p>
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<p>Relationship between I and V for forward and reverse biases at (<b>a</b>) 2, 4, 6, 8, 10 and 20 min and (<b>b</b>) measured after 24 h.</p>
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<p>I-V characteristics of the Al/DNA/p-Si Schottky diode at room temperature. (<b>a</b>) before and after (<b>b</b>) 24 h.</p>
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<p>The relation between the series resistance and voltage generated using conventional method (<b>a</b>) before and (<b>b</b>) after 24 h.</p>
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<p>H(I) and dV/dln I <span class="html-italic">vs.</span> I obtained from forward bias I-V characteristics of Al/DNA/Si/Al Schottky junction diode diode (<b>a</b>,<b>b</b>) before and (<b>c</b>,<b>d</b>) after 24 h.</p>
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<p>F(V) <span class="html-italic">vs.</span> voltage plots of non-radiated and radiated Al/DNA/Si Schottky diodes. diodes (<b>a</b>) before and (<b>b</b>) after 24 h.</p>
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<p>The relation between the series resistance and irradiation time.</p>
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<p>The relation between the ideality factor and barrier height with irradiation time. (<b>a</b>) before and (<b>b</b>) after 24 h.</p>
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<p>Double logarithmic plots of the Al/DNA/p-Si/Al junctions (<b>a</b>) before and (<b>b</b>) after 24 h.</p>
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<p>I-V curve of the Al/Si/Al junction in the absence of the DNA molecule.</p>
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<p>The relation between the saturation current and irradiation time (<b>a</b>) before and (<b>b</b>) after 24 h.</p>
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<p>Irradiation time dependent Richardson constant for the MDM design (<b>a</b>) before and (<b>b</b>) after 24 h.</p>
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2164 KiB  
Article
Highly Stable Liquid Metal-Based Pressure Sensor Integrated with a Microfluidic Channel
by Taekeon Jung and Sung Yang
Sensors 2015, 15(5), 11823-11835; https://doi.org/10.3390/s150511823 - 21 May 2015
Cited by 99 | Viewed by 13215
Abstract
Pressure measurement is considered one of the key parameters in microfluidic systems. It has been widely used in various fields, such as in biology and biomedical fields. The electrical measurement method is the most widely investigated; however, it is unsuitable for microfluidic systems [...] Read more.
Pressure measurement is considered one of the key parameters in microfluidic systems. It has been widely used in various fields, such as in biology and biomedical fields. The electrical measurement method is the most widely investigated; however, it is unsuitable for microfluidic systems because of a complicated fabrication process and difficult integration. Moreover, it is generally damaged by large deflection. This paper proposes a thin-film-based pressure sensor that is free from these limitations, using a liquid metal called galinstan. The proposed pressure sensor is easily integrated into a microfluidic system using soft lithography because galinstan exists in a liquid phase at room temperature. We investigated the characteristics of the proposed pressure sensor by calibrating for a pressure range from 0 to 230 kPa (R2 > 0.98) using deionized water. Furthermore, the viscosity of various fluid samples was measured for a shear-rate range of 30–1000 s1. The results of Newtonian and non-Newtonian fluids were evaluated using a commercial viscometer and normalized difference was found to be less than 5.1% and 7.0%, respectively. The galinstan-based pressure sensor can be used in various microfluidic systems for long-term monitoring with high linearity, repeatability, and long-term stability. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic of the device. The pressure sensor integrated in the microfluidic device consists of three PDMS layers: sensor, thin-film, and fluidic-channel layers. The sensor channel is filled with galinstan.</p>
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<p>Working principle of a galinstan-based pressure sensor. (<b>a</b>) When the membrane is subjected to pressure, the electrical resistance of the pressure sensor gets increased due to reduction in the cross-sectional area; (<b>b</b>) Schematic of the Wheatstone bridge circuit. The pressure is estimated by measuring the voltage difference between nodes A and B.</p>
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<p>Calibration of the pressure sensors. Calibration results of each pressure sensor in the proposed device with pressure of (<b>a</b>) 12 kPa and (<b>b</b>) 230 kPa. From the linear regression analysis, the calibration results show large linearity (<span class="html-italic">R</span><sup>2</sup> &gt; 0.999 for 12 kPa, and <span class="html-italic">R</span><sup>2</sup> &gt; 0.982 for 230 kPa).</p>
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<p>Long-term stability test. (<b>a</b>) Pressure of 30 kPa is repeatedly applied to the microfluidic channel using a pneumatic pump at a 5-s interval; (<b>b</b>) Calibration is conducted four times for 30 days, and the normalized difference between the initial data and the data measured each day is less than 5%.</p>
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<p>(<b>a</b>) Real-time monitoring of signal change. Calibration was repeatedly conducted after applying high pressure (350 kPa); (<b>b</b>) Magnified graph of the initial calibration results. The voltage signal increased with the pressure increase from 0 to 12 kPa; (<b>c</b>) Time-lapse sequential image obtained by a microscope. Most of the galinstan was expelled to the connection part under 350 kPa of pressure, but the galinstan totally refilled the sensor part immediately after the applied pressure was removed.</p>
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<p>Viscosity measurement results of (<b>a</b>) 5%, 10%, and 15% SDS solution as a Newtonian fluid and (<b>b</b>) 0.3%, 0.4%, and 0.5% PEO solutions under a shear-rate range from 30 s<sup>−1</sup> to 1000 s<sup>−1</sup>. The data measured by the galinstan-based pressure sensor are confirmed by a commercial cone and plate viscometer.</p>
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Article
Modeling of Acoustic Emission Signal Propagation in Waveguides
by Andreea-Manuela Zelenyak, Marvin A. Hamstad and Markus G. R. Sause
Sensors 2015, 15(5), 11805-11822; https://doi.org/10.3390/s150511805 - 21 May 2015
Cited by 56 | Viewed by 9573
Abstract
Acoustic emission (AE) testing is a widely used nondestructive testing (NDT) method to investigate material failure. When environmental conditions are harmful for the operation of the sensors, waveguides are typically mounted in between the inspected structure and the sensor. Such waveguides can be [...] Read more.
