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Sensors, Volume 16, Issue 10 (October 2016) – 223 articles

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4480 KiB  
Article
Geometric Calibration and Validation of Kompsat-3A AEISS-A Camera
by Doocheon Seo, Jaehong Oh, Changno Lee, Donghan Lee and Haejin Choi
Sensors 2016, 16(10), 1776; https://doi.org/10.3390/s16101776 - 24 Oct 2016
Cited by 16 | Viewed by 7100
Abstract
Kompsat-3A, which was launched on 25 March 2015, is a sister spacecraft of the Kompsat-3 developed by the Korea Aerospace Research Institute (KARI). Kompsat-3A’s AEISS-A (Advanced Electronic Image Scanning System-A) camera is similar to Kompsat-3’s AEISS but it was designed to provide PAN [...] Read more.
Kompsat-3A, which was launched on 25 March 2015, is a sister spacecraft of the Kompsat-3 developed by the Korea Aerospace Research Institute (KARI). Kompsat-3A’s AEISS-A (Advanced Electronic Image Scanning System-A) camera is similar to Kompsat-3’s AEISS but it was designed to provide PAN (Panchromatic) resolution of 0.55 m, MS (multispectral) resolution of 2.20 m, and TIR (thermal infrared) at 5.5 m resolution. In this paper we present the geometric calibration and validation work of Kompsat-3A that was completed last year. A set of images over the test sites was taken for two months and was utilized for the work. The workflow includes the boresight calibration, CCDs (charge-coupled devices) alignment and focal length determination, the merge of two CCD lines, and the band-to-band registration. Then, the positional accuracies without any GCPs (ground control points) were validated for hundreds of test sites across the world using various image acquisition modes. In addition, we checked the planimetric accuracy by bundle adjustments with GCPs. Full article
(This article belongs to the Special Issue Imaging: Sensors and Technologies)
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Figure 1

Figure 1
<p>Kompsat-3A AEISS-A (Advanced Electronic Image Scanning System-A) sensor configuration. (<b>a</b>) Camera rear view; (<b>b</b>) CCD array configurations.</p> Full article ">Figure 2
<p>Kompsat-3A AEISS-A panchromatic CCD-lines configuration with an overlapping zone (the scan direction is upward).</p> Full article ">Figure 3
<p>The relationship between the sensor coordinate frame and the body coordinate frame.</p> Full article ">Figure 4
<p>Summarized geometric calibration procedure of Kompsat-3A.</p> Full article ">Figure 5
<p>Level 1 site locations.</p> Full article ">Figure 6
<p>Horizontal accuracy in the ground before (<b>a</b>) and after the AOCS calibration (<b>b</b>).</p> Full article ">Figure 7
<p>CCD alignment plots for PAN#1 and BLUE#1.</p> Full article ">Figure 8
<p>Horizontal accuracy before (<b>a</b>) and after the CCD alignment calibration (<b>b</b>).</p> Full article ">Figure 9
<p>Comparison between before and after the merge of subimages.</p> Full article ">Figure 10
<p>Discrepancy between subimages after the merge for various image acquisition modes.</p> Full article ">Figure 11
<p>Examples of the band-to-band registration results.</p> Full article ">Figure 12
<p>Horizontal accuracy for various acquisition modes without any GCPs.</p> Full article ">Figure 13
<p>Mapping accuracy for 16 independent data.</p> Full article ">
2611 KiB  
Article
Optical Fiber Temperature and Torsion Sensor Based on Lyot-Sagnac Interferometer
by Li-Yang Shao, Xinpu Zhang, Haijun He, Zhiyong Zhang, Xihua Zou, Bin Luo, Wei Pan and Lianshan Yan
Sensors 2016, 16(10), 1774; https://doi.org/10.3390/s16101774 - 24 Oct 2016
Cited by 28 | Viewed by 6226
Abstract
An optical fiber temperature and torsion sensor has been proposed by employing the Lyot-Sagnac interferometer, which was composed by inserting two sections of high-birefringence (HiBi) fiber into the Sagnac loop. The two inserted sections of HiBi fiber have different functions; while one section [...] Read more.
An optical fiber temperature and torsion sensor has been proposed by employing the Lyot-Sagnac interferometer, which was composed by inserting two sections of high-birefringence (HiBi) fiber into the Sagnac loop. The two inserted sections of HiBi fiber have different functions; while one section acts as the temperature sensitive region, the other can be used as reference fiber. The temperature and twist sensor based on the proposed interferometer structure have been experimentally demonstrated. The experimental results show that the envelope of the output spectrum will shift with the temperature evolution. The temperature sensitivity is calculated to be −17.99 nm/°C, which is enlarged over 12 times compared to that of the single Sagnac interferometer. Additionally, the fringe visibility of the spectrum will change due to the fiber twist, and the test results reveal that the fringe visibility and twist angle perfectly conform to a Sine relationship over a 360° twist angle. Consequently, simultaneous torsion and temperature measurement could be realized by detecting the envelope shift and fringe visibility of the spectrum. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1
<p>Experimental setup of the Lyot-Sagnac interferometer. ASE: Amplified spontaneous emission; HiBi: High-birefringence; OSA: Optical spectrum analyzer; PC: Polarization controller.</p> Full article ">Figure 2
<p>Spectra of the conventional Sagnac and Lyot-Sagnac interferometers.</p> Full article ">Figure 3
<p>(<b>a</b>) Spectra of Lyot-Sagnac interferometer at 40.0 °C and 40.4 °C; (<b>b</b>) Temperature responses of the Lyot-Sagnac interferometer.</p> Full article ">Figure 4
<p>(<b>a</b>) Fast-Fourier transform (FFT) of the spectra; (<b>b</b>) Torsion responses of the Lyot-Sagnac interferometer.</p> Full article ">Figure 5
<p>Spectra of Lyot-Sagnac sensor at the 20.0 °C and 20.2 °C.</p> Full article ">Figure 6
<p>(<b>a</b>) Temperature responses of the Lyot-Sagnac interferometer under different twist angles; (<b>b</b>) Torsion responses of the Lyot-Sagnac interferometer-based sensor.</p> Full article ">
2854 KiB  
Article
Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging
by Yichun Zhang, Tielin Shi, Lei Su, Xiao Wang, Yuan Hong, Kepeng Chen and Guanglan Liao
Sensors 2016, 16(10), 1773; https://doi.org/10.3390/s16101773 - 24 Oct 2016
Cited by 9 | Viewed by 5463
Abstract
Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then [...] Read more.
Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l1-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging. Full article
(This article belongs to the Special Issue Ultrasonic Sensors)
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Figure 1
<p>The simulation model (unit in μm ): (<b>a</b>) The geometrical model; (<b>b</b>) Area segments in the model with defect; (<b>c</b>) Area segments in the model without defect; (<b>d</b>) Meshing with 10 elements per wavelength, and the position of defect is offset to emulate the transducer scanning in AMI.</p> Full article ">Figure 2
<p>Characteristics of the VT in solid (unit in μm): (<b>a</b>) Beam profile of the VT with diagram of DOF and the spot size; (<b>b</b>) Spot size measurement in lateral direction; (<b>c</b>) Depth of field measurement in vertical direction.</p> Full article ">Figure 3
<p>The A-scan, C-scan, and PSF (unit in μm): (<b>a</b>) A-scan of the model with the transducer at position 0 μm; (<b>b</b>) B-scan like image; (<b>c</b>) C-line; (<b>d</b>) C-scan; (<b>e</b>) PSF extracted from (<b>d</b>).</p> Full article ">Figure 4
<p>Results of the single groove (unit in μm): (<b>a</b>) Schematic diagram of AMI; (<b>b</b>) The topography of the groove; (<b>c</b>) Average profile of the groove; (<b>d</b>) Original C-scan image; (<b>e</b>) The reconstructed image formed by AMISR; (<b>f</b>) Average values of the regions indicated in (d) and (e); (<b>g</b>) The cross section of the groove; (<b>h</b>) The original C-scan image superimposed with the mask; (<b>i</b>) The reconstructed image superimposed with the mask.</p> Full article ">Figure 5
<p>Results of two grooves (unit in μm): (<b>a</b>) The topography of the grooves; (<b>b</b>) Average profile of the grooves; (<b>c</b>) Original C-scan image; (<b>d</b>) The reconstructed image formed by AMISR; (<b>e</b>) Average values of the regions indicated in (c) and (d); (<b>f</b>) The cross section of the grooves; (<b>g</b>) The original C-scan image superimposed with the mask; (<b>h</b>) The reconstructed image superimposed with the mask.</p> Full article ">Figure 6
<p>Results of complex defect (unit in μm): (<b>a</b>) The topography of the defect; (<b>b</b>) Average profile of the branch; (<b>c</b>) Original C-scan image; (<b>d</b>) The reconstructed image formed by AMISR; (<b>e</b>) Average profile of the regions indicated in (c) and (d); (<b>f</b>) The cross section of the defect; (<b>g</b>) The original C-scan image superimposed with the mask; (<b>h</b>) The reconstructed image superimposed with the mask.</p> Full article ">
597 KiB  
Article
SNR Degradation in Undersampled Phase Measurement Systems
by David Salido-Monzú, Francisco J. Meca-Meca, Ernesto Martín-Gorostiza and José L. Lázaro-Galilea
Sensors 2016, 16(10), 1772; https://doi.org/10.3390/s16101772 - 24 Oct 2016
Cited by 10 | Viewed by 4821
Abstract
A wide range of measuring applications rely on phase estimation on sinusoidal signals. These systems, where the estimation is mainly implemented in the digital domain, can generally benefit from the use of undersampling to reduce the digitizer and subsequent digital processing requirements. This [...] Read more.
