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Sensors, Volume 16, Issue 1 (January 2016) – 139 articles

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3718 KiB  
Article
A Novel Pedestrian Navigation Algorithm for a Foot-Mounted Inertial-Sensor-Based System
by Mingrong Ren, Kai Pan, Yanhong Liu, Hongyu Guo, Xiaodong Zhang and Pu Wang
Sensors 2016, 16(1), 139; https://doi.org/10.3390/s16010139 - 21 Jan 2016
Cited by 68 | Viewed by 6435
Abstract
This paper proposes a novel zero velocity update (ZUPT) method for a foot-mounted pedestrian navigation system (PNS). First, the error model of the PNS is developed and a Kalman filter is built based on the error model. Second, a novel zero velocity detection [...] Read more.
This paper proposes a novel zero velocity update (ZUPT) method for a foot-mounted pedestrian navigation system (PNS). First, the error model of the PNS is developed and a Kalman filter is built based on the error model. Second, a novel zero velocity detection algorithm based on the variations in speed over a gait cycle is proposed. A finite state machine including three states is employed to model a gait cycle. The state transition conditions are determined based on speed using a sliding window. Third, the ZUPT software flow is illustrated and described. Finally, the performances of the proposed method and other methods are examined and compared experimentally. The experimental results show that the mean relative accuracy of the proposed method is 0.89% under various motion modes. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1
<p>The detected zero velocity interval when a pedestrian is walking. The red line represents the stationary state and moving state. Small values indicate the stationary state. Large values indicate the moving state.</p> Full article ">Figure 2
<p>The detected zero velocity interval when a pedestrian is running. In these time intervals, there are no false detections for the accelerometer (<b>upper</b>). However, only two zero velocity intervals are detected for the gyroscope (<b>lower</b>).</p> Full article ">Figure 3
<p>Norm of velocity for different types of gait.</p> Full article ">Figure 4
<p>Speed trend and human limb kinematic in a typical walking cycle.</p> Full article ">Figure 5
<p>State transition diagram.</p> Full article ">Figure 6
<p>Pedestrian navigation system software flow chart.</p> Full article ">Figure 7
<p>(<b>Left</b>) IMU strapped on the front of a shoe; (<b>Right</b>) IMU strapped on the rear side of a shoe.</p> Full article ">Figure 8
<p>Zero velocity detection using the method proposed in this paper.</p> Full article ">Figure 9
<p>Zero velocity detection using method proposed in [<a href="#B11-sensors-16-00139" class="html-bibr">11</a>]. The vertical unite is dimensionless numbers.</p> Full article ">Figure 10
<p>Zero velocity detection of six types of gait when the IMU is strapped onto the rear side of a shoe. The parameters are the same as those in <a href="#sensors-16-00139-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 11
<p>Comparison of the calculated trajectories between the proposed algorithm and algorithm [<a href="#B11-sensors-16-00139" class="html-bibr">11</a>].</p> Full article ">Figure 12
<p>Uphill and stair descending trajectory. Path used for testing (<b>left</b>). The calculated trajectories of the proposed algorithm (<b>right</b>).</p> Full article ">
2593 KiB  
Article
A Two Fiber Bragg Gratings Sensing System to Monitor the Torque of Rotating Shaft
by Yongjiao Wang, Lei Liang, Yinquan Yuan, Gang Xu and Fang Liu
Sensors 2016, 16(1), 138; https://doi.org/10.3390/s16010138 - 21 Jan 2016
Cited by 15 | Viewed by 5125
Abstract
By fixing two FBGs on the surface of a rotating shaft along the direction of ±45° and using dynamic wavelength demodulation technology, we propose an optical fiber sensing system to monitor the driving torque and torsion angle of the rotating shaft. In theory, [...] Read more.
By fixing two FBGs on the surface of a rotating shaft along the direction of ±45° and using dynamic wavelength demodulation technology, we propose an optical fiber sensing system to monitor the driving torque and torsion angle of the rotating shaft. In theory, the dependence relation of the dynamic difference of central wavelengths on the torque and torsion angle of the rotating shaft has been deduced. To verify an optical fiber sensing system, a series of sensing experiments have been completed and the measured data are approximately consistent with the theoretical analysis. The difference of two central wavelengths can be expressed as the sum of two parts: a “DC” part and a harmonic “AC” part. The driving torque or torsion angle is linear with the “DC” part of the difference of two central wavelengths, the harmonic “AC” part, meaning the torsion angle vibration, illustrates that periodic vibration torque may be caused by inhomogeneous centrifugal forces or inhomogeneous additional torques produced by the driving system and the load. Full article
(This article belongs to the Section Physical Sensors)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Experimental scheme.</p> Full article ">Figure 2
<p>Sketch of the shaft torsion.</p> Full article ">Figure 3
<p>Two FBGs pasted along the maximum strain of the shaft torsion.</p> Full article ">Figure 4
<p>Experimental setup.</p> Full article ">Figure 5
<p>Central wavelengths of two FBGs with time: (<b>a</b>) <span class="html-italic">M</span> = 0; (<b>b</b>) <span class="html-italic">M</span> = 1.5 Nm.</p> Full article ">Figure 6
<p>Central wavelength <span class="html-italic">versus</span> driving torque: (<b>a</b>) Δλ<sub>DC</sub>(<span class="html-italic">M</span><sub>d</sub>); (<b>b</b>) Δλ<sub>DC</sub>(<span class="html-italic">M</span><sub>d</sub>) − Δλ<sub>DC</sub>(0).</p> Full article ">Figure 7
<p>Central wavelengths of two FBGs with the time for four rotation speeds: (<b>a</b>) 420; (<b>b</b>) 700; (<b>c</b>) 960 and (<b>d</b>) 1200 rpm.</p> Full article ">Figure 8
<p>1-order frequency of vibration signal <span class="html-italic">versus</span> rotating speed.</p> Full article ">
2641 KiB  
Review
Graphene: The Missing Piece for Cancer Diagnosis?
by Sandra M. A. Cruz, André F. Girão, Gil Gonçalves and Paula A. A. P. Marques
Sensors 2016, 16(1), 137; https://doi.org/10.3390/s16010137 - 21 Jan 2016
Cited by 36 | Viewed by 10275
Abstract
This paper reviews recent advances in graphene-based biosensors development in order to obtain smaller and more portable devices with better performance for earlier cancer detection. In fact, the potential of Graphene for sensitive detection and chemical/biological free-label applications results from its exceptional physicochemical [...] Read more.
This paper reviews recent advances in graphene-based biosensors development in order to obtain smaller and more portable devices with better performance for earlier cancer detection. In fact, the potential of Graphene for sensitive detection and chemical/biological free-label applications results from its exceptional physicochemical properties such as high electrical and thermal conductivity, aspect-ratio, optical transparency and remarkable mechanical and chemical stability. Herein we start by providing a general overview of the types of graphene and its derivatives, briefly describing the synthesis procedure and main properties. It follows the reference to different routes to engineer the graphene surface for sensing applications with organic biomolecules and nanoparticles for the development of advanced biosensing platforms able to detect/quantify the characteristic cancer biomolecules in biological fluids or overexpressed on cancerous cells surface with elevated sensitivity, selectivity and stability. We then describe the application of graphene in optical imaging methods such as photoluminescence and Raman imaging, electrochemical sensors for enzymatic biosensing, DNA sensing, and immunosensing. The bioquantification of cancer biomarkers and cells is finally discussed, particularly electrochemical methods such as voltammetry and amperometry which are generally adopted transducing techniques for the development of graphene based sensors for biosensing due to their simplicity, high sensitivity and low-cost. To close, we discuss the major challenges that graphene based biosensors must overcome in order to reach the necessary standards for the early detection of cancer biomarkers by providing reliable information about the patient disease stage. Full article
(This article belongs to the Special Issue Graphene and 2D Material Bionanosensors: Chemistry Matters)
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<p>Illustration of graphene derivatives with potential applications on cancer biosensors.</p> Full article ">Figure 2
<p>Fluorescent images of human breast cancer cell T47D after incubation with green GQDs for 4 h (<b>a</b>) phase contrast picture of T47D cells; (<b>b</b>) Individual nucleus stained blue with DAPI; (<b>c</b>) Agglomerated green GQDs surrounding each nucleus; (<b>d</b>) The overlay high contrast image of nucleolus stained with blue DAPI and GQDs (green) staining. Reproduced with permission [<a href="#B61-sensors-16-00137" class="html-bibr">61</a>] Copyright 2012, American Chemical Society.</p> Full article ">Figure 3
<p>(<b>a</b>) <span class="html-italic">In vivo</span> fluorescence images of GQD-HA in mice after tail vein injection; (<b>b</b>) Ex vivo images of liver, kidney, spleen, heart, and tumor after dissection; (<b>c</b>) Normalized intensity from dissected organs .Reprinted with permission from [<a href="#B58-sensors-16-00137" class="html-bibr">58</a>]. Copyright (2013) American Chemical Society.</p> Full article ">Figure 4
<p>The cellular uptake mechanism investigations of GO and Au/GO hybrids. Hela 229 cells are incubated for 2 h with GO (<b>a</b>–<b>d</b>) or Au/GO hybrids (<b>e</b>–<b>h</b>) at 37 °C (<b>a</b>, <b>b</b>, <b>e</b> and <b>f</b>) and 4 °C (<b>c</b>, <b>d</b>, <b>g</b> and <b>h</b>), respectively. The left panels are optical images while the right panels are the corresponding Raman images (scale bar: 5 mm). Reproduced with permission [<a href="#B80-sensors-16-00137" class="html-bibr">80</a>]. Copyright 2012, Royal Society of Chemistry.</p> Full article ">Figure 5
<p>Schematic Illustration of the Multi-enzyme Labeling Amplification Strategy Using HRP-p53392Ab2-GO Conjugate. Reproduced with permission [<a href="#B110-sensors-16-00137" class="html-bibr">110</a>]. Copyright 2011, American Chemical Society.</p> Full article ">Figure 6
<p>SEM images of MCF-7 cells cultured (12 h) on (<b>a</b>) ITO/(AP)<sub>10</sub> and (<b>b</b>) ITO/(graphene-AP)<sub>10</sub>. The scale bar is 5 μm for (<b>a</b>) and (<b>b</b>). (<b>c</b>) Proliferation curves of cells cultured on fi lms with different compositions: 1. ITO/(AP)<sub>10</sub>, 2. ITO/(graphene)<sub>10</sub>, 3. ITO/(graphene–AP)<sub>10</sub>, 4. ITO/(graphene–AP)<sub>10</sub>-laminin (only one layer of laminin on top of the structure), and 5. ITO/(graphene–AP–laminin)<sub>10</sub>. Reproduced with permission [<a href="#B120-sensors-16-00137" class="html-bibr">120</a>]. Copyright 2010, WILEY-VCH Verlag GmbH &amp; Co.</p> Full article ">Figure 7
<p>Real-time <span class="html-italic">in situ</span> detection of breast cancer cell surface integrin expression by the RGD–pyrene–GO probe: (<b>a</b>) probe fluorescence recovery by live MDA-MB-435 cancer cells which overexpress integrin avb3 on the cell surface. The recovered fluorescence is mainly detected on the cell membrane as indicated by white arrows; (<b>b</b>) probe fluorescence recovery by MDA-MB-435 cancer cells followed by 4% formalin fixing; (<b>c</b>) equivalent concentration of free RGD–pyrene incubated with liveMDA-MB-435 demonstrates significant endocytosis as indicated by white arrows; (<b>d</b>) equivalent concentration of RGD–pyrene incubated with MDA-MB-435 followed by 4% formalin fixing. Reproduced with permission [<a href="#B129-sensors-16-00137" class="html-bibr">129</a>]. Copyright 2012, Royal Society of Chemistry.</p> Full article ">Figure 8
<p>(<b>a</b>) Schematic setup and (<b>b</b>) the photograph of the GO-based FRET aptasensing microfluidic chip. Scale bar: 10 mm. (<b>c</b>) The principle of a ‘signal-on’ aptasensor for detecting CCRF-CEM cells by assaying the cell-induced fluorescence recovery of GO/FAM-Sgc8. Reproduced with permission [<a href="#B130-sensors-16-00137" class="html-bibr">130</a>] Copyright 2012, Royal Society of Chemistry.</p> Full article ">
925 KiB  
Editorial
Acknowledgement to Reviewers of Sensors in 2015
by Sensors Editorial Office
Sensors 2016, 16(1), 136; https://doi.org/10.3390/s16010136 - 21 Jan 2016
Viewed by 12436
Abstract
The editors of Sensors would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2015. [...] Full article
903 KiB  
Article
A Novel Photoelectrochemical Biosensor for Tyrosinase and Thrombin Detection
by Jiexia Chen, Yifan Liu and Guang-Chao Zhao
Sensors 2016, 16(1), 135; https://doi.org/10.3390/s16010135 - 21 Jan 2016
Cited by 26 | Viewed by 6957
Abstract
A novel photoelectrochemical biosensor for step-by-step assay of tyrosinase and thrombin was fabricated based on the specific interactions between the designed peptide and the target enzymes. A peptide chain with a special sequence which contains a positively charged lysine-labeled terminal, tyrosine at the [...] Read more.