Acoustic emission (AE) testing is a widely used nondestructive testing (NDT) method to investigate material failure. When environmental conditions are harmful for the operation of the sensors, waveguides are typically mounted in between the inspected structure and the sensor. Such waveguides can be built from different materials or have different designs in accordance with the experimental needs. All these variations can cause changes in the acoustic emission signals in terms of modal conversion, additional attenuation or shift in frequency content. A finite element method (FEM) was used to model acoustic emission signal propagation in an aluminum plate with an attached waveguide and was validated against experimental data. The geometry of the waveguide is systematically changed by varying the radius and height to investigate the influence on the detected signals. Different waveguide materials were implemented and change of material properties as function of temperature were taken into account. Development of the option of modeling different waveguide options replaces the time consuming and expensive trial and error alternative of experiments. Thus, the aim of this research has important implications for those who use waveguides for AE testing. Full article
(This article belongs to the Special Issue Acoustic Waveguide Sensors)
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<p>Overview of the present study.</p>
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<p>PLB‚ Cosine bell’ source characteristics.</p>
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<p>Geometry model for the (<b>a</b>) plate and sensor (<b>b</b>) plate, WG and sensor and (<b>c</b>) details of the conical sensor used [<a href="#B5-sensors-15-11805" class="html-bibr">5</a>].</p>
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<p>Photography of the experimental setup of the (<b>a</b>) plate and (<b>b</b>) details of the conical sensor.</p>
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<p>Photography of the experimental setup of (<b>a</b>) plate and waveguide and (<b>b</b>) photography of the conical waveguide.</p>
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<p>Experimental results <span class="html-italic">vs.</span> simulation results for (<b>a</b>) plate signal-reference (<b>b</b>) WG signal.</p>
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<p>Experimental WG signals generated by different PLBs.</p>
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<p>Comparison between (<b>a</b>) simulated signals and (<b>b</b>) simulation signals with a detail view of the S<sub>0</sub> mode and (<b>c</b>) FFTs of simulated and experimental signals.</p>
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<p>Schematic geometry of the waveguide cases studied.</p>
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<p>Time domain signals of the (<b>a</b>) extreme WG diameter cases and (<b>b</b>) intermediate WG diameter cases.</p>
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<p>(<b>a</b>) Wavelet transform of reference case and (<b>b</b>) maximum A<sub>0</sub> amplitude as function of WG diameter and frequency.</p>
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<p>Magnitude (<b>a</b>) of S<sub>0</sub> mode from WT (<b>b</b>) from FFT.</p>
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<p>Calculated signals for (<b>a</b>) extreme WG length cases; (<b>b</b>) all cases and (<b>c</b>) frequency spectra thereof.</p>
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<p>Calculated signals for (<b>a</b>) metallic waveguides and (<b>b</b>) ceramic waveguide.</p>
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<p>(<b>a</b>) Calculated FFT spectra and (<b>b</b>) A<sub>0</sub> transmission coefficients.</p>
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<p>(<b>a</b>) Temperature distribution in plate and waveguide and (<b>b</b>) comparison of signals at room temperature and with elevated temperature.</p>
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2290 KiB  
Review
ZnO Nanostructure-Based Intracellular Sensor
by Muhammad H. Asif, Bengt Danielsson and Magnus Willander
Sensors 2015, 15(5), 11787-11804; https://doi.org/10.3390/s150511787 - 21 May 2015
Cited by 27 | Viewed by 6777
Abstract
Recently ZnO has attracted much interest because of its usefulness for intracellular measurements of biochemical species by using its semiconducting, electrochemical, catalytic properties and for being biosafe and biocompatible. ZnO thus has a wide range of applications in optoelectronics, intracellular nanosensors, transducers, energy [...] Read more.
Recently ZnO has attracted much interest because of its usefulness for intracellular measurements of biochemical species by using its semiconducting, electrochemical, catalytic properties and for being biosafe and biocompatible. ZnO thus has a wide range of applications in optoelectronics, intracellular nanosensors, transducers, energy conversion and medical sciences. This review relates specifically to intracellular electrochemical (glucose and free metal ion) biosensors based on functionalized zinc oxide nanowires/nanorods. For intracellular measurements, the ZnO nanowires/nanorods were grown on the tip of a borosilicate glass capillary (0.7 µm in diameter) and functionalized with membranes or enzymes to produce intracellular selective metal ion or glucose sensors. Successful intracellular measurements were carried out using ZnO nanowires/nanorods grown on small tips for glucose and free metal ions using two types of cells, human fat cells and frog oocytes. The sensors in this study were used to detect real-time changes of metal ions and glucose across human fat cells and frog cells using changes in the electrochemical potential at the interface of the intracellular micro-environment. Such devices are helpful in explaining various intracellular processes involving ions and glucose. Full article
(This article belongs to the Special Issue Intracellular Sensing)
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<p>Field emission scanning electron microscope images at different magnifications of the Ag-coated glass tip without (<b>A</b>) and with (<b>B</b>,<b>C</b>) grown ZnO nanorods [<a href="#B3-sensors-15-11787" class="html-bibr">3</a>,<a href="#B5-sensors-15-11787" class="html-bibr">5</a>].</p>
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<p>Schematic experimental setup for the intracellular potentiometric measurements.</p>
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<p>A calibration curve showing the electrochemical potential difference <span class="html-italic">vs.</span> the Ag/AgCl reference electrode in response to the glucose concentration using the functionalized ZnO nanorods as working electrode [<a href="#B3-sensors-15-11787" class="html-bibr">3</a>].</p>
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<p>Shows potentiometric response <span class="html-italic">vs.</span> time as the concentrations of Ca<sup>2+</sup> concentration is changed in the buffer surrounding the cell for the case where for partial insertion of the functionalized ZnO nanorods. The insert shows a typical the calibration curve of the present working electrode [<a href="#B5-sensors-15-11787" class="html-bibr">5</a>].</p>
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<p>Schematic diagram showing the principle of measurements with different penetration depths during the experiment (<b>a</b>) the case with partial insertion of the functionalized ZnO nanorods and in (<b>b</b>) when all the functionalized ZnO nanorods are inserted inside the cell [<a href="#B5-sensors-15-11787" class="html-bibr">5</a>].</p>
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<p>Scanning electron microscopy images showing the working electrode after intracellular measurements at two different magnifications.</p>
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<p>A calibration curve showing the electrochemical potential difference between the Mg<sup>2+</sup>-selective ZnO nanorod-covered and the Ag/AgCl reference microelectrode <span class="html-italic">vs.</span> the Mg<sup>2+</sup> concentration. Insets show images of human adipocytes and frog oocytes with arrows pointing at measured intracellular levels of Mg<sup>2+</sup> for the respective cells [<a href="#B17-sensors-15-11787" class="html-bibr">17</a>].</p>
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<p>The output response with (black and green) and without (red) interfering ions [<a href="#B17-sensors-15-11787" class="html-bibr">17</a>].</p>
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<p>A calibration curve showing the electrochemical potential difference between the Na<sup>+</sup>-selective ZnO nanorod and the Ag/AgCl reference microelectrodes <span class="html-italic">vs.</span> the Na<sup>+</sup> concentration [<a href="#B18-sensors-15-11787" class="html-bibr">18</a>].</p>
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<p>Experimental setup for simultaneous test solution injection and potentiometric measurements, (<b>A</b>) Schematic illustration of the setup and (<b>B</b>) Photography of Xenopus oocyte penetrated by the reference electrode (left), measurement electrode (right), and injector (middle).</p>
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<p>Representative K<sup>+</sup> current recordings and the corresponding I(V) curves measured electrophysiological in Kv channel expressing <span class="html-italic">Xenopus</span> oocytes. (<b>A</b>) Shows data for control oocytes and (<b>B</b>–<b>D</b>) for oocytes injected with indicated test solution. The holding potential was set to −80 mV and test pulses ranging from −80 to + 50 mV. The current generated by stepping to 0 mV is marked in red in each recording [<a href="#B26-sensors-15-11787" class="html-bibr">26</a>].</p>
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<p>Intracellular K<sup>+</sup> concentrations in Kv channel-expressing <span class="html-italic">Xenopus</span>oocytes measured with electrophysiological and K<sup>+</sup>-selective microelectrode methods. The Field emission scanning electron microscopy images of the K<sup>+</sup>-selective microelectrode before (<b>a</b>,<b>b</b>) and after intracellular measurements (<b>c</b>). Data points are expressed as mean values for control oocytes and oocytes injected with 50 nL of indicated test solution (<b>d</b>). Error bars show SE. <span class="html-italic">n</span> = 3–5) [<a href="#B26-sensors-15-11787" class="html-bibr">26</a>].</p>
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2054 KiB  
Article
Building Keypoint Mappings on Multispectral Images by a Cascade of Classifiers with a Resurrection Mechanism
by Yong Li, Jing Jing, Hongbin Jin and Wei Qiao
Sensors 2015, 15(5), 11769-11786; https://doi.org/10.3390/s150511769 - 21 May 2015
Cited by 1 | Viewed by 4846
Abstract
Inspired by the boosting technique for detecting objects, this paper proposes a cascade structure with a resurrection mechanism to establish keypoint mappings on multispectral images. The cascade structure is composed of four steps by utilizing best bin first (BBF), color and intensity distribution [...] Read more.