A wide range of measuring applications rely on phase estimation on sinusoidal signals. These systems, where the estimation is mainly implemented in the digital domain, can generally benefit from the use of undersampling to reduce the digitizer and subsequent digital processing requirements. This may be crucial when the application characteristics necessarily imply a simple and inexpensive sensor. However, practical limitations related to the phase stability of the band-pass filter prior digitization establish restrictions to the reduction of noise bandwidth. Due to this, the undersampling intensity is practically defined by noise aliasing, taking into account the amount of signal-to-noise ratio (SNR) reduction caused by it considering the application accuracy requirements. This work analyzes the relationship between undersampling frequency and SNR reduction, conditioned by the stability requirements of the filter that defines the noise bandwidth before digitization. The effect of undersampling is quantified in a practical situation where phase differences are measured by in-phase and quadrature (I/Q) demodulation for an infrared ranging application. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1
<p>General structure of the differential phase estimator.</p> Full article ">Figure 2
<p>Normalized single-sided power spectral density of noise and signal power before sampling.</p> Full article ">Figure 3
<p>Computation of the normalized noise power spectral distribution after digitization for different sampling frequencies <math display="inline"> <semantics> <mfenced separators="" open="(" close=")"> <msub> <mi>f</mi> <mi>s</mi> </msub> </mfenced> </semantics> </math>.</p> Full article ">Figure 4
<p>Computation of the normalized SNR after digitization as a function of sampling frequency <math display="inline"> <semantics> <mfenced separators="" open="(" close=")"> <msub> <mi>f</mi> <mi>s</mi> </msub> </mfenced> </semantics> </math>.</p> Full article ">Figure 5
<p>Phasemeter.</p> Full article ">Figure 6
<p>Differential distance estimation.</p> Full article ">Figure 7
<p>Output of phasemetters (<math display="inline"> <semantics> <msub> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">^</mo> </mover> <mn>1</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">^</mo> </mover> <mn>2</mn> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">^</mo> </mover> <mn>12</mn> </msub> </semantics> </math> (unwrapped)).</p> Full article ">Figure 8
<p>Alias frequency values <math display="inline"> <semantics> <mfenced separators="" open="(" close=")"> <msubsup> <mi>f</mi> <mi>M</mi> <msup> <mrow/> <mo>′</mo> </msup> </msubsup> </mfenced> </semantics> </math> as a function of sampling frequency <math display="inline"> <semantics> <mfenced separators="" open="(" close=")"> <msub> <mi>f</mi> <mi>s</mi> </msub> </mfenced> </semantics> </math>. Optimal working frequencies.</p> Full article ">Figure 9
<p>Phase typical error <math display="inline"> <semantics> <mfenced separators="" open="(" close=")"> <msub> <mi>σ</mi> <msub> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">^</mo> </mover> <mn>12</mn> </msub> </msub> </mfenced> </semantics> </math> for different noise equivalent bandwidths as a function of SNR of one digitized input signal (<math display="inline"> <semantics> <mrow> <msub> <mi>SNR</mi> <msub> <mn>1</mn> <mrow> <mi mathvariant="normal">A</mi> <mo>/</mo> <mi mathvariant="normal">D</mi> </mrow> </msub> </msub> <mo>=</mo> </mrow> </semantics> </math>[60 dB, 95 dB]) while the other input signal SNR is its complementary value (<math display="inline"> <semantics> <mrow> <msub> <mi>SNR</mi> <msub> <mn>2</mn> <mrow> <mi mathvariant="normal">A</mi> <mo>/</mo> <mi mathvariant="normal">D</mi> </mrow> </msub> </msub> <mo>=</mo> </mrow> </semantics> </math>[95 dB, 60 dB]). Theoretical (solid) and simulations (crosses).</p> Full article ">Figure 10
<p>Phase error <math display="inline"> <semantics> <mfenced open="(" close=")"> <mi>σ</mi> </mfenced> </semantics> </math> as a function of input SNR <math display="inline"> <semantics> <mrow> <mo>(</mo> <msub> <mi>SNR</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> </semantics> </math> and sampling frequency <math display="inline"> <semantics> <mfenced separators="" open="(" close=")"> <msub> <mi>f</mi> <mi>s</mi> </msub> </mfenced> </semantics> </math>. Estimated and measured results.</p> Full article ">Figure 11
<p>Setup for the real measurements.</p> Full article ">
2326 KiB  
Article
Development of an FBG Sensor Array for Multi-Impact Source Localization on CFRP Structures
by Mingshun Jiang, Yaozhang Sai, Xiangyi Geng, Qingmei Sui, Xiaohui Liu and Lei Jia
Sensors 2016, 16(10), 1770; https://doi.org/10.3390/s16101770 - 24 Oct 2016
Cited by 7 | Viewed by 4537
Abstract
We proposed and studied an impact detection system based on a fiber Bragg grating (FBG) sensor array and multiple signal classification (MUSIC) algorithm to determine the location and the number of low velocity impacts on a carbon fiber-reinforced polymer (CFRP) plate. A FBG [...] Read more.
We proposed and studied an impact detection system based on a fiber Bragg grating (FBG) sensor array and multiple signal classification (MUSIC) algorithm to determine the location and the number of low velocity impacts on a carbon fiber-reinforced polymer (CFRP) plate. A FBG linear array, consisting of seven FBG sensors, was used for detecting the ultrasonic signals from impacts. The edge-filter method was employed for signal demodulation. Shannon wavelet transform was used to extract narrow band signals from the impacts. The Gerschgorin disc theorem was used for estimating the number of impacts. We used the MUSIC algorithm to obtain the coordinates of multi-impacts. The impact detection system was tested on a 500 mm × 500 mm × 1.5 mm CFRP plate. The results show that the maximum error and average error of the multi-impacts’ localization are 9.2 mm and 7.4 mm, respectively. Full article
(This article belongs to the Section Physical Sensors)
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Figure 1

Figure 1
<p>Localization algorithm.</p> Full article ">Figure 2
<p>Measurement method of wave velocity.</p> Full article ">Figure 3
<p>Wave velocities measurement system.</p> Full article ">Figure 4
<p>Impact signals.</p> Full article ">Figure 5
<p>Frequency spectrum of S<sub>2</sub>.</p> Full article ">Figure 6
<p>Time difference between S<sub>2</sub> and S<sub>4</sub>.</p> Full article ">Figure 7
<p>Wave velocities of different directions.</p> Full article ">Figure 8
<p>Localization experiment.</p> Full article ">Figure 9
<p>Impact signals of FBG array.</p> Full article ">Figure 10
<p>Narrow band signals.</p> Full article ">Figure 11
<p>The localization spatial spectrum of (67,110) and (177,319).</p> Full article ">Figure 12
<p>The localization spatial spectrum of (−149,289) and (89,172).</p> Full article ">
5466 KiB  
Article
GNSS Spoofing Network Monitoring Based on Differential Pseudorange
by Zhenjun Zhang and Xingqun Zhan
Sensors 2016, 16(10), 1771; https://doi.org/10.3390/s16101771 - 23 Oct 2016
Cited by 19 | Viewed by 5886
Abstract
Spoofing is becoming a serious threat to various Global Navigation Satellite System (GNSS) applications, especially for those that require high reliability and security such as power grid synchronization and applications related to first responders and aviation safety. Most current works on anti-spoofing focus [...] Read more.