A novel photoelectrochemical biosensor for step-by-step assay of tyrosinase and thrombin was fabricated based on the specific interactions between the designed peptide and the target enzymes. A peptide chain with a special sequence which contains a positively charged lysine-labeled terminal, tyrosine at the other end and a cleavage site recognized by thrombin between them was designed. The designed peptide can be fixed on surface of the CdTe quantum dots (QDs)-modified indium-tin oxide (ITO) electrode through electrostatic attraction to construct the photoelectrochemical biosensor. The tyrosinase target can catalyze the oxidization of tyrosine by oxygen into ortho-benzoquinone residues, which results in a decrease in the sensor photocurrent. Subsequently, the cleavage site could be recognized and cut off by another thrombin target, restoring the sensor photocurrent. The decrease or increase of photocurrent in the sensor enables us to assay tyrosinase and thrombin. Thus, the detection of tyrosinase and thrombin can be achieved in the linear range from 2.6 to 32 μg/mL and from 4.5 to 100 μg/mL with detection limits of 1.5 μg/mL and 1.9 μg/mL, respectively. Most importantly, this strategy shall allow us to detect different classes of enzymes simultaneously by designing various enzyme-specific peptide substrates. Full article
(This article belongs to the Special Issue Microbial and Enzymatic Biosensors)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>A</b>) Photocurrent response for (a) ITO/QDs, (b) ITO/QDs/peptide, (c) ITO/QDs/ peptide/tyrosinase (32 μg/mL), (d) ITO/QDs/peptide/tyrosinase (32 μg/mL)/thrombin (25 μg/mL ), (e) ITO/QDs/peptide/tyrosinase (32 μg/mL)/thrombin (50 μg/mL) in 0.1 M PBS (pH 7.4) containing 0.1 M AA at –0.5 V to a light excitation at 400 nm; (<b>B</b>) EIS of the modified electrodes in 0.1 M KCl containing 5 mM [Fe(CN)<sub>6</sub>]<sup>3−</sup>/[Fe(CN)<sub>6</sub>]<sup>4−</sup> (1:1): (a) ITO, (b) ITO/QDs, (c) ITO/QDs/peptide, (d) ITO/QDs/peptide/tyrosinase (32 μg/mL), (e) ITO/QDs/peptide/tyrosinase (32 μg/mL)/thrombin (50 μg/mL). EIS were recorded between 0.01 Hz to 100 kHz with applied voltage of 5 mV.</p> Full article ">Figure 2
<p>Electron transfer mechanism after incubating in (<b>A</b>) tyrosinase and (<b>B</b>) thrombin.</p> Full article ">Figure 3
<p>Effects of incubation temperature and time on photocurrent signals in presence of (<b>A</b>,<b>B</b>) 12.5 μg/mL tyrosinase; (<b>C</b>,<b>D</b>) 50 μg/mL thrombin. The PEC tests are measured in 0.1 M PBS (pH 7.4) containing 0.1 M AA at −0.5 V to a light excitation at 400 nm.</p> Full article ">Figure 4
<p>(<b>A</b>) Photocurrent response of the proposed sensor at 0, 2.5, 5, 7.5, 12.5, 18.8, 32 μg/mL tyrosinase (from a to g). Inset: calibration curve; (<b>B</b>) Photocurrent response of the proposed sensor at 4.5, 10, 25, 50, 100 μg/mL thrombin (from a to e). Inset: calibration curve. All the photocurrent responses were measured in 0.1 M PBS (pH 7.4) containing 0.1 M AA at −0.5 V to a light excitation at 400 nm.</p> Full article ">Figure 5
<p>Selectivity of the proposed sensor to (<b>A</b>) tyrosinase and (<b>B</b>) thrombin by comparing it to the interfering proteins at the 60 μg/mL level and 120 μg/mL level: BSA, γ-globulin and lysozyme, respectively.</p> Full article ">Figure 6
<p>Schematic illustration of the stepwise construction and detection process for the determination of the enzymes.</p> Full article ">
948 KiB  
Article
A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington’s Disease Patients
by Andrea Mannini, Diana Trojaniello, Andrea Cereatti and Angelo M. Sabatini
Sensors 2016, 16(1), 134; https://doi.org/10.3390/s16010134 - 21 Jan 2016
Cited by 176 | Viewed by 14122
Abstract
Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, [...] Read more.
Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, the presented methodology was tested on gait data recorded on two pathological populations (Huntington’s disease and post-stroke subjects) and healthy elderly controls using data from inertial measurement units placed at shank and waist. By extracting features from group-specific Hidden Markov Models (HMMs) and signal information in time and frequency domain, a Support Vector Machines classifier (SVM) was designed and validated. The 90.5% of subjects was assigned to the right group after leave-one-subject–out cross validation and majority voting. The long-term goal we point to is the gait assessment in everyday life to early detect gait alterations. Full article
(This article belongs to the Section Physical Sensors)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>General block scheme of the algorithm for gait classification.</p> Full article ">Figure 2
<p>Feature set definition for classification. The information from the subject being tested was not included in the training set.</p> Full article ">Figure 3
<p>Block scheme of the SVM classifier validation. The LOSO approach was followed: data from <span class="html-italic">N</span>-1 subjects were used for training and the obtained classifier was tested on the features from the remaining subject.</p> Full article ">Figure 4
<p>Output of the classifier after MV in relation to clinical scales: (<b>a</b>) PS subjects (impaired side only), in relation to the FAC scale. No data were misclassified from the PS class to the EL class. Two subjects are misclassified from the PS class to the HD class; (<b>b</b>) HD subjects in relation to the HDRS’ scale. No data were misclassified from the HD class to the EL class. Two subjects were misclassified from the HD class to the PS class.</p> Full article ">
2725 KiB  
Article
Analysis of a Segmented Annular Coplanar Capacitive Tilt Sensor with Increased Sensitivity
by Jiahao Guo, Pengcheng Hu and Jiubin Tan
Sensors 2016, 16(1), 133; https://doi.org/10.3390/s16010133 - 21 Jan 2016
Cited by 15 | Viewed by 6984
Abstract
An investigation of a segmented annular coplanar capacitor is presented. We focus on its theoretical model, and a mathematical expression of the capacitance value is derived by solving a Laplace equation with Hankel transform. The finite element method is employed to verify the [...] Read more.
An investigation of a segmented annular coplanar capacitor is presented. We focus on its theoretical model, and a mathematical expression of the capacitance value is derived by solving a Laplace equation with Hankel transform. The finite element method is employed to verify the analytical result. Different control parameters are discussed, and each contribution to the capacitance value of the capacitor is obtained. On this basis, we analyze and optimize the structure parameters of a segmented coplanar capacitive tilt sensor, and three models with different positions of the electrode gap are fabricated and tested. The experimental result shows that the model (whose electrode-gap position is 10 mm from the electrode center) realizes a high sensitivity: 0.129 pF/° with a non-linearity of <0.4% FS (full scale of ±40°). This finding offers plenty of opportunities for various measurement requirements in addition to achieving an optimized structure in practical design. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1

Figure 1
<p>Schematic views of capacitive tilt sensors using parallel electrodes: (<b>a</b>) front view and (<b>b</b>) side view; schematic views of capacitive tilt sensors using annular coplanar electrodes: (<b>c</b>) front view and (<b>d</b>) side view.</p> Full article ">Figure 2
<p>Schematic views of a segmented concentric annular coplanar capacitor: (<b>a</b>) top view; and (<b>b</b>) section view.</p> Full article ">Figure 3
<p>Capacitance values of the capacitors with (<b>a</b>) different medium permittivity <span class="html-italic">ε</span> and (<b>b</b>) different medium thickness <span class="html-italic">h</span>.</p> Full article ">Figure 4
<p>Capacitance values of the capacitors with different central angle <span class="html-italic">θ</span><sub>0</sub>.</p> Full article ">Figure 5
<p>Capacitance values of the capacitors with different parameters in the radial direction: (<b>a</b>) <span class="html-italic">r</span><sub>io</sub> = 9 mm, <span class="html-italic">r</span><sub>oi</sub> = 9.5 mm, and <span class="html-italic">r</span><sub>oo</sub> = 11.5 mm; (<b>b</b>) <span class="html-italic">r</span><sub>ii</sub> = 7 mm, <span class="html-italic">r</span><sub>oi</sub> =9.5 mm, and <span class="html-italic">r</span><sub>oo</sub> = 11.5 mm; (<b>c</b>) <span class="html-italic">r</span><sub>ii</sub> = 7 mm, <span class="html-italic">r</span><sub>io</sub> = 9 mm, and <span class="html-italic">r</span><sub>oo</sub> = 11.5 mm; and (<b>d</b>) <span class="html-italic">r</span><sub>ii</sub> = 7 mm, <span class="html-italic">r</span><sub>io</sub> = 9 mm, and <span class="html-italic">r</span><sub>oi</sub> = 9.5 mm.</p> Full article ">Figure 6
<p>Schematic view of a tilt sensor with four segmented concentric annular coplanar capacitors.</p> Full article ">Figure 7
<p>Capacitance values with different gap positions (<span class="html-italic">r</span><sub>ii</sub> = 7 mm, <span class="html-italic">r</span><sub>oo</sub> = 11.5 mm, <span class="html-italic">θ</span><sub>0</sub> = 2 rad, <span class="html-italic">h</span> = 15 mm, and <span class="html-italic">ε</span> = 41.5 × ε<sub>0</sub>).</p> Full article ">Figure 8
<p>(<b>a</b>) three models with different structure parameters; (<b>b</b>) percentage change of <span class="html-italic">C</span><sub>right</sub> and <span class="html-italic">C</span><sub>left</sub> respect to the inclination change in three models; (<b>c</b>) corresponding sensitivity comparison results.</p> Full article ">Figure 9
<p>Accuracy test of the proposed tilt sensor with Model 2.</p> Full article ">
2301 KiB  
Article
Vision-Based Georeferencing of GPR in Urban Areas
by Riccardo Barzaghi, Noemi Emanuela Cazzaniga, Diana Pagliari and Livio Pinto
Sensors 2016, 16(1), 132; https://doi.org/10.3390/s16010132 - 21 Jan 2016
Cited by 13 | Viewed by 5466
Abstract
Ground Penetrating Radar (GPR) surveying is widely used to gather accurate knowledge about the geometry and position of underground utilities. The sensor arrays need to be coupled to an accurate positioning system, like a geodetic-grade Global Navigation Satellite System (GNSS) device. However, in [...] Read more.
Ground Penetrating Radar (GPR) surveying is widely used to gather accurate knowledge about the geometry and position of underground utilities. The sensor arrays need to be coupled to an accurate positioning system, like a geodetic-grade Global Navigation Satellite System (GNSS) device. However, in urban areas this approach is not always feasible because GNSS accuracy can be substantially degraded due to the presence of buildings, trees, tunnels, etc. In this work, a photogrammetric (vision-based) method for GPR georeferencing is presented. The method can be summarized in three main steps: tie point extraction from the images acquired during the survey, computation of approximate camera extrinsic parameters and finally a refinement of the parameter estimation using a rigorous implementation of the collinearity equations. A test under operational conditions is described, where accuracy of a few centimeters has been achieved. The results demonstrate that the solution was robust enough for recovering vehicle trajectories even in critical situations, such as poorly textured framed surfaces, short baselines, and low intersection angles. Full article
(This article belongs to the Section Remote Sensors)
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<p>Methodological workflow describing tie point extraction.</p> Full article ">Figure 2
<p>Methodological workflow of the first approximate bundle block adjustment.</p> Full article ">Figure 3
<p>Methodological workflow for the rigorous bundle block adjustment.</p> Full article ">Figure 4
<p>The system during the survey: the (orange) GPR is located at the bottom of the wood carrier, the GPS antenna is on the top of the stake, while the camera is fixed below it. The box in the lower part contains the other components of the system: the batteries and the hardware for controlling the camera. The laptop is near it. In the background it is possible to see the framed building. The façade is made of regular bricks, but some graffiti helps diversifying a bit the texture. This is not true for the gate. Note the different lighting conditions between the wall and the gate.</p> Full article ">Figure 5
<p>Example of image used for the geometric calibration. The (white) targets are visible on the wall and on the rise of the sidewalk.</p> Full article ">Figure 6
<p>Planimetric and altimetric differences between the photogrammetric and the GPS solutions for the different scenarios. In the upper part the (true) plan of the buildings, the gate (orange), the roofing (green) and the sidewalk (blue) are visible. GCPs are represented by crosses.</p> Full article ">
2839 KiB  
Article
Intravehicular, Short- and Long-Range Communication Information Fusion for Providing Safe Speed Warnings
by Felipe Jiménez, Jose Eugenio Naranjo, Francisco Serradilla, Elisa Pérez, María Jose Hernández, Trinidad Ruiz, José Javier Anaya and Alberto Díaz
Sensors 2016, 16(1), 131; https://doi.org/10.3390/s16010131 - 21 Jan 2016
Cited by 7 | Viewed by 5999
Abstract
Inappropriate speed is a relevant concurrent factor in many traffic accidents. Moreover, in recent years, traffic accidents numbers in Spain have fallen sharply, but this reduction has not been so significant on single carriageway roads. These infrastructures have less equipment than high-capacity roads, [...] Read more.