Inspired by the boosting technique for detecting objects, this paper proposes a cascade structure with a resurrection mechanism to establish keypoint mappings on multispectral images. The cascade structure is composed of four steps by utilizing best bin first (BBF), color and intensity distribution of segment (CIDS), global information and the RANSAC process to remove outlier keypoint matchings. Initial keypoint mappings are built with the descriptors associated with keypoints; then, at each step, only a small number of keypoint mappings of a high confidence are classified to be incorrect. The unclassified keypoint mappings will be passed on to subsequent steps for determining whether they are correct. Due to the drawback of a classification rule, some correct keypoint mappings may be misclassified as incorrect at a step. Observing this, we design a resurrection mechanism, so that they will be reconsidered and evaluated by the rules utilized in subsequent steps. Experimental results show that the proposed cascade structure combined with the resurrection mechanism can effectively build more reliable keypoint mappings on multispectral images than existing methods. Full article
(This article belongs to the Section Remote Sensors)
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<p>The example of resurrection. (<b>a</b>) The mappings whose <span class="html-italic">cg</span> is three in Step 1; (<b>b</b>) the mappings whose <span class="html-italic">cg</span> is two in Step 1; (<b>c</b>) the mappings whose <span class="html-italic">cg</span> is one in Step 1; (<b>d</b>) the mappings resurrected from (c) in Step 2.</p>
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<p>The proposed cascade structure with the resurrection mechanism.</p>
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<p>Illustration of sampling the two segments <span class="html-italic">A</span><sub>1</sub><span class="html-italic">A</span><sub>5</sub> and <span class="html-italic">B</span><sub>1</sub><span class="html-italic">B</span><sub>5</sub>.</p>
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<p>(<b>a</b>) <span class="html-italic">I<sub>r</sub></span>(<span class="html-italic">x, y</span>); (<b>b</b>) , <math display="inline"> <semantics id="sm64"> <mrow> <msubsup> <mi>I</mi> <mi>t</mi> <mi>T</mi></msubsup> <mrow> <mo>(</mo> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi></mrow> <mo>)</mo></mrow></mrow></semantics></math>, illustrating three keypoint mappings that are to be assessed. The three mappings determine an affine transformation <span class="html-italic">T</span>, and then, we compute the similarity metric between <span class="html-italic">I<sub>r</sub></span>(<span class="html-italic">x, y</span>) and <math display="inline"> <semantics id="sm62"> <mrow> <msubsup> <mi>I</mi> <mi>t</mi> <mi>T</mi></msubsup> <mrow> <mo>(</mo> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi></mrow> <mo>)</mo></mrow></mrow></semantics></math> with <a href="#FD7" class="html-disp-formula">Equation (7)</a>; (<b>c</b>) Overlapped edge maps, showing the overlapped edge maps where green pixels are from visible image <span class="html-italic">I<sub>r</sub></span>(<span class="html-italic">x, y</span>) and red pixels are from IR image <math display="inline"> <semantics id="sm63"> <mrow> <msubsup> <mi>I</mi> <mi>t</mi> <mi>T</mi></msubsup> <mrow> <mo>(</mo> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi></mrow> <mo>)</mo></mrow></mrow></semantics></math>. When <span class="html-italic">T</span> is close to the ground truth, a majority of edge pixels is expected to be overlapped, resulting in a high similarity metric.</p>
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<p>On each dataset, from left to right: the number of pending mappings, the number of resurrected mappings and the number of resurrected correct mappings.</p>
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<p>Matching result on an image pair from ‘EOIR’ taken by ourselves. The infrared image is transformed to investigate the matching performance under rotation. (<b>a</b>) The proposed method performs well under rotation; and (<b>b</b>) SIFT + RANSAC yields some incorrect matches due to the repeating windows of buildings. Such incorrect mappings are very difficult to remove, since the local gradient patterns (descriptors) of the windows lying in different position are similar to each other.</p>
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<p>Matching result on a remote sensing image pair taken during the 2008 Sichuan earthquake (on a scale of 1:10,000) from the dataset ‘EOIR’. (<b>a</b>) The proposed method; (<b>b</b>) SIFT + RANSAC. The clouds appearing in the IR image do not generate incorrect matches for the proposed method, since they have been removed step by step in the cascade structure. SIFT + RANSAC barely generates a keypoint mapping due to the lack of texture in the cloud area (hence, fewer keypoints). The repeating structure of image content causes mismatches for SIFT + RANSAC.</p>
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<p>Matching results on two image pairs from the OSUColor and Thermal Database. (<b>a</b>) Proposed method; and (<b>c</b>) proposed method results; (<b>b</b>) SIFT + RANSAC; and (<b>d</b>) SIFT + RANSAC results. The top two images were taken at the same time; the bottom were taken at different times. Although this image pair is multispectral, its property in some local area is close to being single-spectrum. For example, the house ceiling is brighter than other areas on both visible and IR images, which means the similarity of the local pattern. SIFT + RANSAC contain some correct matches.</p>
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4424 KiB  
Article
An Ultra-Low Power Wireless Sensor Network for Bicycle Torque Performance Measurements
by Sadik K. Gharghan, Rosdiadee Nordin and Mahamod Ismail
Sensors 2015, 15(5), 11741-11768; https://doi.org/10.3390/s150511741 - 21 May 2015
Cited by 25 | Viewed by 9854
Abstract
In this paper, we propose an energy-efficient transmission technique known as the sleep/wake algorithm for a bicycle torque sensor node. This paper aims to highlight the trade-off between energy efficiency and the communication range between the cyclist and coach. Two experiments were conducted. [...] Read more.