Spoofing is becoming a serious threat to various Global Navigation Satellite System (GNSS) applications, especially for those that require high reliability and security such as power grid synchronization and applications related to first responders and aviation safety. Most current works on anti-spoofing focus on spoofing detection from the individual receiver side, which identifies spoofing when it is under an attack. This paper proposes a novel spoofing network monitoring (SNM) mechanism aiming to reveal the presence of spoofing within an area. Consisting of several receivers and one central processing component, it keeps detecting spoofing even when the network is not attacked. The mechanism is based on the different time difference of arrival (TDOA) properties between spoofing and authentic signals. Normally, TDOAs of spoofing signals from a common spoofer are identical while those of authentic signals from diverse directions are dispersed. The TDOA is measured as the differential pseudorange to carrier frequency ratio (DPF). In a spoofing case, the DPFs include those of both authentic and spoofing signals, among which the DPFs of authentic are dispersed while those of spoofing are almost overlapped. An algorithm is proposed to search for the DPFs that are within a pre-defined small range, and an alarm will be raised if several DPFs are found within such range. The proposed SNM methodology is validated by simulations and a partial field trial. Results show 99.99% detection and 0.01% false alarm probabilities are achieved. The SNM has the potential to be adopted in various applications such as (1) alerting dedicated users when spoofing is occurring, which could significantly shorten the receiver side spoofing cost; (2) in combination with GNSS performance monitoring systems, such as the Continuous Operating Reference System (CORS) and GNSS Availability, Accuracy, Reliability anD Integrity Assessment for Timing and Navigation (GAARDIAN) System, to provide more reliable monitoring services. Full article
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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Figure 1

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<p>Spoofing network monitoring architecture.</p> Full article ">Figure 2
<p>Monitoring Receiver Architecture.</p> Full article ">Figure 3
<p>Spoofing Scenario.</p> Full article ">Figure 4
<p>The improper R results in poor monitoring performance.</p> Full article ">Figure 5
<p>LB detection probability versus the predefined range.</p> Full article ">Figure 6
<p>An example to illustrate the spoofing monitoring algorithm.</p> Full article ">Figure 7
<p>Simulation Setup.</p> Full article ">Figure 8
<p>The predefined range versus false alarm probability.</p> Full article ">Figure 9
<p>The Receiver Operating Characterization (ROC).</p> Full article ">Figure 10
<p>Signal collection: authetic and spoofing signals are collected seperately.</p> Full article ">Figure 11
<p>Signal process for spoofing monitoring.</p> Full article ">Figure 12
<p>The ideal experimental setup.</p> Full article ">Figure 13
<p>The DPFs over the test duration.</p> Full article ">Figure 14
<p>The DPFs for one epoch.</p> Full article ">Figure 15
<p>The DPFs are subtracted by their mean value in order to show the DPF range clearly.</p> Full article ">Figure 16
<p>The monitoring result: spoofing is detected because more than 4 DPFs are found within the predefined range.</p> Full article ">
4984 KiB  
Article
Underwater Communications for Video Surveillance Systems at 2.4 GHz
by Sandra Sendra, Jaime Lloret, Jose Miguel Jimenez and Joel J.P.C. Rodrigues
Sensors 2016, 16(10), 1769; https://doi.org/10.3390/s16101769 - 23 Oct 2016
Cited by 16 | Viewed by 7003
Abstract
Video surveillance is needed to control many activities performed in underwater environments. The use of wired media can be a problem since the material specially designed for underwater environments is very expensive. In order to transmit the images and videos wirelessly under water, [...] Read more.
Video surveillance is needed to control many activities performed in underwater environments. The use of wired media can be a problem since the material specially designed for underwater environments is very expensive. In order to transmit the images and videos wirelessly under water, three main technologies can be used: acoustic waves, which do not provide high bandwidth, optical signals, although the effect of light dispersion in water severely penalizes the transmitted signals and therefore, despite offering high transfer rates, the maximum distance is very small, and electromagnetic (EM) waves, which can provide enough bandwidth for video delivery. In the cases where the distance between transmitter and receiver is short, the use of EM waves would be an interesting option since they provide high enough data transfer rates to transmit videos with high resolution. This paper presents a practical study of the behavior of EM waves at 2.4 GHz in freshwater underwater environments. First, we discuss the minimum requirements of a network to allow video delivery. From these results, we measure the maximum distance between nodes and the round trip time (RTT) value depending on several parameters such as data transfer rate, signal modulations, working frequency, and water temperature. The results are statistically analyzed to determine their relation. Finally, the EM waves’ behavior is modeled by a set of equations. The results show that there are some combinations of working frequency, modulation, transfer rate and temperature that offer better results than others. Our work shows that short communication distances with high data transfer rates is feasible. Full article
(This article belongs to the Special Issue Underwater Sensor Nodes and Underwater Sensor Networks)
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<p>Test bench for video characterization.</p> Full article ">Figure 2
<p>Size of files in MB.</p> Full article ">Figure 3
<p>Bandwidth for videos at 30 fps.</p> Full article ">Figure 4
<p>Bandwidth for videos at 60 fps.</p> Full article ">Figure 5
<p>Maximum and average data transfer rate for each video at 30 fps and 60 fps.</p> Full article ">Figure 6
<p>Preliminary test to check the effect of air gap. (<b>a</b>) distance between antenna and water: 100 cm; (<b>b</b>) distance between antenna and water: 2.1 cm.</p> Full article ">Figure 7
<p>Swimming pool where measures have been taken.</p> Full article ">Figure 8
<p>Maximum distances for BPSK.</p> Full article ">Figure 9
<p>Maximum distances for QPSK.</p> Full article ">Figure 10
<p>Maximum distances for CCK.</p> Full article ">Figure 11
<p>Maximum distances for OFDM at 16 °C.</p> Full article ">Figure 12
<p>Maximum distances for OFDM at 18 °C.</p> Full article ">Figure 13
<p>Maximum distances for OFDM at 20 °C.</p> Full article ">Figure 14
<p>Maximum distances for OFDM at 22 °C.</p> Full article ">Figure 15
<p>Values of RTT in ms for 16 °C.</p> Full article ">Figure 16
<p>Measurements of RTT in ms for 18 °C.</p> Full article ">Figure 17
<p>Values of RTT in ms for 20 °C.</p> Full article ">Figure 18
<p>Values of RTT in ms for 22 °C.</p> Full article ">Figure 19
<p>Values of RTT in ms for 26 °C.</p> Full article ">Figure 20
<p>Estimated maximum distances for BPSK modulation.</p> Full article ">Figure 21
<p>Estimated maximum distances for QPSK modulation.</p> Full article ">Figure 22
<p>Estimated maximum distances for OFDM modulation.</p> Full article ">Figure 23
<p>Estimated maximum distances for CCK transmission scheme.</p> Full article ">
5933 KiB  
Article
An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services
by Yoonsik Bang, Jiyoung Kim and Kiyun Yu
Sensors 2016, 16(10), 1768; https://doi.org/10.3390/s16101768 - 22 Oct 2016
Cited by 22 | Viewed by 7430
Abstract
Wearable and smartphone technology innovations have propelled the growth of Pedestrian Navigation Services (PNS). PNS need a map-matching process to project a user’s locations onto maps. Many map-matching techniques have been developed for vehicle navigation services. These techniques are inappropriate for PNS because [...] Read more.
Wearable and smartphone technology innovations have propelled the growth of Pedestrian Navigation Services (PNS). PNS need a map-matching process to project a user’s locations onto maps. Many map-matching techniques have been developed for vehicle navigation services. These techniques are inappropriate for PNS because pedestrians move, stop, and turn in different ways compared to vehicles. In addition, the base map data for pedestrians are more complicated than for vehicles. This article proposes a new map-matching method for locating Global Positioning System (GPS) trajectories of pedestrians onto road network datasets. The theory underlying this approach is based on the Fréchet distance, one of the measures of geometric similarity between two curves. The Fréchet distance approach can provide reasonable matching results because two linear trajectories are parameterized with the time variable. Then we improved the method to be adaptive to the positional error of the GPS signal. We used an adaptation coefficient to adjust the search range for every input signal, based on the assumption of auto-correlation between consecutive GPS points. To reduce errors in matching, the reliability index was evaluated in real time for each match. To test the proposed map-matching method, we applied it to GPS trajectories of pedestrians and the road network data. We then assessed the performance by comparing the results with reference datasets. Our proposed method performed better with test data when compared to a conventional map-matching technique for vehicles. Full article
(This article belongs to the Section Physical Sensors)
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<p>Examples of GPS signals and network datasets from (<b>a</b>) vehicle and (<b>b</b>) Pedestrian Navigation Services. Solid lines are the background network datasets, and points are the recorded GPS signals.</p> Full article ">Figure 2
<p>An example of (<b>a</b>) two curves <math display="inline"> <semantics> <mi>f</mi> </semantics> </math> and <math display="inline"> <semantics> <mi>g</mi> </semantics> </math> and (<b>b</b>) their free-space diagram for distance threshold <math display="inline"> <semantics> <mi>ε</mi> </semantics> </math>. Two curves were parameterized with<math display="inline"> <semantics> <mrow> <mtext> </mtext> <mi>α</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mi>β</mi> </semantics> </math>. The thick arrowed lines are the part of <math display="inline"> <semantics> <mi>g</mi> </semantics> </math> within distance <math display="inline"> <semantics> <mi>ε</mi> </semantics> </math> from <math display="inline"> <semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mrow> <mn>0.5</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math>. There exists a path, depicted as a thick solid line, through the free space in the diagram [<a href="#B15-sensors-16-01768" class="html-bibr">15</a>].</p> Full article ">Figure 3
<p>An example of (<b>a</b>) a trajectory curve and a graph and (<b>b</b>) an extended free-space diagram between them. The shortest path (a thick dashed line in the diagram) becomes the matching result (from [<a href="#B19-sensors-16-01768" class="html-bibr">19</a>,<a href="#B20-sensors-16-01768" class="html-bibr">20</a>]).</p> Full article ">Figure 4
<p>The Fréchet distance approach may be affected by the positional error: (<b>a</b>) An ideal case: We can identify the set of matched roads with a small value of threshold <math display="inline"> <semantics> <mi>ε</mi> </semantics> </math>; (<b>b</b>) a realistic case: The positional error inflates at some parts and <math display="inline"> <semantics> <mi>ε</mi> </semantics> </math> becomes larger due to errors.</p> Full article ">Figure 5