Inappropriate speed is a relevant concurrent factor in many traffic accidents. Moreover, in recent years, traffic accidents numbers in Spain have fallen sharply, but this reduction has not been so significant on single carriageway roads. These infrastructures have less equipment than high-capacity roads, therefore measures to reduce accidents on them should be implemented in vehicles. This article describes the development and analysis of the impact on the driver of a warning system for the safe speed on each road section in terms of geometry, the presence of traffic jams, weather conditions, type of vehicle and actual driving conditions. This system is based on an application for smartphones and includes knowledge of the vehicle position via Ground Positioning System (GPS), access to intravehicular information from onboard sensors through the Controller Area Network (CAN) bus, vehicle data entry by the driver, access to roadside information (short-range communications) and access to a centralized server with information about the road in the current and following sections of the route (long-range communications). Using this information, the system calculates the safe speed, recommends the appropriate speed in advance in the following sections and provides warnings to the driver. Finally, data are sent from vehicles to a server to generate new information to disseminate to other users or to supervise drivers’ behaviour. Tests in a driving simulator have been used to define the system warnings and Human Machine Interface (HMI) and final tests have been performed on real roads in order to analyze the effect of the system on driver behavior. Full article
(This article belongs to the Special Issue Sensors in New Road Vehicles)
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Figure 1
<p>System layout and information sources.</p> Full article ">Figure 2
<p>(<b>a</b>) Three-dimensional laser scanner; (<b>b</b>) Road section example; (<b>c</b>) Recorded data of the road surroundings.</p> Full article ">Figure 3
<p>Vehicle information retrieving. (<b>a</b>) OBD-II information retrieving; (<b>b</b>) CAN-BUS information retrieving.</p> Full article ">Figure 4
<p>Communication with AVESE server.</p> Full article ">Figure 5
<p>(<b>a</b>) Different interface designs; (<b>b</b>) Implementation of the interface in a smartphone.</p> Full article ">Figure 6
<p>Laboratory tests. (<b>a</b>) ASL Model 504 Ocular system log; (<b>b</b>) Control computers for running simulator and eye-tracking system; (<b>c</b>) Driver performing a simulation.</p> Full article ">Figure 7
<p>Average value in (<b>a</b>) Satisfaction and (<b>b</b>) Utility.</p> Full article ">Figure 8
<p>Number of times the user looks at the interface.</p> Full article ">Figure 9
<p>Implementation of the system in 2 vehicle types. (<b>a</b>) Passenger car; (<b>b</b>) Van; (<b>c</b>) System implementation.</p> Full article ">
1988 KiB  
Article
Interferometric Laser Scanner for Direction Determination
by Gennady Kaloshin and Igor Lukin
Sensors 2016, 16(1), 130; https://doi.org/10.3390/s16010130 - 21 Jan 2016
Cited by 3 | Viewed by 4279
Abstract
In this paper, we explore the potential capabilities of new laser scanning-based method for direction determination. The method for fully coherent beams is extended to the case when interference pattern is produced in the turbulent atmosphere by two partially coherent sources. The performed [...] Read more.
In this paper, we explore the potential capabilities of new laser scanning-based method for direction determination. The method for fully coherent beams is extended to the case when interference pattern is produced in the turbulent atmosphere by two partially coherent sources. The performed theoretical analysis identified the conditions under which stable pattern may form on extended paths of 0.5–10 km in length. We describe a method for selecting laser scanner parameters, ensuring the necessary operability range in the atmosphere for any possible turbulence characteristics. The method is based on analysis of the mean intensity of interference pattern, formed by two partially coherent sources of optical radiation. Visibility of interference pattern is estimated as a function of propagation pathlength, structure parameter of atmospheric turbulence, and spacing of radiation sources, producing the interference pattern. It is shown that, when atmospheric turbulences are moderately strong, the contrast of interference pattern of laser scanner may ensure its applicability at ranges up to 10 km. Full article
(This article belongs to the Section Remote Sensors)
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Full article ">Figure 1
<p>The optical scheme of interferometric laser scanner for direction specification.</p> Full article ">Figure 2
<p>The nomogram for choosing the initial laser beam radii <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 3
<p>The nomogram for choosing the spatial coherence radius of the initial field of laser beams <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">ρ</mi> <mi>k</mi> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 4
<p>The nomogram for choosing the spacing between optical axes of laser beams <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">ρ</mi> <mrow> <mi>t</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics> </math>. The grey-colored region shows the variability range of the function <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> , and the region in light grey depicts the variability range of the function <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> .</p> Full article ">Figure 5
<p>The average intensity of interferometric laser scanning (ILS) interference pattern for different values of <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">ρ</mi> <mrow> <mi>t</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics> </math>: (<b>a</b>) 1 cm; (<b>b</b>) 2 cm; (<b>c</b>) 3 cm; (<b>d</b>) 4 cm; and (<b>e</b>) 5 cm.</p> Full article ">Figure 5 Cont.
<p>The average intensity of interferometric laser scanning (ILS) interference pattern for different values of <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">ρ</mi> <mrow> <mi>t</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics> </math>: (<b>a</b>) 1 cm; (<b>b</b>) 2 cm; (<b>c</b>) 3 cm; (<b>d</b>) 4 cm; and (<b>e</b>) 5 cm.</p> Full article ">Figure 6
<p>The contrast of ILS interference pattern for different distances between the centers of emitting beam apertures <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">ρ</mi> <mrow> <mi>t</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics> </math>: (<b>a</b>) 1 cm; (<b>b</b>) 2 cm; (<b>c</b>) 3 cm; (<b>d</b>) 4 cm; and (<b>e</b>) 5 cm.</p> Full article ">
1552 KiB  
Article
Parallel Imaging of 3D Surface Profile with Space-Division Multiplexing
by Hyung Seok Lee, Soon-Woo Cho, Gyeong Hun Kim, Myung Yung Jeong, Young Jae Won and Chang-Seok Kim
Sensors 2016, 16(1), 129; https://doi.org/10.3390/s16010129 - 21 Jan 2016
Cited by 3 | Viewed by 4840
Abstract
We have developed a modified optical frequency domain imaging (OFDI) system that performs parallel imaging of three-dimensional (3D) surface profiles by using the space division multiplexing (SDM) method with dual-area swept sourced beams. We have also demonstrated that 3D surface information for two [...] Read more.
We have developed a modified optical frequency domain imaging (OFDI) system that performs parallel imaging of three-dimensional (3D) surface profiles by using the space division multiplexing (SDM) method with dual-area swept sourced beams. We have also demonstrated that 3D surface information for two different areas could be well obtained in a same time with only one camera by our method. In this study, double field of views (FOVs) of 11.16 mm × 5.92 mm were achieved within 0.5 s. Height range for each FOV was 460 µm and axial and transverse resolutions were 3.6 and 5.52 µm, respectively. Full article
(This article belongs to the Section Physical Sensors)
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<p>SDM-OFDI system with dual-point swept source beams.</p> Full article ">Figure 2
<p>(<b>a</b>) Distance between sample1, sample2 and reference mirror; (<b>b</b>) Parallel measurement of two 3D surface profiles.</p> Full article ">Figure 3
<p>(<b>a</b>) Depth-encoded signal with only sample1; (<b>b</b>) Depth-encoded signal with only sample 2; (<b>c</b>) Depth-encoded signal with both sample1 and sample 2.</p> Full article ">Figure 4
<p>SDM-OFDI system with dual-area swept source beams.</p> Full article ">Figure 5
<p>(<b>a</b>) Two different 3D surface profiles of a coin measured by SDM-OFDI system with dual-area swept source beams; (<b>b</b>) Cross-sectional profile along the dash line of each 3D surface profile.</p> Full article ">
542 KiB  
Article
Use of Piezoelectric Immunosensors for Detection of Interferon-Gamma Interaction with Specific Antibodies in the Presence of Released-Active Forms of Antibodies to Interferon-Gamma
by Elena Don, Olga Farafonova, Suzanna Pokhil, Darya Barykina, Marina Nikiforova, Darya Shulga, Alena Borshcheva, Sergey Tarasov, Tatyana Ermolaeva and Oleg Epstein
Sensors 2016, 16(1), 96; https://doi.org/10.3390/s16010096 - 20 Jan 2016
Cited by 21 | Viewed by 6095
Abstract
In preliminary ELISA studies where released-active forms (RAF) of antibodies (Abs) to interferon-gamma (IFNg) were added to the antigen-antibody system, a statistically significant difference in absorbance signals obtained in their presence in comparison to placebo was observed. A piezoelectric immunosensor assay was developed [...] Read more.
In preliminary ELISA studies where released-active forms (RAF) of antibodies (Abs) to interferon-gamma (IFNg) were added to the antigen-antibody system, a statistically significant difference in absorbance signals obtained in their presence in comparison to placebo was observed. A piezoelectric immunosensor assay was developed to support these data and investigate the effects of RAF Abs to IFNg on the specific interaction between Abs to IFNg and IFNg. The experimental conditions were designed and optimal electrode coating, detection circumstances and suitable chaotropic agents for electrode regeneration were selected. The developed technique was found to provide high repeatability, intermediate precision and specificity. The difference between the analytical signals of RAF Ab samples and those of the placebo was up to 50.8%, whereas the difference between non-specific controls and the placebo was within 5%–6%. Thus, the piezoelectric immunosensor as well as ELISA has the potential to be used for detecting the effects of RAF Abs to IFNg on the antigen-antibody interaction, which might be the result of RAF’s ability to modify the affinity of IFNg to specific/related Abs. Full article
(This article belongs to the Section Biosensors)
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Full article ">Figure 1
<p>Intermediate precision: analytical signals of test samples and differences of RAF Abs to IFNg (<b>green</b>) <span class="html-italic">vs.</span> control (<b>grey</b>) as measured by three operators over a 10-day period (<span class="html-italic">n</span> = 150. <span class="html-italic">p</span> = 0.95). The full data is presented in the <a href="#app1-sensors-16-00096" class="html-app">Supplementary Table S4</a>. * means Pr &gt; F is &lt;0.0001.</p> Full article ">Figure 2
<p>Specificity: analytical signals of test samples (<b>green</b>), controls (<b>grey</b>) and non-specific controls (<b>red</b>).</p> Full article ">
1873 KiB  
Article
Construction of an Electrochemical Sensor Based on Carbon Nanotubes/Gold Nanoparticles for Trace Determination of Amoxicillin in Bovine Milk
by Aliyu Muhammad, Nor Azah Yusof, Reza Hajian and Jaafar Abdullah
Sensors 2016, 16(1), 56; https://doi.org/10.3390/s16010056 - 20 Jan 2016
Cited by 58 | Viewed by 8657
Abstract
In this work, a novel electrochemical sensor was fabricated for determination of amoxicillin in bovine milk samples by decoration of carboxylated multi-walled carbon nanotubes (MWCNTs) with gold nanoparticles (AuNPs) using ethylenediamine (en) as a cross linker (AuNPs/en-MWCNTs). The constructed nanocomposite was homogenized in [...] Read more.