In this paper, we propose an energy-efficient transmission technique known as the sleep/wake algorithm for a bicycle torque sensor node. This paper aims to highlight the trade-off between energy efficiency and the communication range between the cyclist and coach. Two experiments were conducted. The first experiment utilised the Zigbee protocol (XBee S2), and the second experiment used the Advanced and Adaptive Network Technology (ANT) protocol based on the Nordic nRF24L01 radio transceiver chip. The current consumption of ANT was measured, simulated and compared with a torque sensor node that uses the XBee S2 protocol. In addition, an analytical model was derived to correlate the sensor node average current consumption with a crank arm cadence. The sensor node achieved 98% power savings for ANT relative to ZigBee when they were compared alone, and the power savings amounted to 30% when all components of the sensor node are considered. The achievable communication range was 65 and 50 m for ZigBee and ANT, respectively, during measurement on an outdoor cycling track (i.e., velodrome). The conclusions indicate that the ANT protocol is more suitable for use in a torque sensor node when power consumption is a crucial demand, whereas the ZigBee protocol is more convenient in ensuring data communication between cyclist and coach. Full article
(This article belongs to the Section Sensor Networks)
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<p>Conceptual research framework for the bicycle sensor torque measurement system.</p>
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<p>Bicycle network topology: data transmitted through one router.</p>
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<p>Schematic and hardware diagram of the whole WSN.</p>
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<p>Strain gauge: (<b>a</b>) Wheatstone bridge; (<b>b</b>) installation on the top side of the crankset, and (<b>c</b>) installation on the bottom side of the crankset.</p>
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<p>The data packet of IEEE 802.15.4: (<b>a</b>) data packet 127 bytes; (<b>b</b>) data packet adopted in this experiment 272 bits.</p>
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<p>The data packet of ANT RF transceiver: (<b>a</b>) enhanced Shockburst; (<b>b</b>) enhanced Shockburst adopted in this experiment 97 bits.</p>
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<p>Transition time and current consumption for each mode of ANT.</p>
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<p>Torque sensor node components installed on the bicycle chainring (<b>a</b>) sensors scheme and (<b>b</b>) functionality scheme.</p>
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<p>Atmega 328p: current consumption <span class="html-italic">vs.</span> operating frequency.</p>
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<p>Experimental configuration measurements of current consumption for ANT module.</p>
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<p>Active current consumption of ANT module during transmission torque data of the bicycle.</p>
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<p>Active current consumption of XBee series 2 module during transmission torque data of the bicycle.</p>
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<p>The relationship between the strain gauges output voltage and torque produced in the crank arm.</p>
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<p>Simulation results for: (<b>a</b>) torque values of power and dead stroke phases and average torque every crank arm rotation, and the peak and average current consumption of; (<b>b</b>) XBee S2 module; (<b>c</b>) ANT module; (<b>d</b>) microcontroller Atmega 328p; (<b>e</b>) Magnetic sensor 1; (<b>f</b>) magnetic sensor 2; (<b>g</b>) instrumentation amplifier AD623; and (<b>h</b>) strain gauge sensors.</p>
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<p>Average current consumption of the ANT and XBee S2 for different bicycle cadence.</p>
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<p>Power saving based on ANT module.</p>
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<p>Current consumption comparison between ANT and XBee S2 modules with and without applying <span class="html-italic">sleep/wake</span> algorithm.</p>
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<p>Average current consumption of each component in the torque sensor node as a function of bicycle cadence by applying the <span class="html-italic">sleep/wake</span> algorithm.</p>
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<p>Power saving of the torque sensor node based ANT module.</p>
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<p>Estimated battery lifetime <span class="html-italic">vs.</span> usable battery capacity in the torque sensor node for three cases based on the use of ANT and XBee S2 modules.</p>
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1286 KiB  
Article
Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data
by Tobias Nef, Prabitha Urwyler, Marcel Büchler, Ioannis Tarnanas, Reto Stucki, Dario Cazzoli, René Müri and Urs Mosimann
Sensors 2015, 15(5), 11725-11740; https://doi.org/10.3390/s150511725 - 21 May 2015
Cited by 73 | Viewed by 9485
Abstract
Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, [...] Read more.
Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Starting with the (reformatted) raw data, a clustering further preprocessed the data before the actual classification was performed. Finally, the computed result was displayed.</p>
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<p>Additionally to the activities of daily living (ADL) classifier, a parallel visitor classifier was used. The results of the two classifiers were then merged.</p>
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<p>A token was calculated based on all passive infrared (PIR) values. Whenever the token changed (inactive states of all motion sensors were neglected), a change point was set. Periods between two change points were then compressed.</p>
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<p>To provide the classifier with contextual information about overlapping time periods, additional feature columns were introduced.</p>
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<p>Distribution of PIR recordings during 24 h of measurements for one volunteer. The x-axis shows the time of the day and the y-axis the normalized number of PIR recordings.</p>
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1482 KiB  
Article
Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems
by Zhendong Yin, Kai Cui, Zhilu Wu and Liang Yin
Sensors 2015, 15(5), 11701-11724; https://doi.org/10.3390/s150511701 - 21 May 2015
Cited by 30 | Viewed by 6435
Abstract
The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach [...] Read more.