<p>An example of (<b>a</b>) GPS signal and a road segment and (<b>b</b>) a free-space diagram from the example. For example, an element of the free space at the 5th GPS point is depicted as a bar corresponding to an overlapped section of the road segment and a search range of the point.</p> Full article ">Figure 6
<p>Determining search radii and relocating GPS points: (<b>a</b>) Search radius for the 2nd point is <math display="inline"> <semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> because <math display="inline"> <semantics> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&lt;</mo> <msub> <mi>D</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math>; (<b>b</b>) search radius for the 3rd point is <math display="inline"> <semantics> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> because <math display="inline"> <semantics> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>&gt;</mo> <msub> <mi>D</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math>. Map-matching results are identified to the geometric centers of the overlapped sections.</p> Full article ">Figure 7
<p>Relation between the Adaptation Ratio and the GPS point gap. If the Adaptation Coefficient <math display="inline"> <semantics> <mi>k</mi> </semantics> </math> equals 1, AR remains 1 regardless of the point gap. If <math display="inline"> <semantics> <mi>k</mi> </semantics> </math> equals 0, AR becomes 0 for all nonzero values of the point gap.</p> Full article ">Figure 8
<p>Determining search radius with adaptation when the positional error decreases <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <msub> <mi>D</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mi>R</mi> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>−</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mo stretchy="false">)</mo> </mrow> </semantics> </math>: (<b>a</b>) Assuming no adaptation (the basic method in <a href="#sec4dot1-sensors-16-01768" class="html-sec">Section 4.1</a>.), <math display="inline"> <semantics> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>R</mi> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>−</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mover accent="true"> <mrow> <mi>C</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> <mo stretchy="true">→</mo> </mover> <mo>=</mo> <mover accent="true"> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> <mo stretchy="true">→</mo> </mover> </mrow> </semantics> </math>, same as the <a href="#sensors-16-01768-f006" class="html-fig">Figure 6</a>; (<b>b</b>) Assuming adaptation with a certain coefficient (<math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>≤</mo> <mi>k</mi> <mo>≤</mo> <mn>1</mn> </mrow> </semantics> </math>), <math display="inline"> <semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>≤</mo> <mi>R</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mi>R</mi> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>−</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math>, which means that search radius shrinks and the search center is displaced by <math display="inline"> <semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>p</mi> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>−</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> <mo stretchy="true">→</mo> </mover> <mo>·</mo> <msup> <mi>k</mi> <mrow> <mi>I</mi> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> <mo>−</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> </msup> </mrow> </semantics> </math>.</p> Full article ">Figure 9
<p>Visual explanations of reliability of a map-matching result: (<b>a</b>) unreliable match (<math display="inline"> <semantics> <mrow> <mi>cos</mi> <mi>θ</mi> </mrow> </semantics> </math> is close to or less than 0); (<b>b</b>) reliable match (<math display="inline"> <semantics> <mrow> <mi>cos</mi> <mi>θ</mi> </mrow> </semantics> </math> is close to 1) for the 2nd point compared with the matching result of the previous point.</p> Full article ">Figure 10
<p>Six planned routes (thick parts from the background PND) and collected GPS trajectories along the routes (small circles).</p> Full article ">Figure 11
<p>A part of the ground truth dataset (<b>a</b>) and its matching table (<b>b</b>), identified manually for selected feature points from Route 6. Thick lines are the PND links and circles are the GPS-recorded points. Lines connecting points and PND are the reference matches of the ground truth dataset. In this example, points between P_246 and P_253 should be matched to PND link L_98 and the direction of the link is opposite to that of the GPS signal.</p> Full article ">Figure 12
<p>The ROC curve from the training dataset for the Fréchet-distance-based method. The relationship between TPR and FPR is depicted and the optimal cutoff on the curve is determined to be the point (0.3557, 0.9140).</p> Full article ">Figure 13
<p>A visual explanation for calculating APE: (<b>a</b>) The ground truth tells us that FP(2) should be map-matched to G(2) and there are 12 points before and nine points after the FP(2). Then <math display="inline"> <semantics> <mrow> <mi>W</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> is calculated as <math display="inline"> <semantics> <mrow> <mfrac> <mrow> <mn>12</mn> <mo>+</mo> <mn>9</mn> </mrow> <mrow> <mn>2</mn> <mo>×</mo> <mn>300</mn> </mrow> </mfrac> <mo>=</mo> <mn>0.035</mn> </mrow> </semantics> </math>. (<b>b</b>) The map-matching result of FP(2) derived by the map-matching method is different from G(2). A positional error of FP(2), or <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mi>M</mi> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math> is then standardized by dividing it by <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics> </math>, that is, <math display="inline"> <semantics> <mrow> <mfrac> <mrow> <msub> <mi>d</mi> <mi>M</mi> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>d</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>=</mo> <mfrac> <mn>6</mn> <mn>8</mn> </mfrac> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 14
<p>Some parts of the map-matching results obtained by the adaptive Fréchet-distance-based method. Thick lines are PND segments and circles are GPS-recorded points. Lines connecting points and PND segments are the matching relations from the results.</p> Full article ">Figure 15
<p>Comparison of performances of three map-matching methods–Conventional, Basic, and Adaptive Fréchet-distance-based method: (<b>a</b>) Comparison by RCM; (<b>b</b>) comparison by APE.</p> Full article ">Figure 16
<p>Comparison of results from (<b>a</b>) conventional method, (<b>b</b>) basic Fréchet-distance-based method, and (<b>c</b>) adaptive Fréchet-distance-based method applied to Trajectory 4. Thick lines are PND segments and circles are GPS-recorded points. Lines connecting points and PND segments are the reference matches of the ground truth dataset. Applying the Fréchet-distance-based method reduced the error and increased the number of correct matches.</p> Full article ">
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Article
A Nanoporous Alumina Membrane Based Electrochemical Biosensor for Histamine Determination with Biofunctionalized Magnetic Nanoparticles Concentration and Signal Amplification
by Weiwei Ye, Yifan Xu, Lihao Zheng, Yu Zhang, Mo Yang and Peilong Sun
Sensors 2016, 16(10), 1767; https://doi.org/10.3390/s16101767 - 22 Oct 2016
Cited by 39 | Viewed by 7460
Abstract
Histamine is an indicator of food quality and indispensable in the efficient functioning of various physiological systems. Rapid and sensitive determination of histamine is urgently needed in food analysis and clinical diagnostics. Traditional histamine detection methods require qualified personnel, need complex operation processes, [...] Read more.
Histamine is an indicator of food quality and indispensable in the efficient functioning of various physiological systems. Rapid and sensitive determination of histamine is urgently needed in food analysis and clinical diagnostics. Traditional histamine detection methods require qualified personnel, need complex operation processes, and are time-consuming. In this study, a biofunctionalized nanoporous alumina membrane based electrochemical biosensor with magnetic nanoparticles (MNPs) concentration and signal amplification was developed for histamine determination. Nanoporous alumina membranes were modified by anti-histamine antibody and integrated into polydimethylsiloxane (PDMS) chambers. The specific antibody modified MNPs were used to concentrate histamine from samples and transferred to the antibody modified nanoporous membrane. The MNPs conjugated to histamine were captured in the nanopores via specific reaction between histamine and anti-histamine antibody, resulting in a blocking effect that was amplified by MNPs in the nanopores. The blockage signals could be measured by electrochemical impedance spectroscopy across the nanoporous alumina membrane. The sensing platform had great sensitivity and the limit of detection (LOD) reached as low as 3 nM. This biosensor could be successfully applied for histamine determination in saury that was stored in frozen conditions for different hours, presenting a potentially novel, sensitive, and specific sensing system for food quality assessment and safety support. Full article
(This article belongs to the Special Issue Nanobiosensors in Food Industry)
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<p>Scheme for surface modification of nanoporous alumina membrane by (3-glycidoxypropyl) trimethoxysilane (GPMS) and conjugation with anti-histamine antibody.</p> Full article ">Figure 2
<p>Schematic diagram of biofunctionalization of magnetic nanoparticles (MNPs) for histamine concentration to nanoporous alumina membrane (<b>a</b>) with electrochemical biosensing system for detection (<b>b</b>). EDC, <span class="html-italic">N</span>-(3-Dimethylaminopropyl)-<span class="html-italic">N</span>-ethylcarbodiimide hydrochloride; NHS, <span class="html-italic">N</span>-Hydroxysuccinimide; BSA, bovine serum albumin; PDMS, polydimethylsiloxane.</p> Full article ">Figure 3
<p>Transmission electron microscopy (TEM) images of magnetic nanoparticles (MNPs) (<b>a</b>) and antibody conjugated MNPs (<b>b</b>); scanning electron microscopy (SEM) images of cross-sectional view of bare nanoporous alumina membranes (<b>c</b>) and antibody functionalized nanoporous alumina membranes after histamine-MNPs conjugation capture (<b>d</b>).</p> Full article ">Figure 4
<p>(<b>a</b>) Impedance spectra and (<b>b</b>) impedance change rate of various histamine concentrations without magnetic nanoparticles (MNPs).</p> Full article ">Figure 5
<p>Impedance spectra of various histamine concentrations with MNPs in a nanoporous alumina membrane.</p> Full article ">Figure 6
<p>(<b>a</b>) Linear relationship between impedance change rate versus histamine concentrations from 1 μM to 100 mM; (<b>b</b>) Impedance change with histamine concentrations from 5 nM to 10 μM.</p> Full article ">Figure 7
<p>Comparison of impedance change rate of tryptamine and histamine detection.</p> Full article ">
5264 KiB  
Article
Plantar Pressure Detection with Fiber Bragg Gratings Sensing System
by Tsair-Chun Liang, Jhe-Jhun Lin and Lan-Yuen Guo
Sensors 2016, 16(10), 1766; https://doi.org/10.3390/s16101766 - 22 Oct 2016
Cited by 36 | Viewed by 9805
Abstract
In this paper, a novel fiber-optic sensing system based on fiber Bragg gratings (FBGs) to measure foot plantar pressure is proposed. This study first explores the Pedar-X insole foot pressure types of the adult-size chart and then defines six measurement areas to effectively [...] Read more.