In this work, a novel electrochemical sensor was fabricated for determination of amoxicillin in bovine milk samples by decoration of carboxylated multi-walled carbon nanotubes (MWCNTs) with gold nanoparticles (AuNPs) using ethylenediamine (en) as a cross linker (AuNPs/en-MWCNTs). The constructed nanocomposite was homogenized in dimethylformamide and drop casted on screen printed electrode. Field emission scanning electron microscopy (FESEM), energy dispersive X-Ray (EDX), X-Ray diffraction (XRD) and cyclic voltammetry were used to characterize the synthesized nanocomposites. The results show that the synthesized nanocomposites induced a remarkable synergetic effect for the oxidation of amoxicillin. Effect of some parameters, including pH, buffer, scan rate, accumulation potential, accumulation time and amount of casted nanocomposites, on the sensitivity of fabricated sensor were optimized. Under the optimum conditions, there was two linear calibration ranges from 0.2–10 µM and 10–30 µM with equations of Ipa (µA) = 2.88C (µM) + 1.2017; r = 0.9939 and Ipa (µA) = 0.88C (µM) + 22.97; r = 0.9973, respectively. The limit of detection (LOD) and limit of quantitation (LOQ) were calculated as 0.015 µM and 0.149 µM, respectively. The fabricated electrochemical sensor was successfully applied for determination of Amoxicillin in bovine milk samples and all results compared with high performance liquid chromatography (HPLC) standard method. Full article
(This article belongs to the Section Chemical Sensors)
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Full article ">Figure 1
<p>Chemical structure of amoxicillin.</p> Full article ">Figure 2
<p>FESEM Images of (<b>a</b>) MWCNTs film; and (<b>b</b>) AuNPs/en-MWCNTs films.</p> Full article ">Figure 3
<p>EDX Spectra of AuNPs/en-MWCNTs nanocomposites. Inset: XRD Spectra of the synthesized nanocomposites.</p> Full article ">Figure 4
<p>Cyclic voltammograms of 10 µM Amoxicillin on bare SPE and after modification with MWCNTs and AuNPs/en-MWCNTs in phosphate buffer (pH 4.0).</p> Full article ">Figure 5
<p>Effect of accumulation potential on the oxidation peak current of Amoxicillin on the surface of AuNPs/en-MWCNTs/SPE. Amoxicillin, 10 µM; Phosphate buffer, pH 7.0; Accumulation time, 60 s.</p> Full article ">Figure 6
<p>Effect of accumulation time on the oxidation peak current of Amoxicillin on the surface of AuNPs/en-MWCNTs/SPE. Amoxicillin, 10 µM; Phosphate buffer, pH 7.0; Accumulation potential, −0.4 V.</p> Full article ">Figure 7
<p>Adsorptive stripping response of different concentration of Amoxicillin at the modified electrode. Inset: Calibration curve of the peak current <span class="html-italic">versus</span> concentration of Amoxicillin in the presence of 0.1 M Phosphate buffer (0.1 M, pH 7.0); accumulation time, 180 s; accumulation potential, −0.4 V; and scan rate, 0.1 V·s<sup>−1</sup>.</p> Full article ">Figure 8
<p>Bulk scale synthetic illustration of AuNPs/en-MWCNTs nanocomposite using ethylenediamine as a cross linker.</p> Full article ">
2703 KiB  
Article
A Microwave Ring-Resonator Sensor for Non-Invasive Assessment of Meat Aging
by Muhammad Taha Jilani, Wong Peng Wen, Lee Yen Cheong and Muhammad Zaka Ur Rehman
Sensors 2016, 16(1), 52; https://doi.org/10.3390/s16010052 - 20 Jan 2016
Cited by 35 | Viewed by 8905
Abstract
The assessment of moisture loss from meat during the aging period is a critical issue for the meat industry. In this article, a non-invasive microwave ring-resonator sensor is presented to evaluate the moisture content, or more precisely water holding capacity (WHC) of broiler [...] Read more.
The assessment of moisture loss from meat during the aging period is a critical issue for the meat industry. In this article, a non-invasive microwave ring-resonator sensor is presented to evaluate the moisture content, or more precisely water holding capacity (WHC) of broiler meat over a four-week period. The developed sensor has shown significant changes in its resonance frequency and return loss due to reduction in WHC in the studied duration. The obtained results are also confirmed by physical measurements. Further, these results are evaluated using the Fricke model, which provides a good fit for electric circuit components in biological tissue. Significant changes were observed in membrane integrity, where the corresponding capacitance decreases 30% in the early aging (0D-7D) period. Similarly, the losses associated with intracellular and extracellular fluids exhibit changed up to 42% and 53%, respectively. Ultimately, empirical polynomial models are developed to predict the electrical component values for a better understanding of aging effects. The measured and calculated values are found to be in good agreement. Full article
(This article belongs to the Section Physical Sensors)
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<p>Fluids within a muscle, intracellular (ICF) and extracellular (ECF) compartment fluids. (<b>a</b>) Fresh muscle with intact cell membrane; (<b>b</b>) muscle with permeable cell membrane.</p> Full article ">Figure 2
<p>Highly sensitive microwave ring-resonator sensor. (<b>a</b>) Illustration; (<b>b</b>) Fabricated prototype.</p> Full article ">Figure 3
<p>Simulated and measured resonance frequency response of a microwave ring-resonator sensor (without sample).</p> Full article ">Figure 4
<p>Equivalent RLC circuit of (<b>a</b>) Capacitively coupled ring resonator; (<b>b</b>) Fricke model representing the biological tissue.</p> Full article ">Figure 5
<p>Studied broiler breast fillets. (<b>a</b>) Samples stored in freezer; (<b>b</b>) Equilibrated sample with collected drip.</p> Full article ">Figure 6
<p>Experimental design for physical and electrical measurements.</p> Full article ">Figure 7
<p>Resonance frequency observed for the meat stored up to four weeks (D = days).</p> Full article ">Figure 8
<p>Weight loss of samples during storage period with respect to their fresh (0D) weight.</p> Full article ">Figure 9
<p>Capacitance (C<sub>1</sub>) of cell membrane as a function of aging period.</p> Full article ">Figure 10
<p>Resistance (R<sub>1</sub>) of an intracellular fluid (ICF) as a function of aging period.</p> Full article ">Figure 11
<p>Resistance (R<sub>2</sub>) of an extracellular fluid (ECF) as a function of aging period.</p> Full article ">
13314 KiB  
Article
Vision-Based People Detection System for Heavy Machine Applications
by Vincent Fremont, Manh Tuan Bui, Djamal Boukerroui and Pierrick Letort
Sensors 2016, 16(1), 128; https://doi.org/10.3390/s16010128 - 20 Jan 2016
Cited by 16 | Viewed by 7783
Abstract
This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they [...] Read more.
This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance. Full article
(This article belongs to the Section Physical Sensors)
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Full article ">Figure 1
<p>Common types of heavy machines. (<b>a</b>) Loader; (<b>b</b>) excavator; (<b>c</b>) truck.</p> Full article ">Figure 2
<p>The telescopic forklift Bobcat TL470: Real images of the acquisition system setup in two different configurations.</p> Full article ">Figure 3
<p>Sensor configuration map. Sensor heights are <math display="inline"> <mrow> <mi>h</mi> <mo>=</mo> <mn>110</mn> <mi>cm</mi> </mrow> </math> and <math display="inline"> <mrow> <mi>H</mi> <mo>=</mo> <mn>210</mn> <mi>cm</mi> </mrow> </math>, respectively.</p> Full article ">Figure 4
<p>(<b>a</b>) <math display="inline"> <mrow> <mi>M</mi> <mi>S</mi> <msub> <mi>E</mi> <msub> <mi mathvariant="bold">p</mi> <mi mathvariant="script">R</mi> </msub> </msub> </mrow> </math> <span class="html-italic">versus</span> the relative position of a person to the camera; (<b>b</b>) regions on the fisheye camera’s FOV.</p> Full article ">Figure 5
<p>Artificial distorted images and their corresponding positions on the fisheye camera’s FOV. The sample image are simulated at <math display="inline"> <mrow> <mrow> <mo>-</mo> <mn>45</mn> <mo>°</mo> </mrow> <mo>,</mo> <mspace width="0.166667em"/> <mn>0</mn> <mo>°</mo> </mrow> </math> and 45° at a distance of <math display="inline"> <mrow> <mn>1</mn> <mo>.</mo> <mn>2</mn> <mi mathvariant="normal">m</mi> </mrow> </math>.</p> Full article ">Figure 6
<p>Examples of pixel values in the padding zone for one line of the image. (<b>a</b>) Duplicate; (<b>b</b>) mirror; (<b>c</b>) mirror-inverted.</p> Full article ">Figure 7
<p>Visualization of HOG features from different border extension methods before (<b>left</b> part) and after (<b>right</b> part) the distortion process: (<b>a</b>) original image; (<b>b</b>) mirror extension; (<b>c</b>) mirror-inverted extension; (<b>d</b>) duplicate extension.</p> Full article ">Figure 8
<p>ROC curves of the mix-training-dataset approach with 50% distorted training dataset, using different padding functions.</p> Full article ">Figure 9
<p>Flowchart of the proposed mix training detection approach.</p> Full article ">Figure 10
<p>Results of different detectors trained with different percentage of distorted samples on fisheye test sequences.</p> Full article ">Figure 11
<p>HOG features visualization in artificial distorted image samples. (<b>a</b>,<b>b</b>) The original <math display="inline"> <mrow> <mn>48</mn> <mo>×</mo> <mn>96</mn> </mrow> </math> training sample image and its distorted version; (<b>c</b>) original distorted image sample captured by a fisheye camera.</p> Full article ">Figure 12
<p>Illustration of adapted deformable part model to fisheye FOV.</p> Full article ">Figure 13
<p>Detection performance of the deformable part model (DPM) approach trained with different datasets (<b>a</b>) and the adaptive-DPM approach (<b>b</b>).</p> Full article ">Figure 14
<p>Examples of detection results with (<b>a</b>) the DPM approach and (<b>b</b>) the adaptive-DPM approach.</p> Full article ">Figure 15
<p>Processing steps in the LiDAR-based pedestrian detection system (PDS). (<b>a</b>) LiDAR points in polar coordinates; (<b>b</b>) point distance filtering; (<b>c</b>) two segments correspond to two obstacles in the FOV; (<b>d</b>) projection of points using calibration information; (<b>e</b>) image projection of adaptive ROIs; (<b>f</b>) final result of LiDAR-based PDS.</p> Full article ">Figure 16
<p>Computation of the ROI angle on the image.</p> Full article ">Figure 17
<p>Comparison of detection performance between the exclusively vision-based approaches and the LiDAR-fisheye camera PDS in Configuration 1: (<b>a</b>) ROC curve, (<b>b</b>) miss rate for lateral image position, (<b>c</b>) vision-only detections and (<b>d</b>) LiDAR-fisheye detections.</p> Full article ">Figure 18
<p>Comparison of detection performance between the exclusively vision-based approaches and the LiDAR-fisheye camera PDS in Configuration 2: (<b>a</b>) ROC curve, (<b>b</b>) Miss rate for lateral image position, (<b>c</b>) vision-only detections and (<b>d</b>) LiDAR-fisheye detections.</p> Full article ">Figure 19
<p>Diagram of the scenarios from the heavy machine dataset: machine stationary (<b>a</b>); machine moving forward (<b>b</b>); and machine turning (<b>c</b>,<b>d</b>).</p> Full article ">Figure 20
<p>Comparison of detection performance between the exclusively vision-based approaches and the LiDAR-fisheye camera PDS in different machine operating states: (<b>a</b>) static, (<b>b</b>) turning and (<b>c</b>) forward.</p> Full article ">Figure 20 Cont.
<p>Comparison of detection performance between the exclusively vision-based approaches and the LiDAR-fisheye camera PDS in different machine operating states: (<b>a</b>) static, (<b>b</b>) turning and (<b>c</b>) forward.</p> Full article ">
2671 KiB  
Article
Closed-Loop Control of Chemical Injection Rate for a Direct Nozzle Injection System
by Xiang Cai, Martin Walgenbach, Malte Doerpmond, Peter Schulze Lammers and Yurui Sun
Sensors 2016, 16(1), 127; https://doi.org/10.3390/s16010127 - 20 Jan 2016
Cited by 8 | Viewed by 8727
Abstract
To realize site-specific and variable-rate application of agricultural pesticides, accurately metering and controlling the chemical injection rate is necessary. This study presents a prototype of a direct nozzle injection system (DNIS) by which chemical concentration transport lag was greatly reduced. In this system, [...] Read more.