The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches. Full article
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
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<p>Illustration of conventional TOA approaches.</p>
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<p>Procedure of entropy-based TOA estimation.</p>
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<p>RMSE as a function of the threshold factor in IEEE802.15.4a. (<b>a</b>) CM1; (<b>b</b>) CM2; (<b>c</b>) CM3; (<b>d</b>) CM4.</p>
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<p>Illustration of the procedure in one realization (CM1 channel, SNR = 10 dB). (<b>a</b>) Received signal; (<b>b</b>) threshold crossing of nth sample; (<b>c</b>) entropy of received signal.</p>
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<p>Entropy of received signal of CM1 to CM4 (<span class="html-italic">SNR</span> = 10 dB). (<b>a</b>) CM1; (<b>b</b>) CM2; (<b>c</b>) CM3; (<b>d</b>) CM4.</p>
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<p>Comparison of proposed method and conventional methods in CM1 to CM4, −10–20 dB. (<b>a</b>) CM1; (<b>b</b>) CM2; (<b>c</b>) CM3; (<b>d</b>) CM4.</p>
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<p>Illustration of ε-range.</p>
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<p>Flow chart of SVM regression.</p>
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<p>Mitigation results of CM1 to CM4 with SVM. (<b>a</b>) CM1; (<b>b</b>) CM2; (<b>c</b>) CM3; (<b>d</b>) CM4.</p>
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<p>CDF of residual ranging error without mitigation, and using SVM based mitigation.</p>
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<p>Connection between threshold factor and ranging error.</p>
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<p>Shape of the entropy series curve with various threshold factors (CM1, SNR = 10 dB) Threshold factor (<b>a</b>) 0.1; (<b>b</b>) 0.2; (<b>c</b>) 0.3; (<b>d</b>) 1; (<b>e</b>)1.5; (<b>f</b>) 2; (<b>g</b>) 2.5; (<b>h</b>) 3; (<b>i</b>) 3.5; (<b>j</b>) 10.</p>
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<p>Shape of the entropy series curve with various threshold factors (CM1, SNR = 10 dB) Threshold factor (<b>a</b>) 0.1; (<b>b</b>) 0.2; (<b>c</b>) 0.3; (<b>d</b>) 1; (<b>e</b>)1.5; (<b>f</b>) 2; (<b>g</b>) 2.5; (<b>h</b>) 3; (<b>i</b>) 3.5; (<b>j</b>) 10.</p>
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<p>Shape of entropy series curve with various threshold factors (CM2, SNR = 10 dB) Threshold factor (<b>a</b>) 0.1; (<b>b</b>) 0.2; (<b>c</b>)0.3; (<b>d</b>) 1; (<b>e</b>)1.5; (<b>f</b>)2; (<b>g</b>) 2.5; (<b>h</b>) 3; (<b>i</b>) 3.5; (<b>j</b>) 10.</p>
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<p>Shape of entropy series curve with various threshold factors (CM2, SNR = 10 dB) Threshold factor (<b>a</b>) 0.1; (<b>b</b>) 0.2; (<b>c</b>)0.3; (<b>d</b>) 1; (<b>e</b>)1.5; (<b>f</b>)2; (<b>g</b>) 2.5; (<b>h</b>) 3; (<b>i</b>) 3.5; (<b>j</b>) 10.</p>
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1900 KiB  
Article
Optimal Self-Tuning PID Controller Based on Low Power Consumption for a Server Fan Cooling System
by Chengming Lee and Rongshun Chen
Sensors 2015, 15(5), 11685-11700; https://doi.org/10.3390/s150511685 - 20 May 2015
Cited by 32 | Viewed by 10350
Abstract
Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID) controller, combining a PID neural network (PIDNN) with fan-power-based optimization in [...] Read more.
Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID) controller, combining a PID neural network (PIDNN) with fan-power-based optimization in the transient-state temperature response in the time domain, for a server fan cooling system. Because the thermal model of the cooling system is nonlinear and complex, a server mockup system simulating a 1U rack server was constructed and a fan power model was created using a third-order nonlinear curve fit to determine the cooling power consumption by the fan speed control. PIDNN with a time domain criterion is used to tune all online and optimized PID gains. The proposed controller was validated through experiments of step response when the server operated from the low to high power state. The results show that up to 14% of a server’s fan cooling power can be saved if the fan control permits a slight temperature response overshoot in the electronic components, which may provide a time-saving strategy for tuning the PID controller to control the server fan speed during low fan power consumption. Full article
(This article belongs to the Section Physical Sensors)
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<p>Server mockup system.</p>
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<p>Fan power model.</p>
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<p>The block diagram of the self-tuning proportional-integral-derivative (PID) control system.</p>
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<p>Structure of PIDNN for executing an online self-tuning PID controller for a server.</p>
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<p>(<b>a</b>) Configuration of the server fan control system. Add a descriptive label of the figure here; (<b>b</b>) Server mockup system.</p>
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<p>The flow chart of PID self-tuning.</p>
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<p>The result of PID self-tuning. (<b>a</b>) The process of P gain; (<b>b</b>) The process of I gain; (<b>c</b>) The process of D gain.</p>
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<p>The result of PID self-tuning. (<b>a</b>) The process of P gain; (<b>b</b>) The process of I gain; (<b>c</b>) The process of D gain.</p>
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<p>Control results of various PID controllers for CPU1. (<b>a</b>) The temperature responses of CPU1 by the PID controller; (<b>b</b>) The control efforts of Fans 3 and 4 by the PID controller.</p>
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<p>Control results of PID controllers with an overshoot. (<b>a</b>) The temperature responses of the electronic components by the PID controllers; (<b>b</b>) The control efforts of the fans by the PID controllers.</p>
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<p>Control results of PID controllers without an overshoot. (<b>a</b>) The temperature responses of the electronic components by the PID controllers; (<b>b</b>) The control efforts of the fans by the PID controllers.</p>
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3427 KiB  
Article
Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)
by Shaharil Mad Saad, Allan Melvin Andrew, Ali Yeon Md Shakaff, Abdul Rahman Mohd Saad, Azman Muhamad Yusof @ Kamarudin and Ammar Zakaria
Sensors 2015, 15(5), 11665-11684; https://doi.org/10.3390/s150511665 - 20 May 2015
Cited by 57 | Viewed by 11242
Abstract
Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed [...] Read more.
Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity. Full article
(This article belongs to the Section Sensor Networks)
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<p>System architecture for real-time IAQ monitoring.</p>
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<p>Block diagram of the sensing node.</p>
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<p>Prototype of sensor module.</p>
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<p>Block diagram of base station.</p>
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<p>GUI visualization for IAQ monitoring system.</p>
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<p>Calibration setup.</p>
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<p>Scatterplot of CO<sub>2</sub> sensor calibration.</p>
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<p>(<b>a</b>) NO<sub>2</sub> data; (<b>b</b>) Temperature data; (<b>c</b>) Humidity data.</p>
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<p>Data collection process.</p>
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<p>(<b>a</b>) Ambient environment; (<b>b</b>) Chemical presence; (<b>c</b>) Fragrance presence; (<b>d</b>) Human activity; (<b>e</b>) Food and beverages</p>
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<p>(<b>a</b>) Ambient environment; (<b>b</b>) Chemical presence; (<b>c</b>) Fragrance presence; (<b>d</b>) Human activity; (<b>e</b>) Food and beverages</p>
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<p>PCA plot of five different sources of IAQ pollutant.</p>
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<p>ANN model for source influencing IAQ.</p>
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2004 KiB  
Article
Medically Relevant Assays with a Simple Smartphone and Tablet Based Fluorescence Detection System
by Piotr Wargocki, Wei Deng, Ayad G. Anwer and Ewa M. Goldys
Sensors 2015, 15(5), 11653-11664; https://doi.org/10.3390/s150511653 - 20 May 2015
Cited by 23 | Viewed by 16638
Abstract
Cell phones and smart phones can be reconfigured as biomedical sensor devices but this requires specialized add-ons. In this paper we present a simple cell phone-based portable bioassay platform, which can be used with fluorescent assays in solution. The system consists of a [...] Read more.