In this paper, a novel fiber-optic sensing system based on fiber Bragg gratings (FBGs) to measure foot plantar pressure is proposed. This study first explores the Pedar-X insole foot pressure types of the adult-size chart and then defines six measurement areas to effectively identify four foot types: neutral foot, cavus foot, supinated foot and flat foot. The plantar pressure signals are detected by only six FBGs, which are embedded in silicone rubber. The performance of the fiber optic sensing is examined and compared with a digital pressure plate of i-Step P1000 with 1024 barometric sensors. In the experiment, there are 11 participants with different foot types to participate in the test. The Pearson correlation coefficient, which is determined from the measured results of the homemade fiber-optic plantar pressure system and i-Step P1000 plantar pressure plate, reaches up to 0.671 (p < 0.01). According to the measured results from the plantar pressure data, the proposed fiber optic sensing system can successfully identify the four different foot types. Measurements of this study have demonstrated the feasibility of the proposed system so that it can be an alternative for plantar pressure detection systems. Full article
(This article belongs to the Special Issue Applications of Advanced Materials on Microelectronic and Optical Sensors)
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<p>Schematic illustration of foot pressure distribution of (<b>a</b>) flat foot; (<b>b</b>) neutral foot; (<b>c</b>) cavus foot; and (<b>d</b>) supinated foot of left footprint when standing.</p> Full article ">Figure 2
<p>The configuration of the fiber sensing system for plantar pressure measurement.</p> Full article ">Figure 3
<p>The reflection wavelength spectrum of six FBGs without any stress.</p> Full article ">Figure 4
<p>Foot plantar pressure distribution of participants: (<b>a</b>) No. 2; (<b>b</b>) No. 6; (<b>c</b>) No. 8; (<b>d</b>) No. 10; (<b>e</b>) No. 11, by i-Step P1000 digital pressure plate.</p> Full article ">Figure 4 Cont.
<p>Foot plantar pressure distribution of participants: (<b>a</b>) No. 2; (<b>b</b>) No. 6; (<b>c</b>) No. 8; (<b>d</b>) No. 10; (<b>e</b>) No. 11, by i-Step P1000 digital pressure plate.</p> Full article ">Figure 5
<p>The six sensing area definitions in this research.</p> Full article ">Figure 6
<p>Set-up of FBG sensors and test in action.</p> Full article ">Figure 7
<p>The plantar pressure distribution of the neutral feet of participant No. 2: (<b>a</b>) Footprint; (<b>b</b>) central wavelength shift as a function of sensing area number.</p> Full article ">Figure 8
<p>The plantar pressure distribution of the cavus feet of participant No. 6: (<b>a</b>) Footprint; (<b>b</b>) central wavelength shift as a function of sensing area number.</p> Full article ">Figure 9
<p>The plantar pressure distribution of the flat feet of participant No. 8: (<b>a</b>) Footprint; (<b>b</b>) central wavelength shift as a function of sensing area number.</p> Full article ">Figure 10
<p>The plantar pressure distribution of supinated feet of participant No. 10: (<b>a</b>) Footprint; (<b>b</b>) central wavelength shift as a function of sensing area number.</p> Full article ">Figure 11
<p>The plantar pressure distribution of participant No. 11, whose left foot is cavus and right foot is neutral: (<b>a</b>) Footprint; (<b>b</b>) central wavelength shift as a function of sensing area number.</p> Full article ">Figure 12
<p>The central wavelength shift as a function of sensing area number for plantar pressure distribution of left and right foot: (<b>a</b>) Flat feet; (<b>b</b>) supinated feet; (<b>c</b>) cavus feet; and (<b>d</b>) neutral feet.</p> Full article ">Figure 13
<p>The central wavelength shift of six FBGs corresponding to the plantar pressure value of i-Step P1000.</p> Full article ">
6345 KiB  
Article
Experimental Study on Damage Detection in Timber Specimens Based on an Electromechanical Impedance Technique and RMSD-Based Mahalanobis Distance
by Dansheng Wang, Qinghua Wang, Hao Wang and Hongping Zhu
Sensors 2016, 16(10), 1765; https://doi.org/10.3390/s16101765 - 22 Oct 2016
Cited by 53 | Viewed by 5139
Abstract
In the electromechanical impedance (EMI) method, the PZT patch performs the functions of both sensor and exciter. Due to the high frequency actuation and non-model based characteristics, the EMI method can be utilized to detect incipient structural damage. In recent years EMI techniques [...] Read more.
In the electromechanical impedance (EMI) method, the PZT patch performs the functions of both sensor and exciter. Due to the high frequency actuation and non-model based characteristics, the EMI method can be utilized to detect incipient structural damage. In recent years EMI techniques have been widely applied to monitor the health status of concrete and steel materials, however, studies on application to timber are limited. This paper will explore the feasibility of using the EMI technique for damage detection in timber specimens. In addition, the conventional damage index, namely root mean square deviation (RMSD) is employed to evaluate the level of damage. On that basis, a new damage index, Mahalanobis distance based on RMSD, is proposed to evaluate the damage severity of timber specimens. Experimental studies are implemented to detect notch and hole damage in the timber specimens. Experimental results verify the availability and robustness of the proposed damage index and its superiority over the RMSD indexes. Full article
(This article belongs to the Section Physical Sensors)
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<p>Configuration of the PZT patches.</p> Full article ">Figure 2
<p>Sketch of timber specimens in group A.</p> Full article ">Figure 3
<p>Timber specimens in group A (<b>a</b>) Pinus sylvestris and (<b>b</b>) Bangkirai.</p> Full article ">Figure 4
<p>Sketch of timber specimens in group B.</p> Full article ">Figure 5
<p>Timber specimens in group B (<b>a</b>) Pinus sylvestris and (<b>b</b>) Bangkirai.</p> Full article ">Figure 6
<p>Sketch of timber specimens in group C.</p> Full article ">Figure 7
<p>Timber specimens in group C (<b>a</b>) Pinus Sylvestris and (<b>b</b>) Bangkirai.</p> Full article ">Figure 8
<p>The flow chart of the experiments.</p> Full article ">Figure 9
<p>Real admittance for A1a in different cases (<b>a</b>) 40 Hz–30 kHz; (<b>b</b>) 30–50 kHz; (<b>c</b>) 50–150 kHz; and (<b>d</b>) 150–500 kHz.</p> Full article ">Figure 10
<p>Real admittances for B1a in different cases (<b>a</b>) 40 Hz–30 kHz; (<b>b</b>) 30–50 kHz; (<b>c</b>) 50–150 kHz; and (<b>d</b>) 150–500 kHz.</p> Full article ">Figure 11
<p>Real admittances for C1a in different cases (<b>a</b>) 40 Hz–30 kHz; (<b>b</b>) 30–50 kHz; (<b>c</b>) 50–150 kHz; and (<b>d</b>) 150–500 kHz.</p> Full article ">Figure 12
<p>RMSD values for A1a in different cases (<b>a</b>) 40 Hz–30 kHz; (<b>b</b>) 30–50 kHz; (<b>c</b>) 50–150 kHz; and (<b>d</b>) 150–500 kHz.</p> Full article ">Figure 12 Cont.
<p>RMSD values for A1a in different cases (<b>a</b>) 40 Hz–30 kHz; (<b>b</b>) 30–50 kHz; (<b>c</b>) 50–150 kHz; and (<b>d</b>) 150–500 kHz.</p> Full article ">Figure 13
<p>RMSD values for B1a in different cases (<b>a</b>) 40 Hz–30 kHz; (<b>b</b>) 30–50 kHz; (<b>c</b>) 50–150 kHz; and (<b>d</b>) 150–500 kHz.</p> Full article ">Figure 14
<p>RMSD values for C1a in different cases (<b>a</b>) 40 Hz–30 kHz; (<b>b</b>) 30–50 kHz; (<b>c</b>) 50–150 kHz; and (<b>d</b>) 150–500 kHz.</p> Full article ">Figure 14 Cont.