To realize site-specific and variable-rate application of agricultural pesticides, accurately metering and controlling the chemical injection rate is necessary. This study presents a prototype of a direct nozzle injection system (DNIS) by which chemical concentration transport lag was greatly reduced. In this system, a rapid-reacting solenoid valve (RRV) was utilized for injecting chemicals, driven by a pulse-width modulation (PWM) signal at 100 Hz, so with varying pulse width the chemical injection rate could be adjusted. Meanwhile, a closed-loop control strategy, proportional-integral-derivative (PID) method, was applied for metering and stabilizing the chemical injection rate. In order to measure chemical flow rates and input them into the controller as a feedback in real-time, a thermodynamic flowmeter that was independent of chemical viscosity was used. Laboratory tests were conducted to assess the performance of DNIS and PID control strategy. Due to the nonlinear input–output characteristics of the RRV, a two-phase PID control process obtained better effects as compared with single PID control strategy. Test results also indicated that the set-point chemical flow rate could be achieved within less than 4 s, and the output stability was improved compared to the case without control strategy. Full article
(This article belongs to the Section Physical Sensors)
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<p>Structure of the RRV.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic structure of the experimental setup with a boom section; (<b>b</b>) one of the injection units.</p> Full article ">Figure 3
<p>Setup for controlling pesticide injection rate by closed-loop method.</p> Full article ">Figure 4
<p>Block diagram of the closed-loop control.</p> Full article ">Figure 5
<p>Injection rate of RRV under three air pressures, calibrated by using 10% LUVITEC<sup>®</sup> solution.</p> Full article ">Figure 6
<p>Injection rates of 6 RRVs within one boom section, when PWM signal was with pulse width 1000 μs.</p> Full article ">Figure 7
<p>Calibration of the flowmeter.</p> Full article ">Figure 8
<p>Open loop response to a step input <span class="html-italic">P</span> = 1200 μs, to obtain the transfer function coefficients.</p> Full article ">Figure 9
<p>Variation in output of flowmeter as a function of time as flow rate controlled with four groups of different PID parameters, the set-point flowmeter = 8 V (equivalent to chemical flow rate = 11.8 mL/min).</p> Full article ">Figure 10
<p>Sectional linear input-output characteristic of RRV.</p> Full article ">Figure 11
<p>Time domain responses of the system, when the set-point flowmeter output is 5 V (equivalent to a chemical flow rate of 0.17 mL/min).</p> Full article ">Figure 12
<p>Output of the EC sensor when the desired output of the flowmeter was set at 8 V (equivalent to a desired chemical flow rate set at 11.8 mL/min).</p> Full article ">
4298 KiB  
Article
EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals
by Jiaduo Zhao, Weiguo Gong, Yuzhen Tang and Weihong Li
Sensors 2016, 16(1), 126; https://doi.org/10.3390/s16010126 - 20 Jan 2016
Cited by 11 | Viewed by 6369
Abstract
In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; [...] Read more.
In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector’s false alarms. Full article
(This article belongs to the Special Issue Infrared and THz Sensing and Imaging)
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<p>(<b>a</b>) Structure of a common PIR detector; (<b>b</b>) layout of the Fresnel lenses array; (<b>c</b>) top view of the distribution of the SZs and NZs in each detection layer, where the sectors labeled by “+” indicate the PSZs, those labeled by “−“ indicate the NSZs and the gaps between the PSZs and NSZs indicate the BZs; (<b>d</b>) lateral view of the distribution of the three detector layers.</p> Full article ">Figure 2
<p>Simulation of the adult’s (<b>a</b>) and the dog’s (<b>b</b>) PIR signals, where the squares with the symbol “+” in the front view of FOV indicate the PSZs and those with the symbol “−“ indicate the NSZs, the blank between PSZ and NSZ indicates the PSZs.</p> Full article ">Figure 3
<p>Real (blue-solid) and simulated (red-dashed) PIR signals of adult (<b>a</b>) and dog (<b>b</b>).</p> Full article ">Figure 4
<p>Constitution of the EMD-based symbolic time series analysis.</p> Full article ">Figure 5
<p>IMF components of an adult's PIR signal, where <b><span class="html-italic">C</span></b><span class="html-italic"><sub>i</sub></span> is the <span class="html-italic">i</span>th IMF component.</p> Full article ">Figure 6
<p>Illustration of the EMD-based symbolization method, where <span class="html-italic">C<sub>i</sub></span> indicates the <span class="html-italic">i</span>th IMF component and <span class="html-italic">S<sub>i</sub></span> indicates the symbol sequence generated from <span class="html-italic">C<sub>i</sub></span>.</p> Full article ">Figure 7
<p>An example of constructing a PFSA from a symbol sequence, the left-hand of the figure indicates the generation of state sequence and the right hand indicates the constructed PFSA, in which the circle indicates the state of the PFSA and the arrow indicates the transition between the states.</p> Full article ">Figure 8
<p>Construction of the weighted voting classification strategy, where <span class="html-italic">C<sub>i</sub></span> indicates the <span class="html-italic">i</span>th classifier.</p> Full article ">Figure 9
<p>Two-layer recognition procedure for indoor intrusion detection.</p> Full article ">Figure 10
<p>Recognition procedure of the outdoor pedestrian detection.</p> Full article ">
1030 KiB  
Article
Non-Cooperative Target Imaging and Parameter Estimation with Narrowband Radar Echoes
by Chun-mao Yeh, Wei Zhou, Yao-bing Lu and Jian Yang
Sensors 2016, 16(1), 125; https://doi.org/10.3390/s16010125 - 20 Jan 2016
Cited by 3 | Viewed by 4832
Abstract
This study focuses on the rotating target imaging and parameter estimation with narrowband radar echoes, which is essential for radar target recognition. First, a two-dimensional (2D) imaging model with narrowband echoes is established in this paper, and two images of the target are [...] Read more.
This study focuses on the rotating target imaging and parameter estimation with narrowband radar echoes, which is essential for radar target recognition. First, a two-dimensional (2D) imaging model with narrowband echoes is established in this paper, and two images of the target are formed on the velocity-acceleration plane at two neighboring coherent processing intervals (CPIs). Then, the rotating velocity (RV) is proposed to be estimated by utilizing the relationship between the positions of the scattering centers among two images. Finally, the target image is rescaled to the range-cross-range plane with the estimated rotational parameter. The validity of the proposed approach is confirmed using numerical simulations. Full article
(This article belongs to the Section Remote Sensors)
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<p>Rotating object geometry.</p> Full article ">Figure 2
<p>Numerical results with isolated scattering centers. (<b>a</b>) TFR of the received echoes; (<b>b</b>) TFR after TMC; (<b>c</b>) scattering center extraction and association result; (<b>d</b>) 2D image with first CPI; (<b>e</b>) 2D image with second CPI; (<b>f</b>) correlation coefficients with different RV; (<b>g</b>) cross-range scaled HRCRPs; and (<b>h</b>) range and cross-range scaled image with narrowband echoes.</p> Full article ">Figure 3
<p>Experiments with RCS Simulation. (<b>a</b>) RCS variation with aspect angle; (<b>b</b>) cross-range scaled HRCRPs of the ALCM; (<b>c</b>) TFR without TMC; (<b>d</b>) TFR with TMC; (<b>e</b>) extraction and association of scattering centers; (<b>f</b>) 2D image with first CPI; (<b>g</b>) 2D image with second CPI; (<b>h</b>) correlation coefficients with different RV; (<b>i</b>) cross-range scaled HRCRPs; (<b>j</b>) range and cross-range scaled image; and (<b>k</b>) target image rotated to the horizontal direction.</p> Full article ">Figure 4
<p>RV estimation for accelerated rotating object with isolated scattering model. (<b>a</b>) Correlation coefficients with different RV; and (<b>b</b>) range and cross-range scaled image with the estimated RV by image rotation correlation method.</p> Full article ">Figure 5
<p>RV estimation with accelerated rotating ALCM. (<b>a</b>) Correlation coefficients with different RV; and (<b>b</b>) range and cross-range scaled image with the estimated RV by image rotation correlation method.</p> Full article ">
9421 KiB  
Article
A Pedestrian Detection Scheme Using a Coherent Phase Difference Method Based on 2D Range-Doppler FMCW Radar
by Eugin Hyun, Young-Seok Jin and Jong-Hun Lee
Sensors 2016, 16(1), 124; https://doi.org/10.3390/s16010124 - 20 Jan 2016
Cited by 68 | Viewed by 19072
Abstract
For an automotive pedestrian detection radar system, fast-ramp based 2D range-Doppler Frequency Modulated Continuous Wave (FMCW) radar is effective for distinguishing between moving targets and unwanted clutter. However, when a weak moving target such as a pedestrian exists together with strong clutter, the [...] Read more.
For an automotive pedestrian detection radar system, fast-ramp based 2D range-Doppler Frequency Modulated Continuous Wave (FMCW) radar is effective for distinguishing between moving targets and unwanted clutter. However, when a weak moving target such as a pedestrian exists together with strong clutter, the pedestrian may be masked by the side-lobe of the clutter even though they are notably separated in the Doppler dimension. To prevent this problem, one popular solution is the use of a windowing scheme with a weighting function. However, this method leads to a spread spectrum, so the pedestrian with weak signal power and slow Doppler may also be masked by the main-lobe of clutter. With a fast-ramp based FMCW radar, if the target is moving, the complex spectrum of the range- Fast Fourier Transform (FFT) is changed with a constant phase difference over ramps. In contrast, the clutter exhibits constant phase irrespective of the ramps. Based on this fact, in this paper we propose a pedestrian detection for highly cluttered environments using a coherent phase difference method. By detecting the coherent phase difference from the complex spectrum of the range-FFT, we first extract the range profile of the moving pedestrians. Then, through the Doppler FFT, we obtain the 2D range-Doppler map for only the pedestrian. To test the proposed detection scheme, we have developed a real-time data logging system with a 24 GHz FMCW transceiver. In laboratory tests, we verified that the signal processing results from the proposed method were much better than those expected from the conventional 2D FFT-based detection method. Full article
(This article belongs to the Section Remote Sensors)
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<p>Basic concept of fast-ramp based 2D range-Doppler FMCW radar: (<b>a</b>) transmitted signal in the frequency-time domain; and (<b>b</b>) beat signal for a single moving target in ramp index domain. Here, <math display="inline"> <semantics> <mi>T</mi> </semantics> </math> is modulation period, <math display="inline"> <semantics> <mi>B</mi> </semantics> </math> is bandwidth, <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mi>H</mi> </msub> </mrow> </semantics> </math> is maximum instantaneous carrier frequency, <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mi>L</mi> </msub> </mrow> </semantics> </math> is the lowest carrier frequency, and <math display="inline"> <semantics> <mi>K</mi> </semantics> </math> is the number of ramps.</p> Full article ">Figure 2
<p>The proposed pedestrian detection scheme using coherent phase difference for 2D range-Doppler FMCW radar.</p> Full article ">Figure 3
<p>Scheme of the complex results of range-FFT processing in <span class="html-italic">K</span> ramps, consisting of clutter and one moving pedestrian at one <span class="html-italic">r</span>th range-bin: <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mi>D</mi> </msub> </mrow> </semantics> </math> is the detected Doppler-frequency by the radial velocity of the pedestrian, <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi mathvariant="sans-serif">φ</mi> <mi>c</mi> </msub> </mrow> </semantics> </math> is the constant initial phase of clutter, and <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi mathvariant="sans-serif">φ</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> is the coherent phase difference of the pedestrian.</p> Full article ">Figure 4
<p>The detection probability of a moving target when varying the scaling factor for the SNR.</p> Full article ">Figure 5
<p>The detection probability of a moving target when varying the scaling factor for the angular position: (<b>a</b>) scenario considered for the worst case; and (<b>b</b>) simulation results.</p> Full article ">Figure 6
<p>Measurement set-up using real time logging system together with 24 GHz FMCW transceiver: (<b>a</b>) block diagram of the developed radar test bed; and (<b>b</b>) photo of measurement set-up.</p> Full article ">Figure 7
<p>Configuration of the three measurement scenarios for the laboratory test.</p> Full article ">Figure 8
<p>Measurement results obtained using the conventional method and the proposed detection method for scenario (i). Here, (<b>a</b>–<b>c</b>) present the results of the typical method using windowing; (<b>d</b>) indicates the results of the typical method without windowing; (<b>e</b>,<b>f</b>) show the results of removing the zero Doppler components in the typical method; and (<b>g</b>–<b>i</b>) are the results of the proposed method.</p> Full article ">Figure 8 Cont.