Cell phones and smart phones can be reconfigured as biomedical sensor devices but this requires specialized add-ons. In this paper we present a simple cell phone-based portable bioassay platform, which can be used with fluorescent assays in solution. The system consists of a tablet, a polarizer, a smart phone (camera) and a box that provides dark readout conditions. The assay in a well plate is placed on the tablet screen acting as an excitation source. A polarizer on top of the well plate separates excitation light from assay fluorescence emission enabling assay readout with a smartphone camera. The assay result is obtained by analysing the intensity of image pixels in an appropriate colour channel. With this device we carried out two assays, for collagenase and trypsin using fluorescein as the detected fluorophore. The results of collagenase assay with the lowest measured concentration of 3.75 µg/mL and 0.938 µg in total in the sample were comparable to those obtained by a microplate reader. The lowest measured amount of trypsin was 930 pg, which is comparable to the low detection limit of 400 pg for this assay obtained in a microplate reader. The device is sensitive enough to be used in point-of-care medical diagnostics of clinically relevant conditions, including arthritis, cystic fibrosis and acute pancreatitis. Full article
(This article belongs to the Special Issue Smartphone-Based Sensors for Non-Invasive Physiological Monitoring)
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<p>(<b>a</b>) Schematic diagram of the device cross-section; (<b>b</b>) Excitation and emission spectra of fluorescein in comparison to a typical RGB colour range.</p>
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<p>Image processing steps. (<b>a</b>) Initial image with the AOA rectangle highlighted; (<b>b</b>) Green channel image (in grayscale) and a representative cross-section of screen intensity presenting noise amplitude; (<b>c</b>) Image filtered with adaptive Wiener filter and a representative cross-section of intensity presenting noise amplitude; (<b>d</b>) Pixel intensity map of the AOA after adaptive filtering.</p>
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<p>The assay workflow.</p>
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<p>Assay signal as a function of analyte concentration for trypsin and collagenase assays. (<b>a</b>) Trypsin assay with smartphone device; (<b>b</b>) Trypsin assay with Cary Eclipse readout with photomultiplier detector voltage option set to ‘Low’. Uncertainty of each data point is 0.1; (<b>c</b>) Trypsin assay with smartphone device at low concentrations; (<b>d</b>) Trypsin assay at low concentrations with Cary Eclipse readout with photomultiplier detector voltage option set to ‘Medium’. Uncertainty of each data point is 2.7; (<b>e</b>) Collagenase assay with smartphone device; (<b>f</b>) Collagenase assay with Cary Eclipse readout with photomultiplier detector voltage option set to ‘Low’. Uncertainty of each data point is 1.1.</p>
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2790 KiB  
Article
An Energy-Efficient Transmission Scheme for Real-Time Data in Wireless Sensor Networks
by Jin-Woo Kim, José Ramón Ramos Barrado and Dong-Keun Jeon
Sensors 2015, 15(5), 11628-11652; https://doi.org/10.3390/s150511628 - 20 May 2015
Cited by 13 | Viewed by 7257
Abstract
The Internet of things (IoT) is a novel paradigm where all things or objects in daily life can communicate with other devices and provide services over the Internet. Things or objects need identifying, sensing, networking and processing capabilities to make the IoT paradigm [...] Read more.
The Internet of things (IoT) is a novel paradigm where all things or objects in daily life can communicate with other devices and provide services over the Internet. Things or objects need identifying, sensing, networking and processing capabilities to make the IoT paradigm a reality. The IEEE 802.15.4 standard is one of the main communication protocols proposed for the IoT. The IEEE 802.15.4 standard provides the guaranteed time slot (GTS) mechanism that supports the quality of service (QoS) for the real-time data transmission. In spite of some QoS features in IEEE 802.15.4 standard, the problem of end-to-end delay still remains. In order to solve this problem, we propose a cooperative medium access scheme (MAC) protocol for real-time data transmission. We also evaluate the performance of the proposed scheme through simulation. The simulation results demonstrate that the proposed scheme can improve the network performance. Full article
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<p>TCP/IP stack and IoT protocol stack.</p>
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<p>IEEE 802.15.4 superframe structure.</p>
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<p>The proposed superframe structure.</p>
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<p>The flow chart of coordinator using the cooperative MAC structure.</p>
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<p>The format of a D2D request command frame.</p>
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<p>The format of the proposed beacon frame.</p>
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<p>The format of the D2D field.</p>
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<p>The format of D2D Descriptor.</p>
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<p>Message sequence chart for D2D allocation initiated by a device.</p>
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<p>Message sequence chart for D2D deallocation initiated by a device.</p>
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<p>Message sequence chart for D2D deallocation initiated by a coordinator.</p>
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<p>The flow of data frames and resource allocation in the proposed scheme.</p>
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<p>Throughput comparison.</p>
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<p>Variation of throughput with packet size given number of devices = 20.</p>
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<p>The throughput as a function of the average SNR.</p>
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<p>End-to-End delay according to beacon order.</p>
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<p>End-to-End delay for different node densities.</p>
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<p>The transmission success ratio <span class="html-italic">versus</span> the number of nodes.</p>
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<p>The energy consumption of device <span class="html-italic">versus</span> the average SNR.</p>
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<p>The energy consumption of device <span class="html-italic">versus</span> the distance between coordinator and end device.</p>
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<p>The total energy consumption of devices <span class="html-italic">versus</span> the number of maximum retransmissions.</p>
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<p>The energy consumption of device <span class="html-italic">versus</span> the number of devices in the network.</p>
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<p>The lifetime of devices which transmit and receive the real-time data under different percentage of duty cycle.</p>
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5237 KiB  
Article
A Negative Index Metamaterial-Inspired UWB Antenna with an Integration of Complementary SRR and CLS Unit Cells for Microwave Imaging Sensor Applications
by Mohammad Tariqul Islam, Md. Moinul Islam, Md. Samsuzzaman, Mohammad Rashed Iqbal Faruque and Norbahiah Misran
Sensors 2015, 15(5), 11601-11627; https://doi.org/10.3390/s150511601 - 20 May 2015
Cited by 79 | Viewed by 10304
Abstract
This paper presents a negative index metamaterial incorporated UWB antenna with an integration of complementary SRR (split-ring resonator) and CLS (capacitive loaded strip) unit cells for microwave imaging sensor applications. This metamaterial UWB antenna sensor consists of four unit cells along one axis, [...] Read more.