<p>RMSD values for C1a in different cases (<b>a</b>) 40 Hz–30 kHz; (<b>b</b>) 30–50 kHz; (<b>c</b>) 50–150 kHz; and (<b>d</b>) 150–500 kHz.</p> Full article ">Figure 15
<p>MD values based on RMSD for (<b>a</b>) A1a; (<b>b</b>) B1a and (<b>c</b>) C1a.</p> Full article ">Figure 16
<p>MD values based on RMSD for (<b>a</b>) A2a; (<b>b</b>) B2a and (<b>c</b>) C2a.</p> Full article ">Figure 16 Cont.
<p>MD values based on RMSD for (<b>a</b>) A2a; (<b>b</b>) B2a and (<b>c</b>) C2a.</p> Full article ">Figure 17
<p>Sketch of timber specimen for the sensitivity verification experiment.</p> Full article ">Figure 18
<p>Timber specimen for the sensitivity verification experiment.</p> Full article ">Figure 19
<p>Real admittances for all cases in the frequency range of 30–50 kHz (<b>a</b>) PZT1; (<b>b</b>) PZT2; and (<b>c</b>) PZT3.</p> Full article ">Figure 20
<p>RMSD values for all cases in the frequency range of 30–50 kHz (<b>a</b>) PZT1; (<b>b</b>) PZT2; and (<b>c</b>) PZT3.</p> Full article ">Figure 21
<p>MD values based on RMSD for (<b>a</b>) PZT1; (<b>b</b>) PZT2 and (<b>c</b>) PZT3.</p> Full article ">
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Article
In-Field, In Situ, and In Vivo 3-Dimensional Elemental Mapping for Plant Tissue and Soil Analysis Using Laser-Induced Breakdown Spectroscopy
by Chunjiang Zhao, Daming Dong, Xiaofan Du and Wengang Zheng
Sensors 2016, 16(10), 1764; https://doi.org/10.3390/s16101764 - 22 Oct 2016
Cited by 19 | Viewed by 6637
Abstract
Sensing and mapping element distributions in plant tissues and its growth environment has great significance for understanding the uptake, transport, and accumulation of nutrients and harmful elements in plants, as well as for understanding interactions between plants and the environment. In this study, [...] Read more.
Sensing and mapping element distributions in plant tissues and its growth environment has great significance for understanding the uptake, transport, and accumulation of nutrients and harmful elements in plants, as well as for understanding interactions between plants and the environment. In this study, we developed a 3-dimensional elemental mapping system based on laser-induced breakdown spectroscopy that can be deployed in- field to directly measure the distribution of multiple elements in living plants as well as in the soil. Mapping is performed by a fast scanning laser, which ablates a micro volume of a sample to form a plasma. The presence and concentration of specific elements are calculated using the atomic, ionic, and molecular spectral characteristics of the plasma emission spectra. Furthermore, we mapped the pesticide residues in maize leaves after spraying to demonstrate the capacity of this method for trace elemental mapping. We also used the system to quantitatively detect the element concentrations in soil, which can be used to further understand the element transport between plants and soil. We demonstrate that this method has great potential for elemental mapping in plant tissues and soil with the advantages of 3-dimensional and multi-elemental mapping, in situ and in vivo measurement, flexible use, and low cost. Full article
(This article belongs to the Special Issue Sensors for Environmental Monitoring 2016)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic of the LipsImag apparatus and its application for in situ and in vivo elemental mapping of maize.</p> Full article ">Figure 2
<p>The photographs in a real application of LipsImag for an in-field measurement. (<b>a</b>) Maize plants for the elemental mapping experiment; (<b>b</b>) Photograph of a maize leaf after a 20 × 20 scanning by LipsImag; (<b>c</b>) The microstructure of a single shot on a maize leaf by LipsImag; (<b>d</b>) A plasma emission spectra from the scanning that is shown in (<b>b</b>).</p> Full article ">Figure 3
<p>The mapping results of holly (Ilex chinensis Sims) leaf based on the spectral intensities of specific elements using LipsImag. (<b>a</b>) The visible image of the leaf for the mapping experiment; (<b>b</b>) and (<b>c</b>) shows the distributions of the spectral intensities of Mg and K, respectively; (<b>d</b>,<b>e</b>) illustrate the spectral characteristics of Mg and K that were used for mapping.</p> Full article ">Figure 4
<p>The laser-induced plasma spectral characteristics of chlorpyrifos in a leaf. (<b>a</b>) The spectral characteristics of the element P at 213.62 nm and 214.91 nm; (<b>b</b>) The spectral characteristics of the element P at 253.56 nm and 255.33 nm; (<b>c</b>) The spectral characteristics of the element Cl at 827.59 nm; (<b>d</b>) The multi-variable linear regression model for chlorpyrifos measurement based on the spectral characteristics of P and Cl.</p> Full article ">Figure 5
<p>The 2D mapping results of chlorpyrifos on a leaf surface using LipsImag. (<b>a</b>) The mapping result of pesticide on a leaf that used 100 mL chlorpyrifos EC (40.7%) diluted by 50 L water and was subsequently sprayed in a 1 mu (666.7 m<sup>2</sup>) maize field; (<b>b</b>) The mapping result of a lower concentration of pesticide that used 10 mL chlorpyrifos; (<b>c</b>) The mapping result of the leaf 10 h after spray application that used 100 mL chlorpyrifos EC. The left image of (<b>a</b>–<b>c</b>) shows the visible image observed by microscopy, while the right image is the scanning results that show the distribution of the pesticide.</p> Full article ">Figure 6
<p>The 3-dimensional mapping of pesticide residues in a maize leaf 10 h after spray. (<b>a</b>) is the visible image of the scanning area observed by microscopy; (<b>b</b>) is a cross-sectional image that shows the pesticide distribution in a lateral plane; (<b>c</b>) shows the 3-dimensional image of the pesticide by scanning one plane after another, with a thickness of 12 µm for each plane.</p> Full article ">Figure 7
<p>Determination of K in soil using LipsImag. (<b>a</b>) shows the spectral characteristics of K in soil; (<b>b</b>) is the calibration model for K concentration measurement.</p> Full article ">
3772 KiB  
Article
A Portable Farmland Information Collection System with Multiple Sensors
by Jianfeng Zhang, Jinyang Hu, Lvwen Huang, Zhiyong Zhang and Yimian Ma
Sensors 2016, 16(10), 1762; https://doi.org/10.3390/s16101762 - 22 Oct 2016
Cited by 16 | Viewed by 7191
Abstract
Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture—efficient use of agricultural resources, and improving the crop yields [...] Read more.
Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture—efficient use of agricultural resources, and improving the crop yields and quality—some necessary field information in crop growth environment needs to be collected and monitored. In this paper, a farmland information collection system is developed, which includes a portable farmland information collection device based on STM32 (a 32-bit comprehensive range of microcontrollers based on ARM Crotex-M3), a remote server and a mobile phone APP. The device realizes the function of portable and mobile collecting of multiple parameters farmland information, such as chlorophyll content of crop leaves, air temperature, air humidity, and light intensity. UM220-III (Unicore Communication Inc., Beijing, China) is used to realize the positioning based on BDS/GPS (BeiDou Navigation Satellite System, BDS/Global Positioning System, GPS) dual-mode navigation and positioning system, and the CDMA (Code Division Multiple Access, CDMA) wireless communication module is adopted to realize the real-time remote transmission. The portable multi-function farmland information collection system is real-time, accurate, and easy to use to collect farmland information and multiple information parameters of crops. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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<p>Scheme of the farmland information collection system.</p> Full article ">Figure 2
<p>Block diagram of the farmland information collection device.</p> Full article ">Figure 3
<p>Operation flowchart of the farmland information collection device.</p> Full article ">Figure 4
<p>Program flowchart of Chlorophyll measurement module.</p> Full article ">Figure 5
<p>Parsing flowchart of obtaining real-time location information, $ is the beginning character of all the messages, RMC provides the minimum recommended data message, and GSV describes the visible satellite messages.</p> Full article ">Figure 6
<p>Block diagram of the power supply system.</p> Full article ">Figure 7
<p>The experiment instrument of the farmland information collection device.</p> Full article ">Figure 8
<p>Farmland experiment.</p> Full article ">Figure 9
<p>Sample of farmland information—on 3 May 2016, at 15:13—including with the geographic coordinates by Lon (longitude, degree) and Lat (latitude, degree), Chlorophyll (chlorophyll relative content, SPAD), Environment information by the Temp (air temperature, °C), RH (air humidity, %), Light (light intensity, Lx), and CO<sub>2</sub> (CO<sub>2</sub> concentration, ppm) and Soil information by the Temp (soil temperature, °C), RH (soil humidity, %), and Elec con (soil electrical conductivity, µs/cm).</p> Full article ">Figure 10
<p>Interface of mobile phone APP in Chinese.</p> Full article ">Figure 11
<p>Fifty air temperature (°C) experimental results by MGT2.0 (marked by <b>blue</b> line) and AR837 (marked by <b>red</b> line).</p> Full article ">Figure 12
<p>FIfty air humidity (%RH) experimental results by MGT2.0 (marked by <b>blue</b> line) and AR837 (marked by <b>red</b> line).</p> Full article ">Figure 13
<p>Fifty light intensity (Lx) experimental results by MGT2.0 (marked by <b>blue</b> line) and DT-1308 (marked by <b>red</b> line).</p> Full article ">Figure 14
<p>Fifty chlorophyll relative content (SPAD) experimental results by MGT2.0 (marked by <b>blue</b> line) and SPAD-502 (marked by <b>red</b> line).</p> Full article ">
8538 KiB  
Article
A Comparative Study of the Application of Fluorescence Excitation-Emission Matrices Combined with Parallel Factor Analysis and Nonnegative Matrix Factorization in the Analysis of Zn Complexation by Humic Acids
by Patrycja Boguta, Piotr M. Pieczywek and Zofia Sokołowska
Sensors 2016, 16(10), 1760; https://doi.org/10.3390/s16101760 - 22 Oct 2016
Cited by 20 | Viewed by 6268
Abstract
The main aim of this study was the application of excitation-emission fluorescence matrices (EEMs) combined with two decomposition methods: parallel factor analysis (PARAFAC) and nonnegative matrix factorization (NMF) to study the interaction mechanisms between humic acids (HAs) and Zn(II) over a wide concentration [...] Read more.