<p>Measurement results obtained using the conventional method and the proposed detection method for scenario (i). Here, (<b>a</b>–<b>c</b>) present the results of the typical method using windowing; (<b>d</b>) indicates the results of the typical method without windowing; (<b>e</b>,<b>f</b>) show the results of removing the zero Doppler components in the typical method; and (<b>g</b>–<b>i</b>) are the results of the proposed method.</p> Full article ">Figure 9
<p>Measurement results using the conventional method and the proposed detection method for scenario (ii). Here, (<b>a</b>–<b>c</b>) present the results of the typical method using windowing; (<b>d</b>) indicates the results of the typical method without windowing; (<b>e</b>,<b>f</b>) show the results of removing the zero Doppler components in the typical method; and (<b>g</b>–<b>i</b>) are the results of the proposed method.</p> Full article ">Figure 9 Cont.
<p>Measurement results using the conventional method and the proposed detection method for scenario (ii). Here, (<b>a</b>–<b>c</b>) present the results of the typical method using windowing; (<b>d</b>) indicates the results of the typical method without windowing; (<b>e</b>,<b>f</b>) show the results of removing the zero Doppler components in the typical method; and (<b>g</b>–<b>i</b>) are the results of the proposed method.</p> Full article ">Figure 10
<p>Measurement results using the conventional method and the proposed detection method for scenario (iii). Here, (<b>a</b>–<b>c</b>) present the results of typical method using windowing; (<b>d</b>) indicates the results of the typical method without windowing; (<b>e</b>,<b>f</b>) show the results of removing the zero Doppler components in the typical method; and (<b>g</b>–<b>i</b>) are the results of the proposed method.</p> Full article ">
8221 KiB  
Article
Analysis of BeiDou Satellite Measurements with Code Multipath and Geometry-Free Ionosphere-Free Combinations
by Qile Zhao, Guangxing Wang, Zhizhao Liu, Zhigang Hu, Zhiqiang Dai and Jingnan Liu
Sensors 2016, 16(1), 123; https://doi.org/10.3390/s16010123 - 20 Jan 2016
Cited by 51 | Viewed by 7154
Abstract
Using GNSS observable from some stations in the Asia-Pacific area, the carrier-to-noise ratio (CNR) and multipath combinations of BeiDou Navigation Satellite System (BDS), as well as their variations with time and/or elevation were investigated and compared with those of GPS and Galileo. Provided [...] Read more.
Using GNSS observable from some stations in the Asia-Pacific area, the carrier-to-noise ratio (CNR) and multipath combinations of BeiDou Navigation Satellite System (BDS), as well as their variations with time and/or elevation were investigated and compared with those of GPS and Galileo. Provided the same elevation, the CNR of B1 observables is the lowest among the three BDS frequencies, while B3 is the highest. The code multipath combinations of BDS inclined geosynchronous orbit (IGSO) and medium Earth orbit (MEO) satellites are remarkably correlated with elevation, and the systematic “V” shape trends could be eliminated through between-station-differencing or modeling correction. Daily periodicity was found in the geometry-free ionosphere-free (GFIF) combinations of both BDS geostationary Earth orbit (GEO) and IGSO satellites. The variation range of carrier phase GFIF combinations of GEO satellites is −2.0 to 2.0 cm. The periodicity of carrier phase GFIF combination could be significantly mitigated through between-station differencing. Carrier phase GFIF combinations of BDS GEO and IGSO satellites might also contain delays related to satellites. Cross-correlation suggests that the GFIF combinations’ time series of some GEO satellites might vary according to their relative geometries with the sun. Full article
(This article belongs to the Section Remote Sensors)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The distribution of the GNSS stations.</p> Full article ">Figure 2
<p>The relationship between CNRs and satellite elevations for GPS, GLONASS, Galileo, and BDS satellites at GNSS station F713. (<b>a</b>) G06; (<b>b</b>) R01; (<b>c</b>) E11; (<b>d</b>) C11; (<b>e</b>) C08; (<b>f</b>) C03. Three frequencies are denoted as X1, X2, and X3 with the colors black, red and blue.</p> Full article ">Figure 3
<p>The multipath combinations (blue) and satellite elevations (red) for (<b>a</b>) GPS satellite G06, (<b>b</b>) GLONASS satellite R08, (<b>c</b>) Galileo satellite E11 and (<b>d</b>) BDS MEO satellite C14 at station F713.</p> Full article ">Figure 4
<p>The relationship between multipath combination (blue) and satellite elevations (red) for BDS (<b>a</b>) IGSO satellite C09 and (<b>b</b>) GEO satellite C03 at station F713.</p> Full article ">Figure 5
<p>Cross-correlations between the code multipath combinations <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> (black), <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics> </math>and <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>3</mn> </msub> </mrow> </semantics> </math> (red), <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>3</mn> </msub> </mrow> </semantics> </math> (blue) at stations (<b>a</b>) F713, (<b>b</b>) F783, (<b>c</b>) GMSD, and (<b>d</b>) JFNG.</p> Full article ">Figure 6
<p>Cross-correlations between the code multipath combinations <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> (black), <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> (red), <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>3</mn> </msub> </mrow> </semantics> </math> (blue) and the elevations at stations (<b>a</b>) F713, (<b>b</b>) F783, (<b>c</b>) GMSD, and (<b>d</b>) JFNG.</p> Full article ">Figure 7
<p>Cross-correlations between the code multipath combinations <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> (black), <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> (red), <math display="inline"> <semantics> <mrow> <mi>M</mi> <msub> <mi>P</mi> <mn>3</mn> </msub> </mrow> </semantics> </math> (blue) and the azimuths at stations (<b>a</b>) F713, (<b>b</b>) F783, (<b>c</b>) GMSD, and (<b>d</b>) JFNG.</p> Full article ">Figure 8
<p>The systematic bias in multipath combination is eliminated through a between-station differencing. The single-station elevation-angle dependent troughs for satellites (<b>a</b>) C07, (<b>b</b>) C09, (<b>c</b>) C13, and (<b>d</b>) C14 vanish after the between-station differencing is performed.</p> Full article ">Figure 9
<p>The multipath combinations of BDS satellites (<b>a</b>) C09 (IGSO) and (<b>b</b>) C14 (MEO); after the elevation-dependent correction model is applied.</p> Full article ">Figure 10
<p>GFIF combination (blue) and elevation (red) of BDS satellites at stations (<b>a</b>) GMSD and (<b>b,c</b>) CUT0.</p> Full article ">Figure 11
<p>GFIF combinations at station F783 (<b>top</b> <b>row</b>), station GMSD (<b>middle</b> <b>row</b>) and differencing result between the two stations (<b>bottom</b> <b>row</b>) for different BDS satellites, the red curve showing the satellite elevation.</p> Full article ">Figure 12
<p>Cross-correlations between the GFIF combination time series (ten days) of different GEO satellites at the stations GMSD (<b>a</b>) and CUT0 (<b>b</b>), with the red vertical line indicating the local time difference between the two satellites.</p> Full article ">
4066 KiB  
Article
Assessment of Three Tropospheric Delay Models (IGGtrop, EGNOS and UNB3m) Based on Precise Point Positioning in the Chinese Region
by Hongxing Zhang, Yunbin Yuan, Wei Li, Ying Li and Yanju Chai
Sensors 2016, 16(1), 122; https://doi.org/10.3390/s16010122 - 20 Jan 2016
Cited by 31 | Viewed by 5892
Abstract
Tropospheric delays are one of the main sources of errors in the Global Navigation Satellite System (GNSS). They are usually corrected by using tropospheric delay models, which makes the accuracy of the models rather critical for accurate positioning. To provide references for suitable [...] Read more.
Tropospheric delays are one of the main sources of errors in the Global Navigation Satellite System (GNSS). They are usually corrected by using tropospheric delay models, which makes the accuracy of the models rather critical for accurate positioning. To provide references for suitable models to be chosen for GNSS users in China, we conduct herein a comprehensive study of the performances of the IGGtrop, EGNOS and UNB3m models in China. Firstly, we assess the models using 5 years’ Global Positioning System (GPS) derived Zenith Tropospheric Delay (ZTD) series from 25 stations of the Crustal Movement Observation Network of China (CMONOC). Then we study the effects of the models on satellite positioning by using various Precise Point Positioning (PPP) cases with different tropospheric delay resolutions, the observation data processed in PPP is from 21 base stations of CMONOC for a whole year of 2012. The results show that: (1) the Root Mean Square (RMS) of the IGGtrop model is about 4.4 cm, which improves the accuracy of ZTD estimations by about 24% for EGNOS and 19% for UNB3m; (2) The positioning error in the vertical component of the PPP solution obtained by using the IGGtrop model is about 15.0 cm, which is about 30% and 21% smaller than those of the EGNOS and UNB3m models, respectively. In summary, the IGGtrop model achieves the best performance among the three models in the Chinese region. Full article
(This article belongs to the Section Remote Sensors)
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<p>Distribution of 25 selected stations of CMONOC used in this study.</p> Full article ">Figure 2
<p>Time series of ZTD derived from GPS and three empirical models (IGGtrop, EGNOS and UNB3m) over the time period from 2009 to 2013.</p> Full article ">Figure 3
<p>Histogram of the biases for the IGGtrop, EGNOS and UNB3m models over the period from 2009 to 2013 at four exemplary stations of JIXN, BJFS, HRBN and ZHNZ.</p> Full article ">Figure 4
<p>Temporal variations of monthly mean RMS (<b>a</b>) and Bias (<b>b</b>) for the IGGtrop, EGNOS and UNB3m models at four exemplary stations of BJFS, ZHNZ, KMIN and SHAO.</p> Full article ">Figure 5
<p>Mean bias (<b>a</b>) and RMS (<b>b</b>) over the period from 2009 to 2013 for the IGGtrop, EGNOS and UNB3m models. Stations are listed from left to right of the x-axis according to their station height from low to high.</p> Full article ">Figure 6
<p>Positioning errors of the conventional PPP and three model-based PPP (IGGtrop-based, EGNOS-based, UNB3m-based) at stations of BJFS, HRBN, KMIN and LHAZ, and the epoch interval is 30 s, the DOY are 28 (<b>a</b>) and 200 (<b>b</b>) in 2012.</p> Full article ">Figure 7
<p>Mean positioning errors of the conventional PPP, IGGtrop-based, EGNOS-based and UNB3m-based PPP solutions over the period from January to December 2012 at selected stations. The upper, medium and bottom panels show the positioning errors in north, east and up directions, respectively.</p> Full article ">Figure 8
<p>Time series of positioning errors for the conventional, IGGtrop-based, EGNOS-based and UNB3m-based PPP solutions at stations of BJFS, ZHNZ, XIAG and LHAZ, the DOY represents the day of year in 2012.</p> Full article ">
7127 KiB  
Article
Realtime Gas Emission Monitoring at Hazardous Sites Using a Distributed Point-Source Sensing Infrastructure
by Gianfranco Manes, Giovanni Collodi, Leonardo Gelpi, Rosanna Fusco, Giuseppe Ricci, Antonio Manes and Marco Passafiume
Sensors 2016, 16(1), 121; https://doi.org/10.3390/s16010121 - 20 Jan 2016
Cited by 27 | Viewed by 6032
Abstract
This paper describes a distributed point-source monitoring platform for gas level and leakage detection in hazardous environments. The platform, based on a wireless sensor network (WSN) architecture, is organised into sub-networks to be positioned in the plant’s critical areas; each sub-net includes a [...] Read more.
This paper describes a distributed point-source monitoring platform for gas level and leakage detection in hazardous environments. The platform, based on a wireless sensor network (WSN) architecture, is organised into sub-networks to be positioned in the plant’s critical areas; each sub-net includes a gateway unit wirelessly connected to the WSN nodes, hence providing an easily deployable, stand-alone infrastructure featuring a high degree of scalability and reconfigurability. Furthermore, the system provides automated calibration routines which can be accomplished by non-specialized maintenance operators without system reliability reduction issues. Internet connectivity is provided via TCP/IP over GPRS (Internet standard protocols over mobile networks) gateways at a one-minute sampling rate. Environmental and process data are forwarded to a remote server and made available to authenticated users through a user interface that provides data rendering in various formats and multi-sensor data fusion. The platform is able to provide real-time plant management with an effective; accurate tool for immediate warning in case of critical events. Full article
(This article belongs to the Special Issue Towards Energy-Neutral WSN Architectures: Energy Harvesting and Other Enabling Technologies)
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<p>(<b>a</b>) layout of the RAGE installation; (<b>b</b>) close-up of SNU and end node units (ENU) deployment around the wastewater treatment plant.</p> Full article ">Figure 2
<p>PID schematic.</p> Full article ">Figure 3
<p>Measured <span class="html-italic">vs.</span> linear PID calibration curve and relative error.</p> Full article ">Figure 4
<p>Ionization and diffusion process under steady-state conditions.</p> Full article ">Figure 5
<p>Concentration gradient.</p> Full article ">Figure 6
<p>Measured α functions for PID 11-6.</p> Full article ">Figure 7
<p>Average, measured α function and relative error percentage.</p> Full article ">Figure 8
<p>Calibration curves measured and calculated for the set of six PIDs.</p> Full article ">Figure 8 Cont.