This paper presents a negative index metamaterial incorporated UWB antenna with an integration of complementary SRR (split-ring resonator) and CLS (capacitive loaded strip) unit cells for microwave imaging sensor applications. This metamaterial UWB antenna sensor consists of four unit cells along one axis, where each unit cell incorporates a complementary SRR and CLS pair. This integration enables a design layout that allows both a negative value of permittivity and a negative value of permeability simultaneous, resulting in a durable negative index to enhance the antenna sensor performance for microwave imaging sensor applications. The proposed MTM antenna sensor was designed and fabricated on an FR4 substrate having a thickness of 1.6 mm and a dielectric constant of 4.6. The electrical dimensions of this antenna sensor are 0.20 λ × 0.29 λ at a lower frequency of 3.1 GHz. This antenna sensor achieves a 131.5% bandwidth (VSWR < 2) covering the frequency bands from 3.1 GHz to more than 15 GHz with a maximum gain of 6.57 dBi. High fidelity factor and gain, smooth surface-current distribution and nearly omni-directional radiation patterns with low cross-polarization confirm that the proposed negative index UWB antenna is a promising entrant in the field of microwave imaging sensors. Full article
(This article belongs to the Special Issue Metamaterial-Inspired Sensors)
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<p>Front view of the complementary SRR unit cell with CLS.</p>
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<p>Simulation geometry of the proposed unit cell.</p>
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<p>Equivalent circuit of the proposed unit cell.</p>
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<p>Simulated results of S-parameters for the unit cell plotted in <a href="#sensors-15-11601-f001" class="html-fig">Figure 1</a>.</p>
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<p>The observed effective parameters. (<b>a</b>) Permeability; (<b>b</b>) Permittivity; (<b>c</b>) Refractive index of the proposed unit cell.</p>
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<p>The observed effective parameters. (<b>a</b>) Permeability; (<b>b</b>) Permittivity; (<b>c</b>) Refractive index of the proposed unit cell.</p>
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<p>The MTM antenna (<b>a</b>) One element; (<b>b</b>) Four element.</p>
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<p>The VSWR of the MTM antenna (<b>a</b>) One element; (<b>b</b>) Four element.</p>
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<p>The proposed UWB antenna (<b>a</b>) Front view; (<b>b</b>) Bottom view; (<b>c</b>) Cross sectional view.</p>
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<p>The proposed antenna. (<b>a</b>) No unit cell; (<b>b</b>) One unit cell; (<b>c</b>) Two unit cells; (<b>d</b>) Three unit cells; (<b>e</b>) Four unit cells (proposed).</p>
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<p>The effects of the unit cell of the patch on the VSWR.</p>
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<p>(<b>a</b>) The effects of various slots on the ground plane; (<b>b</b>) The effects of the ground plane size.</p>
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<p>The anechoic chamber for the proposed UWB antenna.</p>
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<p>Photograph of the fabricated UWB MTM antenna.</p>
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<p>Agilent N5227A PNA Network Analyzer.</p>
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<p>Comparison between simulated and measured VSWR.</p>
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<p>The measured radiation pattern at (<b>a</b>) 4 GHz; (<b>b</b>) 6 GHz; (<b>c</b>) 8 GHz; (<b>d</b>) 10 GHz.</p>
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<p>The measured radiation pattern at (<b>a</b>) 4 GHz; (<b>b</b>) 6 GHz; (<b>c</b>) 8 GHz; (<b>d</b>) 10 GHz.</p>
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<p>The surface current distribution at (<b>a</b>) 4 GHz; (<b>b</b>) 6 GHz; (<b>c</b>) 8 GHz; (<b>d</b>) 10 GHz; (<b>e</b>) Scale.</p>
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<p>The surface current distribution at (<b>a</b>) 4 GHz; (<b>b</b>) 6 GHz; (<b>c</b>) 8 GHz; (<b>d</b>) 10 GHz; (<b>e</b>) Scale.</p>
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<p>The measured peak gain of the proposed UWB MTM antenna.</p>
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<p>The radiation efficiency of the reported UWB antenna.</p>
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<p>The input pulse with τ = 67 ps.</p>
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<p>Pulse transmission analysis in different orientation of the proposed fractal UWB antenna. (<b>a</b>) Face-to-face; (<b>b</b>) Side-by-side Y; (<b>c</b>) Side-by-side X.</p>
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<p>Normalized received signals by virtual probe for Phi = 90° and varying Theta in the E-plane, (<b>a</b>) Antenna with probe; (<b>b</b>) Theta=0°; (<b>c</b>) Theta = 30°; (<b>d</b>) Theta = 45°; (<b>e</b>) Theta = 60°; (<b>f</b>) Theta = 90°.</p>
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<p>Normalized received signals by virtual probe for Phi = 90° and varying Theta in the E-plane, (<b>a</b>) Antenna with probe; (<b>b</b>) Theta=0°; (<b>c</b>) Theta = 30°; (<b>d</b>) Theta = 45°; (<b>e</b>) Theta = 60°; (<b>f</b>) Theta = 90°.</p>
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<p>The breast phantom with tumour simulants (<b>a</b>) Top view; (<b>b</b>) Cross view including two proposed UWB antennas.</p>
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<p>The simulated <math display="inline"> <semantics> <mrow> <mrow> <mo>|</mo> <mrow> <msubsup> <mi>S</mi> <mrow> <mn>21</mn> </mrow> <mrow> <mi>c</mi> <mi>a</mi> <mi>l</mi> </mrow> </msubsup> </mrow> <mo>|</mo> </mrow> </mrow> </semantics> </math> images from 2-D scanning at (<b>a</b>) 5.2 GHz; (<b>b</b>) 6.9 GHz; (<b>c</b>) 8.8 GHz.</p>
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3266 KiB  
Article
Mapping the Salinity Gradient in a Microfluidic Device with Schlieren Imaging
by Chen-li Sun, Shao-Tuan Chen and Po-Jen Hsiao
Sensors 2015, 15(5), 11587-11600; https://doi.org/10.3390/s150511587 - 20 May 2015
Cited by 4 | Viewed by 6972
Abstract
This work presents the use of the schlieren imaging to quantify the salinity gradients in a microfluidic device. By partially blocking the back focal plane of the objective lens, the schlieren microscope produces an image with patterns that correspond to spatial derivative of [...] Read more.
This work presents the use of the schlieren imaging to quantify the salinity gradients in a microfluidic device. By partially blocking the back focal plane of the objective lens, the schlieren microscope produces an image with patterns that correspond to spatial derivative of refractive index in the specimen. Since salinity variation leads to change in refractive index, the fluid mixing of an aqueous salt solution of a known concentration and water in a T-microchannel is used to establish the relation between salinity gradients and grayscale readouts. This relation is then employed to map the salinity gradients in the target microfluidic device from the grayscale readouts of the corresponding micro-schlieren image. For saline solution with salinity close to that of the seawater, the grayscale readouts vary linearly with the salinity gradient, and the regression line is independent of the flow condition and the salinity of the injected solution. It is shown that the schlieren technique is well suited to quantify the salinity gradients in microfluidic devices, for it provides a spatially resolved, non-invasive, full-field measurement. Full article
(This article belongs to the Special Issue Modeling, Testing and Reliability Issues in MEMS Engineering 2013)
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<p>Experimental setup.</p>
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<p>(<b>a</b>) Distributions of salinity gradient (numerical simulation); (<b>b</b>) Grayscale ratios (micro-schlieren images) in the T-microchannel, <span class="html-italic">Re</span> = 5.</p>
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<p>Variations of grayscale ratio with salinity gradient for (<b>a</b>) ∂<span class="html-italic">S</span>/∂<span class="html-italic">y</span> &gt; 0 and (<b>b</b>) ∂<span class="html-italic">S</span>/∂<span class="html-italic">y</span> &lt; 0.</p>
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<p>Comparison of relative grayscale change to the normalized cumulative distribution for the 5× objective.</p>
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<p>The microfluidic device for studying microbial response to a salinity gradient. The blue and black lines delineate the microchannels for saline solution and water in the top layer, respectively. The red lines outline the cavity-flow microchannel for water in the bottom layer.</p>
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<p>Distribution of salinity gradient in the target microfluidic device under different Reynolds numbers: (<b>a</b>) <span class="html-italic">Re</span> = 5, (<b>b</b>) <span class="html-italic">Re</span> = 50, (<b>c</b>) <span class="html-italic">Re</span> = 100, (<b>d</b>) <span class="html-italic">Re</span> = 300, (<b>e</b>) <span class="html-italic">Re</span> = 500, and (<b>f</b>) <span class="html-italic">Re</span> = 700.</p>
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810 KiB  
Article
A Wavelet-Based Approach to Fall Detection
by Luca Palmerini, Fabio Bagalà, Andrea Zanetti, Jochen Klenk, Clemens Becker and Angelo Cappello
Sensors 2015, 15(5), 11575-11586; https://doi.org/10.3390/s150511575 - 20 May 2015
Cited by 40 | Viewed by 8305
Abstract
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based [...] Read more.