The main aim of this study was the application of excitation-emission fluorescence matrices (EEMs) combined with two decomposition methods: parallel factor analysis (PARAFAC) and nonnegative matrix factorization (NMF) to study the interaction mechanisms between humic acids (HAs) and Zn(II) over a wide concentration range (0–50 mg·dm−3). The influence of HA properties on Zn(II) complexation was also investigated. Stability constants, quenching degree and complexation capacity were estimated for binding sites found in raw EEM, EEM-PARAFAC and EEM-NMF data using mathematical models. A combination of EEM fluorescence analysis with one of the proposed decomposition methods enabled separation of overlapping binding sites and yielded more accurate calculations of the binding parameters. PARAFAC and NMF processing allowed finding binding sites invisible in a few raw EEM datasets as well as finding totally new maxima attributed to structures of the lowest humification. Decomposed data showed an increase in Zn complexation with an increase in humification, aromaticity and molecular weight of HAs. EEM-PARAFAC analysis also revealed that the most stable compounds were formed by structures containing the highest amounts of nitrogen. The content of oxygen-functional groups did not influence the binding parameters, mainly due to fact of higher competition of metal cation with protons. EEM spectra coupled with NMF and especially PARAFAC processing gave more adequate assessments of interactions as compared to raw EEM data and should be especially recommended for modeling of complexation processes where the fluorescence intensities (FI) changes are weak or where the processes are interfered with by the presence of other fluorophores. Full article
(This article belongs to the Section Chemical Sensors)
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<p>Results of data pre-processing: (<b>a</b>) raw EEM map; (<b>b</b>) binary mask with Rayleigh peaks marked on white; (<b>c</b>) final, processed data.</p> Full article ">Figure 2
<p>Example results of raw fluorescence data decomposed into two separate components by means of PARAFAC and NMF algorithms.</p> Full article ">Figure 3
<p>EEM-RAW fluorescence matrices of the studied HAs samples: without Zn (column A) and with example Zn concentrations (columns B, C and D).</p> Full article ">Figure 4
<p>EEM-PARAFAC fluorescence matrices of HA1 and HA4 samples: Without Zn (column A) and with exemplary Zn concentrations: 5, 20 and 50 mg·dm<sup>−3</sup> (columns B, C and D). For each HA: upper plot: Component 1, lower: Component 2.</p> Full article ">Figure 5
<p>EEM-NMF fluorescence matrices of HA1 and HA4 samples: without Zn (column A) and with exemplary Zn concentrations: 5, 20 and 50 mg·dm<sup>−3</sup> (columns B, C and D). For each HA: Upper plot: Component 1, lower: Component 2.</p> Full article ">Figure 6
<p>Quenching profiles of HAs fluorescence at α, β, γ and ω maxima of RAW, PARAFAC and NMF data under increasing Zn concentrations. Symbols are denoted for experimental points, solid lines—for model curves.</p> Full article ">
7853 KiB  
Article
Achievement of High-Response Organic Field-Effect Transistor NO2 Sensor by Using the Synergistic Effect of ZnO/PMMA Hybrid Dielectric and CuPc/Pentacene Heterojunction
by Shijiao Han, Jiang Cheng, Huidong Fan, Junsheng Yu and Lu Li
Sensors 2016, 16(10), 1763; https://doi.org/10.3390/s16101763 - 21 Oct 2016
Cited by 15 | Viewed by 7825
Abstract
High-response organic field-effect transistor (OFET)-based NO2 sensors were fabricated using the synergistic effect the synergistic effect of zinc oxide/poly(methyl methacrylate) (ZnO/PMMA) hybrid dielectric and CuPc/Pentacene heterojunction. Compared with the OFET sensors without synergistic effect, the fabricated OFET sensors showed a remarkable shift [...] Read more.
High-response organic field-effect transistor (OFET)-based NO2 sensors were fabricated using the synergistic effect the synergistic effect of zinc oxide/poly(methyl methacrylate) (ZnO/PMMA) hybrid dielectric and CuPc/Pentacene heterojunction. Compared with the OFET sensors without synergistic effect, the fabricated OFET sensors showed a remarkable shift of saturation current, field-effect mobility and threshold voltage when exposed to various concentrations of NO2 analyte. Moreover, after being stored in atmosphere for 30 days, the variation of saturation current increased more than 10 folds at 0.5 ppm NO2. By analyzing the electrical characteristics, and the morphologies of organic semiconductor films of the OFET-based sensors, the performance enhancement was ascribed to the synergistic effect of the dielectric and organic semiconductor. The ZnO nanoparticles on PMMA dielectric surface decreased the grain size of pentacene formed on hybrid dielectric, facilitating the diffusion of CuPc molecules into the grain boundary of pentacene and the approach towards the conducting channel of OFET. Hence, NO2 molecules could interact with CuPc and ZnO nanoparticles at the interface of dielectric and organic semiconductor. Our results provided a promising strategy for the design of high performance OFET-based NO2 sensors in future electronic nose and environment monitoring. Full article
(This article belongs to the Special Issue Gas Nanosensors)
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Full article ">Figure 1
<p>Molecular structures of the pentacene, CuPc and PMMA, along with the organic field-effect transistor (OFET)-based sensor device configurations in this study, device A with only CuPc/Pentacene heterojunction; device B with both ZnO/PMMA hybrid dielectric and CuPc/Pentacene heterojunction.</p> Full article ">Figure 2
<p>(<b>a</b>,<b>b</b>) Typical transfer curve I<sub>DS</sub>-V<sub>G</sub>, and (<b>c</b>,<b>d</b>) output curve I<sub>DS</sub>-V<sub>D</sub> of devices A and B, respectively.</p> Full article ">Figure 3
<p>Transfer curves of devices A and B under a specific concentration of NO<sub>2</sub>, (<b>a</b>,<b>d</b>) without calculation, (<b>b</b>,<b>e</b>) after taking log, (<b>c</b>,<b>f</b>) after extracting.</p> Full article ">Figure 4
<p>Percentage variation of I<sub>on</sub> (<b>a</b>), μ (<b>b</b>), V<sub>T</sub> (<b>c</b>) and SS (<b>d</b>) of all the devices at different NO<sub>2</sub> concentrations, respectively.</p> Full article ">Figure 5
<p>Atomic force microscopy (AFM) topography images of the pentacene films on pure PMMA dielectric (<b>a</b>); ZnO/PMMA hybrid dielectrics (<b>b</b>) and CuPc film on it (<b>c</b>).</p> Full article ">Figure 6
<p>Schematic illustration of ZnO/PMMA hybrid dielectric and CuPc/pentacene heterojunction under NO<sub>2</sub>.</p> Full article ">Figure 7
<p>Real-time response curve of this OFET sensor based on ZnO/PMMA hybrid dielectric and CuPc/pentacene heterojunction to different NO<sub>2</sub> pluses.</p> Full article ">Figure 8
<p>Output curve of devices B (<b>a</b>) and percentage variation of I<sub>on</sub> (<b>b</b>) under a specific concentration of NO<sub>2</sub> after stored under ambient for 30 days.</p> Full article ">Figure 9
<p>Transfer curves of devices B (<b>a</b>,<b>b</b>) and percentage variations of I<sub>on</sub> (<b>c</b>) under a specific concentration of SO<sub>2</sub>.</p> Full article ">
2723 KiB  
Review
Printable Electrochemical Biosensors: A Focus on Screen-Printed Electrodes and Their Application
by Keiichiro Yamanaka, Mun’delanji C. Vestergaard and Eiichi Tamiya
Sensors 2016, 16(10), 1761; https://doi.org/10.3390/s16101761 - 21 Oct 2016
Cited by 132 | Viewed by 12546
Abstract
In this review we present electrochemical biosensor developments, focusing on screen-printed electrodes (SPEs) and their applications. In particular, we discuss how SPEs enable simple integration, and the portability needed for on-field applications. First, we briefly discuss the general concept of biosensors and quickly [...] Read more.