<p>Calibration curves measured and calculated for the set of six PIDs.</p> Full article ">Figure 9
<p>(<b>a</b>) VOC and (<b>b</b>) H<sub>2</sub>S concentrations in the wastewater treatment plant area.</p> Full article ">Figure 9 Cont.
<p>(<b>a</b>) VOC and (<b>b</b>) H<sub>2</sub>S concentrations in the wastewater treatment plant area.</p> Full article ">Figure 10
<p>Layout of the six VOC detectors located around a chemical plant in Mantova.</p> Full article ">Figure 11
<p>VOC readouts from (<b>a</b>) northern side (Sink Node ENI 6) and (<b>b</b>) southern side (Sink Node ENI 7) with related wind direction (light green line) on Mantova installation.</p> Full article ">Figure 12
<p>Concentration (radius, ppb) <span class="html-italic">versus</span> wind direction (angle) polar plot over 24 h.</p> Full article ">Figure 13
<p>Pseudo-colour map of (<b>a</b>) VOC and (<b>b</b>) H<sub>2</sub>S concentrations over the RAGE refinery area.</p> Full article ">Figure 13 Cont.
<p>Pseudo-colour map of (<b>a</b>) VOC and (<b>b</b>) H<sub>2</sub>S concentrations over the RAGE refinery area.</p> Full article ">
2864 KiB  
Article
DC-Compensated Current Transformer
by Pavel Ripka, Karel Draxler and Renata Styblíková
Sensors 2016, 16(1), 114; https://doi.org/10.3390/s16010114 - 20 Jan 2016
Cited by 19 | Viewed by 8330
Abstract
Instrument current transformers (CTs) measure AC currents. The DC component in the measured current can saturate the transformer and cause gross error. We use fluxgate detection and digital feedback compensation of the DC flux to suppress the overall error to 0.15%. This concept [...] Read more.
Instrument current transformers (CTs) measure AC currents. The DC component in the measured current can saturate the transformer and cause gross error. We use fluxgate detection and digital feedback compensation of the DC flux to suppress the overall error to 0.15%. This concept can be used not only for high-end CTs with a nanocrystalline core, but it also works for low-cost CTs with FeSi cores. The method described here allows simultaneous measurements of the DC current component. Full article
(This article belongs to the Special Issue Magnetic Sensor Device-Part 2)
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<p>Ratio error ε<sub>I</sub> and phase error δ<sub>I</sub> of the 500 A/5 A current transformer CT1 as a function of the spurious DC current I<sub>1DC</sub>. AC measured current I<sub>1AC</sub> is a parameter.</p> Full article ">Figure 2
<p>Ratio error ε<sub>I</sub> and phase error δ<sub>I</sub> of the 500 A/1 A current transformer CT2 for zero DC current and I<sub>DC</sub> = 5 A as a function of measured current I<sub>1</sub>.</p> Full article ">Figure 3
<p>The I<sub>exc</sub> for several values of I<sub>DC</sub>; the vertical scale is 1 A/div. Measured on CT2.</p> Full article ">Figure 4
<p>Second harmonic voltage V<sub>2</sub> as a function of the DC current I<sub>DC</sub> for R<sub>1</sub> = 0 (dashed line) and R<sub>1</sub> = 5 Ω (solid line).</p> Full article ">Figure 5
<p>DC-compensated current transformer.</p> Full article ">Figure 6
<p>Closed-loop response to DC current I<sub>1DC;</sub> AC measured current I<sub>1AC</sub> is a parameter. Tested for low impedance in the primary circuit. The units for both axes are Ampere-Turns (magnetic voltage).</p> Full article ">Figure 7
<p>Closed-loop response to DC current for high impedance in the primary circuit. The parameter is the measured AC current I<sub>1</sub>.</p> Full article ">Figure 8
<p>Measurement setup for testing the accuracy of the DC-compensated CT using a lock-in amplifier.</p> Full article ">Figure 9
<p>Amplitude and phase errors of the DC-compensated current transformer as a function of the primary DC current.</p> Full article ">
5200 KiB  
Article
Dynamic Vehicle Detection via the Use of Magnetic Field Sensors
by Vytautas Markevicius, Dangirutis Navikas, Mindaugas Zilys, Darius Andriukaitis, Algimantas Valinevicius and Mindaugas Cepenas
Sensors 2016, 16(1), 78; https://doi.org/10.3390/s16010078 - 19 Jan 2016
Cited by 56 | Viewed by 9891
Abstract
The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. [...] Read more.
The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. In accordance with the results obtained from research into process modeling and experimentally testing all the relevant hypotheses an algorithm for vehicle detection using the state criteria was proposed. Aiming to evaluate all of the possibilities, as well as pros and cons of the use of anisotropic magnetoresistance (AMR) sensors in the transport flow control process, we have performed a series of experiments with various vehicles (or different series) from several car manufacturers. A comparison of 12 selected methods, based on either the process of determining the peak signal values and their concurrence in time whilst calculating the delay, or by measuring the cross-correlation of these signals, was carried out. It was established that the relative error can be minimized via the Z component cross-correlation and Kz criterion cross-correlation methods. The average relative error of vehicle speed determination in the best case did not exceed 1.5% when the distance between sensors was set to 2 m. Full article
(This article belongs to the Special Issue Magnetic Sensor Device-Part 2)
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<p>Experiment structure.</p> Full article ">Figure 2
<p>Vehicle scanning scheme.</p> Full article ">Figure 3
<p>The distribution of the magnetic field measured by two distinct sensors (plots of magnetic field components X1, Y1, Z1 and X2, Y2, Z2 of sensors 1 and 2 respectively). Plot curves are color coded by sensor position along latitudinal vehicle line.</p> Full article ">Figure 4
<p>The distribution of the magnetic field distortion (Z component) caused by three different vehicles (different color means different position of sensor with respect to the Y axis—across the vehicle).</p> Full article ">Figure 4 Cont.
<p>The distribution of the magnetic field distortion (Z component) caused by three different vehicles (different color means different position of sensor with respect to the Y axis—across the vehicle).</p> Full article ">Figure 5
<p>The gathered data when using different methods (1—Z component, 2—Module, 3—Vectorial deviation, 4—Combined vectorial deviation, 5—<span class="html-italic">K<sub>z</sub></span> criterium).</p> Full article ">Figure 6
<p>A view of the data samples from two sensors (<b>a</b>) and samples matching (<b>b</b>) via the use of the Z component.</p> Full article ">Figure 7
<p>Box plot of the methods’ (<a href="#sensors-16-00078-t001" class="html-table">Table 1</a>) relative errors.</p> Full article ">Figure 8
<p>The dependence of error on the position of sensors with respect to vehicles using the second method.</p> Full article ">Figure 9
<p>The dependence of error on the position of sensors with respect to vehicles using the tenth method.</p> Full article ">
1228 KiB  
Article
Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building
by Allan Melvin Andrew, Ammar Zakaria, Shaharil Mad Saad and Ali Yeon Md Shakaff
Sensors 2016, 16(1), 31; https://doi.org/10.3390/s16010031 - 19 Jan 2016
Cited by 19 | Viewed by 7846
Abstract
In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or “smellprint” emanated from various fire [...] Read more.
In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or “smellprint” emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalised feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odour signals in the time domain. The obtained features undergo the proposed multi-stage feature selection technique and lastly, further reduced by Principal Component Analysis (PCA), a dimension reduction technique. The hybrid PCA-PNN based approach has been applied on different datasets from in-house developed system and the portable electronic nose unit. Experimental classification results show that the dimension reduction process performed by PCA has improved the classification accuracy and provided high reliability, regardless of ambient temperature and humidity variation, baseline sensor drift, the different gas concentration level and exposure towards different heating temperature range. Full article
(This article belongs to the Special Issue Sensors for Fire Detection)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>A flowchart of the proposed multi- stage feature selection approach using PCA and PNN.</p> Full article ">Figure 2
<p>(<b>a</b>) Example of raw data for a scorching smell generated by paper at 250 °C; (<b>b</b>) The RLSSV feature extracted from the scorching smell of paper at 250 °C in (a).</p> Full article ">Figure 3
<p>Feature Fusion Process for IAQ- PCA Hybrid Features.</p> Full article ">Figure 4
<p>PNN Architecture.</p> Full article ">Figure 5
<p>Multi-stage Feature Selection and Fusion Process Flow.</p> Full article ">
4350 KiB  
Article
Pedestrian Navigation Using Foot-Mounted Inertial Sensor and LIDAR
by Duy Duong Pham and Young Soo Suh
Sensors 2016, 16(1), 120; https://doi.org/10.3390/s16010120 - 19 Jan 2016
Cited by 31 | Viewed by 7432
Abstract
Foot-mounted inertial sensors can be used for indoor pedestrian navigation. In this paper, to improve the accuracy of pedestrian location, we propose a method using a distance sensor (LIDAR) in addition to an inertial measurement unit (IMU). The distance sensor is a time [...] Read more.
Foot-mounted inertial sensors can be used for indoor pedestrian navigation. In this paper, to improve the accuracy of pedestrian location, we propose a method using a distance sensor (LIDAR) in addition to an inertial measurement unit (IMU). The distance sensor is a time of flight range finder with 30 m measurement range (at 33.33 Hz). Using a distance sensor, walls on corridors are automatically detected. The detected walls are used to correct the heading of the pedestrian path. Through experiments, it is shown that the accuracy of the heading is significantly improved using the proposed algorithm. Furthermore, the system is shown to work robustly in indoor environments with many doors and passing people. Full article
(This article belongs to the Section Physical Sensors)
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<p>IMU and distance sensor on a shoe.</p> Full article ">Figure 2
<p>Vertical plane detection using LIDAR.</p> Full article ">Figure 3
<p>LIDAR calibration (the sensor unit is handheld while LIDAR is pointing at the floor for the calibration).</p> Full article ">Figure 4
<p>Proposed position and heading updating algorithm using the distance sensor.</p> Full article ">Figure 5
<p>New vertical plane detection.</p> Full article ">Figure 6
<p>IMU and LIDAR system for the experiment.</p> Full article ">Figure 7
<p>Corridor with doors and passing persons.</p> Full article ">Figure 8
<p>Pedestrian path using a pure INA.</p> Full article ">Figure 9
<p>Pedestrian path using the proposed method.</p> Full article ">Figure 10
<p>Pedestrian path in the U shaped corridor using a pure INA.</p> Full article ">Figure 11
<p>Pedestrian path in the U shaped corridor using the proposed method.</p> Full article ">Figure 12
<p>Pedestrian with complex path.</p> Full article ">Figure 13
<p>Trajectory of foot and detected zero velocity points.</p> Full article ">Figure 14
<p><span class="html-italic">z</span>-position of foot and detected zero velocity points.</p> Full article ">
5763 KiB  
Article
Mapping Vineyard Leaf Area Using Mobile Terrestrial Laser Scanners: Should Rows be Scanned On-the-Go or Discontinuously Sampled?
by Ignacio Del-Moral-Martínez, Joan R. Rosell-Polo, Joaquim Company, Ricardo Sanz, Alexandre Escolà, Joan Masip, José A. Martínez-Casasnovas and Jaume Arnó
Sensors 2016, 16(1), 119; https://doi.org/10.3390/s16010119 - 19 Jan 2016
Cited by 33 | Viewed by 7560
Abstract
The leaf area index (LAI) is defined as the one-side leaf area per unit ground area, and is probably the most widely used index to characterize grapevine vigor. However, LAI varies spatially within vineyard plots. Mapping and quantifying this variability is very important [...] Read more.