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms. Full article
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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<p>Average (±1 standard deviation) acceleration sum vector (centered on the peak) over the 29 real-world falls.</p>
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<p>Workflow of the procedure that is used to compute the wavelet-based feature.</p>
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<p>ROC curve of three features: the Wavelet-based (in blue), the Upper Peak Value (UPV, in red), and the Lower Peak Value (LPV, in green).</p>
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9048 KiB  
Article
Towards the Automatic Scanning of Indoors with Robots
by Antonio Adán, Blanca Quintana, Andres S. Vázquez, Alberto Olivares, Eduardo Parra and Samuel Prieto
Sensors 2015, 15(5), 11551-11574; https://doi.org/10.3390/s150511551 - 19 May 2015
Cited by 28 | Viewed by 8133
Abstract
This paper is framed in both 3D digitization and 3D data intelligent processing research fields. Our objective is focused on developing a set of techniques for the automatic creation of simple three-dimensional indoor models with mobile robots. The document presents the principal steps [...] Read more.
This paper is framed in both 3D digitization and 3D data intelligent processing research fields. Our objective is focused on developing a set of techniques for the automatic creation of simple three-dimensional indoor models with mobile robots. The document presents the principal steps of the process, the experimental setup and the results achieved. We distinguish between the stages concerning intelligent data acquisition and 3D data processing. This paper is focused on the first stage. We show how the mobile robot, which carries a 3D scanner, is able to, on the one hand, make decisions about the next best scanner position and, on the other hand, navigate autonomously in the scene with the help of the data collected from earlier scans. After this stage, millions of 3D data are converted into a simplified 3D indoor model. The robot imposes a stopping criterion when the whole point cloud covers the essential parts of the scene. This system has been tested under real conditions indoors with promising results. The future is addressed to extend the method in much more complex and larger scenarios. Full article
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
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<p>Overview of the automatic scanning system.</p>
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<p>(<b>a</b>) Flowchart for obtaining interval [<span class="html-italic">z</span><sub>1</sub>, <span class="html-italic">z</span><sub>2</sub>] and points belonging to the ceiling and floor marked in the Z-histogram; (<b>b</b>) original point cloud <span class="html-italic">P</span>(<span class="html-italic">t</span>) (left) and the points belonging to the ceiling (in red) and floor (in green); (<b>c</b>) from left to right: image with ceiling points, extraction of the contour H and the indoor point cloud.</p>
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<p>Obtaining the first robot map. (Top) Point cloud with a set of small bars on the floor and the obtained normal's map. (Bottom) Fusion of <span class="html-italic">M′</span>(1) and <span class="html-italic">F</span>(<span class="html-italic">1</span>) and the resulting robot map.</p>
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<p>(<b>a</b>) Occlusion in the voxel space: Voxel A occludes Voxel B since <span class="html-italic">α &gt; β</span>; (<b>b</b>) labeled voxel space. The ceiling and floor are removed for a better visualization.</p>
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<p>(<b>a</b>) Initial labeled map; (<b>b,c</b>) permitted and non-permitted positions before and after imposing security and path planning requirements; (<b>d</b>) final labeled map.</p>
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<p>Chart containing the labeled map creation and the next best view (NBV) algorithm.</p>
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<p>(<b>a</b>) Labeled map for Cycles 1 and 2 after applying the NBV algorithm; (<b>b</b>) labeled voxel space for Cycles 1 and 2.</p>
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<p>Mobile robot navigation in a corridor. The internal map used by the robot in each cycle is shown.</p>
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<p>Results in the first scan. (<b>a</b>) Initial point cloud; (<b>b</b>) points belonging to the ceiling; (<b>c</b>) the first labeled voxel space <span class="html-italic">V</span>(1); (<b>d</b>) position of the next best view in the labeled map; (<b>e</b>) robot map and the next position; (<b>f</b>) pictures of the mobile robot in Positions 1 and 2.</p>
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478 KiB  
Article
Semi-Supervised Bayesian Classification of Materials with Impact-Echo Signals
by Jorge Igual, Addisson Salazar, Gonzalo Safont and Luis Vergara
Sensors 2015, 15(5), 11528-11550; https://doi.org/10.3390/s150511528 - 19 May 2015
Cited by 27 | Viewed by 5265
Abstract
The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their [...] Read more.
The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects) and kind of defect (hole or crack, passing through or not). Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process. Full article
(This article belongs to the Section Physical Sensors)
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<p>Flowchart of the algorithm.</p>
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<p>Example of the piece under study.</p>
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<p>Setup experiment for a piece with a hole and a crack.</p>
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<p>Dimension reduction of the feature vector after PCA. Box and whiskers plot of the F measure for each of the 12 classes. (<b>Top</b>) 3 × 1 feature vector; (<b>Middle</b>) 7 × 1 feature vector; (<b>Bottom</b>) 16 × 1 feature vector. Above each plot is the overall mean F measure.</p>
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<p>Model selection: number of Gaussians per class for the seven classes problem. (<b>Top</b>) Mean F value of each class for the training dataset <span class="html-italic">vs</span>. the number of Gaussians per class; (<b>Bottom</b>) the same for the testing dataset.</p>
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<p>Influence of data size and supervision. (<b>Top</b>) Four-class problem; (top-left) mean F value for the four classes <span class="html-italic">vs</span>. the number of samples per class for a 10% supervision ratio; (top-right) results with 50% of supervision; (<b>Bottom</b>) 12-class problem; (bottom-left) mean F value for the 12 classes for a 10% supervision ratio; (bottom-right) results with 50% supervision.</p>
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<p>Influence of supervision. F value vs. percentage of supervision. (<b>Top</b>) Four-class problem; (<b>Bottom</b>) 12-class problem.</p>
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<p>Precision and recall for the four-class problem vs. the supervision ratio.</p>
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<p>Precision and recall for the seven-class problem vs. supervision ratio.</p>
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