The leaf area index (LAI) is defined as the one-side leaf area per unit ground area, and is probably the most widely used index to characterize grapevine vigor. However, LAI varies spatially within vineyard plots. Mapping and quantifying this variability is very important for improving management decisions and agricultural practices. In this study, a mobile terrestrial laser scanner (MTLS) was used to map the LAI of a vineyard, and then to examine how different scanning methods (on-the-go or discontinuous systematic sampling) may affect the reliability of the resulting raster maps. The use of the MTLS allows calculating the enveloping vegetative area of the canopy, which is the sum of the leaf wall areas for both sides of the row (excluding gaps) and the projected upper area. Obtaining the enveloping areas requires scanning from both sides one meter length section along the row at each systematic sampling point. By converting the enveloping areas into LAI values, a raster map of the latter can be obtained by spatial interpolation (kriging). However, the user can opt for scanning on-the-go in a continuous way and compute 1-m LAI values along the rows, or instead, perform the scanning at discontinuous systematic sampling within the plot. An analysis of correlation between maps indicated that MTLS can be used discontinuously in specific sampling sections separated by up to 15 m along the rows. This capability significantly reduces the amount of data to be acquired at field level, the data storage capacity and the processing power of computers. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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<p>Plot of <span class="html-italic">Vitis vinifera</span> L. cv. Syrah (<b>left</b>), and location of the six rows scanned with the terrestrial laser scanner (<b>right</b>) [<a href="#B32-sensors-16-00119" class="html-bibr">32</a>]. The length of the vine rows is 360 m.</p> Full article ">Figure 2
<p>Image of the different sensors (<b>A</b>); interaction of the MTLS components (<b>B</b>); and connection to the three-point hitch of the tractor (<b>C</b>).</p> Full article ">Figure 3
<p>1-m long section of the row including <span class="html-italic">scans</span> obtained from both sides of the row. (<b>A</b>) and (<b>B</b>) are views of the same scene from different perspective. The LAI estimation is assigned to the <span class="html-italic">sampling point</span>.</p> Full article ">Figure 4
<p>Intercepted points generated by consecutive scans and specific pixelated area (Sj) assigned to one of interception points (Pj).</p> Full article ">Figure 5
<p>Diagram of data processing to compute the envelope vegetative area of the 1-m length section.</p> Full article ">Figure 6
<p>Measured and estimated LAI in three different contrasting vineyard blocks.</p> Full article ">Figure 7
<p>Flowchart for obtaining and comparing LAI raster maps.</p> Full article ">Figure 8
<p>(<b>a</b>) Sampling points, (<b>b</b>) LAI raster (interpolated) map and (<b>c</b>) LAI classified maps (2 classes and 3 classes) in an area of 0.70 ha within a plot in a vineyard.</p> Full article ">Figure 9
<p>Different modes of operation of a MTLS along a row of vines. The rectangular area indicates the scanned sampling sections. (<b>A</b>) On-the-go scanning mode; (<b>B</b>) discontinuous scanning mode separating the sampling sections 1 m.</p> Full article ">Figure 10
<p>Sampling points, LAI raster maps, and LAI classified maps for different MTLS sampling schemes along the rows. The sampling sections are separated 1 m (<b>A</b>); 10 m (<b>B</b>); 15 m (<b>C</b>); and 20 m (<b>D</b>).</p> Full article ">
6193 KiB  
Article
Defence against Black Hole and Selective Forwarding Attacks for Medical WSNs in the IoT
by Avijit Mathur, Thomas Newe and Muzaffar Rao
Sensors 2016, 16(1), 118; https://doi.org/10.3390/s16010118 - 19 Jan 2016
Cited by 68 | Viewed by 9449
Abstract
Wireless sensor networks (WSNs) are being used to facilitate monitoring of patients in hospital and home environments. These systems consist of a variety of different components/sensors and many processes like clustering, routing, security, and self-organization. Routing is necessary for medical-based WSNs because it [...] Read more.
Wireless sensor networks (WSNs) are being used to facilitate monitoring of patients in hospital and home environments. These systems consist of a variety of different components/sensors and many processes like clustering, routing, security, and self-organization. Routing is necessary for medical-based WSNs because it allows remote data delivery and it facilitates network scalability in large hospitals. However, routing entails several problems, mainly due to the open nature of wireless networks, and these need to be addressed. This paper looks at two of the problems that arise due to wireless routing between the nodes and access points of a medical WSN (for IoT use): black hole and selective forwarding (SF) attacks. A solution to the former can readily be provided through the use of cryptographic hashes, while the latter makes use of a neighbourhood watch and threshold-based analysis to detect and correct SF attacks. The scheme proposed here is capable of detecting a selective forwarding attack with over 96% accuracy and successfully identifying the malicious node with 83% accuracy. Full article
(This article belongs to the Special Issue Security and Privacy in Sensor Networks)
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<p>System model.</p> Full article ">Figure 2
<p>System overview.</p> Full article ">Figure 3
<p>Network model.</p> Full article ">Figure 4
<p>Black hole attack simulation on Cooja simulator (Contiki): (<b>a</b>) nodes layout. Source node green “2”, destination is blue “1”, and malicious node is red “5”; and (<b>b</b>) Mote output: node “2” sending data packets to node “1” via node “5”, but these packets never reach their destination.</p> Full article ">Figure 5
<p>Pre-Deployment process: The access points receiving their respective unique random numbers from the BS.</p> Full article ">Figure 6
<p>Routing Phase: (<b>a</b>) AODV protocol; (<b>b</b>) data direction; and (<b>c</b>) modification for our system.</p> Full article ">Figure 7
<p>RREQ Packet.</p> Full article ">Figure 8
<p>Neighbour monitoring process.</p> Full article ">Figure 9
<p>Packets: (<b>a</b>) data Packet; and (<b>b</b>) ACK packet.</p> Full article ">Figure 10
<p>Selective forwarding: (<b>a</b>) detection and correction process; and (<b>b</b>) new path formation.</p> Full article ">Figure 11
<p>Fix for scenario when malicious node drops CPs.</p> Full article ">Figure 12
<p>Testbed layout. The arrows represent DATA packets, and the bolts represent monitoring the neighbour node’s DATA sending habit.</p> Full article ">Figure 13
<p>Network Stack.</p> Full article ">Figure 14
<p>Power consumption for protocol—normal operation.</p> Full article ">Figure 15
<p>Power consumption for protocol—detection phase.</p> Full article ">Figure 16
<p>Experimental setup of the network.</p> Full article ">Figure 17
<p>Current consumption of OpenMote running modified protocol during detection phase—nullrdc.</p> Full article ">Figure 18
<p>Current consumption of OpenMote running modified protocol during normal phase.</p> Full article ">Figure 19
<p>Memory footprint for the program on an OpenMote.</p> Full article ">
1528 KiB  
Article
Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
by Tao Huang, Siyu Yan, Fan Yang, Tian Pan and Jiang Liu
Sensors 2016, 16(1), 108; https://doi.org/10.3390/s16010108 - 19 Jan 2016
Cited by 16 | Viewed by 7199
Abstract
Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, [...] Read more.
Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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<p>System architecture.</p> Full article ">Figure 2
<p>(<b>a</b>) State transition; (<b>b</b>) tracking connection state in the situation that the link is disconnected. In ACTIVE, because the switch can’t receive any messages or even echo reply messages from the controller, it moves into IDLE. Similarly, it finally moves from IDLE to DISCONNECTION.</p> Full article ">Figure 3
<p>Synchronizing the real-time connection state to the kernel datapath and user space for encapsulating a lookup key. The key containing the connection state can match stateful rules.</p> Full article ">Figure 4
<p>Connection-state processing module. Step 1 is to track the connection state. Step 2 is to transmit the state detected to the kernel and user space. Step 3 is to support a stateful match in the kernel. Step 4 is to support a stateful match in user space.</p> Full article ">Figure 5
<p>Self-learning module. Step 1 is to make packets match against rules in flow tables. Step 2 is to judge whether or not the rule’s action is Self_Learning. Step 3 is to extract Src-Ip and ImPort from the packet’s header. Step 4 is to insert/update a new rule according to the information extracted.</p> Full article ">Figure 6
<p>Failure Recovery Time of CDF.</p> Full article ">Figure 7
<p>The number of consecutive packet losses during recovery time.</p> Full article ">Figure 8
<p>Instant throughput over time.</p> Full article ">
508 KiB  
Article
Wireless Relay Selection in Pocket Switched Networks Based on Spatial Regularity of Human Mobility
by Jianhui Huang, Xiuzhen Cheng, Jingping Bi and Biao Chen
Sensors 2016, 16(1), 94; https://doi.org/10.3390/s16010094 - 18 Jan 2016
Cited by 6 | Viewed by 4941
Abstract
Pocket switched networks (PSNs) take advantage of human mobility to deliver data. Investigations on real-world trace data indicate that human mobility shows an obvious spatial regularity: a human being usually visits a few places at high frequencies. These most frequently visited places form [...] Read more.
Pocket switched networks (PSNs) take advantage of human mobility to deliver data. Investigations on real-world trace data indicate that human mobility shows an obvious spatial regularity: a human being usually visits a few places at high frequencies. These most frequently visited places form the home of a node, which is exploited in this paper to design two HomE based Relay selectiOn (HERO) algorithms. Both algorithms input single data copy into the network at any time. In the basic HERO, only the first node encountered by the source and whose home overlaps a destination’s home is selected as a relay while the enhanced HERO keeps finding more optimal relay that visits the destination’s home with higher probability. The two proposed algorithms only require the relays to exchange the information of their home and/or the visiting frequencies to their home when two nodes meet. As a result, the information update is reduced and there is no global status information that needs to be maintained. This causes light loads on relays because of the low communication cost and storage requirements. Additionally, only simple operations are needed in the two proposed algorithms, resulting in little computation overhead at relays. At last, a theoretical analysis is performed on some key metrics and then the real-world based simulations indicate that the two HERO algorithms are efficient and effective through employing only one or a few relays. Full article
(This article belongs to the Special Issue Identification, Information & Knowledge in the Internet of Things)
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<p>The snapshots of four mobile nodes: (<b>a</b>) from the data set 21 September 2003 to 20 October 2003; (<b>b</b>) from the data set 21 October 2003 to 19 November 2003; (<b>c</b>) from the data set 28 January 2004 to 26 February 2004; and (<b>d</b>) from the data set 20 April 2004 to 19 May 2004.</p> Full article ">Figure 2
<p>The HERO framework. In the framework, the whole network is divided into multiple non-overlapping zones which are identified by its center coordinates.</p> Full article ">Figure 3
<p>The Markov model of node mobility. Each state in the model represents the zone at which a node resides and the transition probability indicates the probability that node transfers among zones.</p> Full article ">Figure 4
<p>Average data holding time, which is the duration from the time when the data is generated at the source to the time when the source sends it to the first relay or the source drops it due to data expiration.</p> Full article ">Figure 5
<p>Average relay latency of the basic HERO algorithm, which is the duration from the time when the source sends the data to the first relay to the time when the data is delivered to the destination zone. (<b>a</b>) Relay 1; (<b>b</b>) Relay 2; (<b>c</b>) Relay 3; (<b>d</b>) Relay 4.</p> Full article ">Figure 6
<p>The impact of home size on the delivery ratio of HERO, which shows that delivery ratios are improved with the increase of average home size in all scenarios.</p> Full article ">Figure 7
<p>The impact of home size on the average relay latency, which shows that the relay latencies of enhanced HERO are better than those of basic HERO in all scenarios. (<b>a</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>300</mn> </mrow> </math>; (<b>b</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>400</mn> </mrow> </math>; (<b>c</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>500</mn> </mrow> </math>; (<b>d</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>600</mn> </mrow> </math>.</p> Full article ">Figure 8
<p>The impact of home size on the average total latency, which shows that the total latencies of enhanced HERO are better than those of basic HERO in all scenarios. (<b>a</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>300</mn> </mrow> </math>; (<b>b</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>400</mn> </mrow> </math>; (<b>c</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>500</mn> </mrow> </math>; (<b>d</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>600</mn> </mrow> </math>.</p> Full article ">Figure 9
<p>The delivery ratio and the relay latency under different average home size. The figure shows when the average home size equals 12.06, the best performance of both HERO algorithms can be obtained. (<b>a</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>300</mn> </mrow> </math>; (<b>b</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>400</mn> </mrow> </math>; (<b>c</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>500</mn> </mrow> </math>; (<b>d</b>) <math display="inline"> <mrow> <mi>U</mi> <mo>=</mo> <mn>600</mn> </mrow> </math>.</p> Full article ">Figure 10
<p>Comparison of delivery ratio. With the help of home, the delivery ratios of two HEROs are much better than that of the MobySpace.</p> Full article ">Figure 11
<p>Comparison of relay latency. With the help of home zones, the relay latencies of two HERO algorithms are superior to that of the MobySpace algorithm.</p> Full article ">Figure 12
<p>Comparison of total latency, which shows that the total delaies of two HERO algorithms are better than that of the MobySpace algorithms.</p> Full article ">Figure 13
<p>The average number of relays in the four schemes. Owing to the adopted single-copy mechanism, the relay numbers of HERO algorithms are less than those of Epidemic and Mobyspace algorithms.</p> Full article ">
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