In this review we present electrochemical biosensor developments, focusing on screen-printed electrodes (SPEs) and their applications. In particular, we discuss how SPEs enable simple integration, and the portability needed for on-field applications. First, we briefly discuss the general concept of biosensors and quickly move on to electrochemical biosensors. Drawing from research undertaken in this area, we cover the development of electrochemical DNA biosensors in great detail. Through specific examples, we describe the fabrication and surface modification of printed electrodes for sensitive and selective detection of targeted DNA sequences, as well as integration with reverse transcription-polymerase chain reaction (RT-PCR). For a more rounded approach, we also touch on electrochemical immunosensors and enzyme-based biosensors. Last, we present some electrochemical devices specifically developed for use with SPEs, including USB-powered compact mini potentiostat. The coupling demonstrates the practical use of printable electrode technologies for application at point-of-use. Although tremendous advances have indeed been made in this area, a few challenges remain. One of the main challenges is application of these technologies for on-field analysis, which involves complicated sample matrices. Full article
(This article belongs to the Special Issue Point-of-Care Biosensors)
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<p>Cyclic voltamograms of various DNA intercalators [<a href="#B51-sensors-16-01761" class="html-bibr">51</a>] Hoechst 33258 (<b>A</b>); metoxanthrone (<b>B</b>); distamycin (<b>C</b>); methylene blue (<b>D</b>) showing the superiority of Hoescht 33258 as a DNA intercalator, over the other intercalators.</p> Full article ">Figure 2
<p>The principle of electrochemical DNA detection using Hoechst 33258 at disposable electrochemical printed (DEP) chips. (<b>A</b>) Hoechst 33258 interacts with the DNA resulting in a slow diffusion coefficient of the molecular label to the electrode surface (<b>a</b>); When DNA is amplified there is more interaction of DNA with the molecular label and aggregate formation, resulting in even slower diffusion of the molecular label to the electrode surface (<b>b</b>); (<b>B</b>) Voltammograms showing anodic peak currents of Hoechst 33258 after intercalation (i) with DNA at relatively low concentration; and (ii) amplified DNA. When the DNA is amplified, more Hoechst 33258 intercalates the DNA resulting in higher decrease in current detected (<b>a</b>); The degree of interaction is proportional to the current detected (<b>b</b>). Part of this figure was used in our original work [<a href="#B52-sensors-16-01761" class="html-bibr">52</a>]).</p> Full article ">Figure 3
<p>An RT-PCR micro flow chip: Electrochemical semi-real time detection of influenza virus RNA by RT-LAMP on a USB-powered portable potentiostat [<a href="#B46-sensors-16-01761" class="html-bibr">46</a>].</p> Full article ">Figure 4
<p>(<b>A</b>) Illustration of the principle of gold-linked electrochemical immunoassay (GLEIA). A primary antibody is immobilized directly on an electrode surface, and a typical sandwich-type immunoreaction carried out. The secondary antibody is labeled with Au NPs. In the presence of 0.1 M HCl, and upon application of approximately 1.2 V, the Au NPs are pre-oxidized from Au<sup>0</sup> to Au<sup>3+</sup>. A differential pulse voltammetry (DPV) from 0 V to 1.0 V immediately after the pre-oxidation step reduces the oxidised NPs back to Au<sup>0</sup> (this figure is a partial reproduction of our previous figure in Vestergaard et al. [<a href="#B1-sensors-16-01761" class="html-bibr">1</a>]); (<b>B</b>) A voltammogram of the reduction signal of Au NPs on carbon SPE. The signal increases proportionally to increase in antigen concentration (part of the figure is from our previous work: Idegami et al. [<a href="#B83-sensors-16-01761" class="html-bibr">83</a>]).</p> Full article ">Figure 5
<p>Portable electrochemical devices used with disposable electrochemical printed (DEP) chips: Handheld potentiostat, operated by a simple PC or tablet (<b>A</b>); conveniently fitted in a small portable case (<b>B</b>); The DEP chips can conveniently fit into PCR tubes (i) for DNA amplification (ii) and subsequent detection (iii, iv) (<b>C</b>); Part of this figure was used in our original work [<a href="#B52-sensors-16-01761" class="html-bibr">52</a>].</p> Full article ">
5262 KiB  
Article
A Fiber Bragg Grating-Based Monitoring System for Roof Safety Control in Underground Coal Mining
by Yiming Zhao, Nong Zhang and Guangyao Si
Sensors 2016, 16(10), 1759; https://doi.org/10.3390/s16101759 - 21 Oct 2016
Cited by 57 | Viewed by 7219
Abstract
Monitoring of roof activity is a primary measure adopted in the prevention of roof collapse accidents and functions to optimize and support the design of roadways in underground coalmines. However, traditional monitoring measures, such as using mechanical extensometers or electronic gauges, either require [...] Read more.
Monitoring of roof activity is a primary measure adopted in the prevention of roof collapse accidents and functions to optimize and support the design of roadways in underground coalmines. However, traditional monitoring measures, such as using mechanical extensometers or electronic gauges, either require arduous underground labor or cannot function properly in the harsh underground environment. Therefore, in this paper, in order to break through this technological barrier, a novel monitoring system for roof safety control in underground coal mining, using fiber Bragg grating (FBG) material as a perceived element and transmission medium, has been developed. Compared with traditional monitoring equipment, the developed, novel monitoring system has the advantages of providing accurate, reliable, and continuous online monitoring of roof activities in underground coal mining. This is expected to further enable the prevention of catastrophic roof collapse accidents. The system has been successfully implemented at a deep hazardous roadway in Zhuji Coal Mine, China. Monitoring results from the study site have demonstrated the advantages of FBG-based sensors over traditional monitoring approaches. The dynamic impacts of progressive face advance on roof displacement and stress have been accurately captured by the novel roadway roof activity and safety monitoring system, which provided essential references for roadway support and design of the mine. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic showing the basic principle of fiber Bragg grating (FBG)-based monitoring sensors.</p> Full article ">Figure 2
<p>Framework of the FBG-based monitoring system.</p> Full article ">Figure 3
<p>Internal schematic of FBG roof separation sensors: 1—Optical fiber; 2—FBG; 3—Wire rope; 4—Cantilever beam; 5—Fixed roller; 6—Spring; 7—Fixed device.</p> Full article ">Figure 4
<p>Schematic of FBG roof separation sensors implemented at field.</p> Full article ">Figure 5
<p>A packaged FBG roof separation sensor.</p> Full article ">Figure 6
<p>Schematic of FBG stress sensors: 1—FBG; 2—Optic fiber; 3—Cantilever beam; 4—Wire rope; 5—Bourdon tube; 6—pressure ring.</p> Full article ">Figure 7
<p>Two packaged FBG stress sensors.</p> Full article ">Figure 8
<p>Schematic of FBG temperature sensors: 1—FBG sensor; 2—stainless steel case; 3—Fiber jumper.</p> Full article ">Figure 9
<p>A packaged FBG temperature sensor.</p> Full article ">Figure 10
<p>Reflected wavelength and displacement response curve (FBG roof separation sensor).</p> Full article ">Figure 11
<p>Reflected wavelength and pressure response curve (FBG stress sensor).</p> Full article ">Figure 12
<p>Reflected wavelength and temperature response curve (FBG temperature sensor).</p> Full article ">Figure 13
<p>Geological borehole profile of the 1111(1) longwall working face.</p> Full article ">Figure 14
<p>Plan view of FBG sensors layouts in haulage entry roof of 1111(1) working face (unit: Decimeter).</p> Full article ">Figure 15
<p>Field implementation of all measurement equipment: (<b>a</b>) FBG roof separation sensors; (<b>b</b>) FBG stress sensors; (<b>c</b>) Electric pressure gauges.</p> Full article ">Figure 16
<p>Schematic of the optical path for data transmission at Zhuji coal mine.</p> Full article ">Figure 17
<p>Monitoring results of roof displacement during the approaching of the 1111(1) longwall face.</p> Full article ">Figure 18
<p>Monitoring results of normal stress loading on roof bolts during the approaching of the 1111(1) longwall face: (<b>a</b>) Electric pressure gauges; (<b>b</b>) FBG stress sensors.</p> Full article ">
3299 KiB  
Article
Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder
by Shuoyang Chen, Tingfa Xu, Daqun Li, Jizhou Zhang and Shenwang Jiang
Sensors 2016, 16(10), 1758; https://doi.org/10.3390/s16101758 - 21 Oct 2016
Cited by 13 | Viewed by 10730
Abstract
During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In [...] Read more.
During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern)” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array) platforms and the high-precision intelligent holder. Full article
(This article belongs to the Section Physical Sensors)
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<p>The flow chart of the proposed scheme.</p> Full article ">Figure 2
<p>(<b>a</b>) The basic 3 × 3 operator of the LBP algorithm; and (<b>b</b>) multi-scale LBP (Local Binary Pattern) based on blocks of 9 × 9 size. Computation is done based on average values of block sub regions, instead of individual pixels. In each sub-region, average sum of image intensity is computed. These average sums are then compared with the gray value of the center block.</p> Full article ">Figure 3
<p>(<b>a</b>) The gray scale image of the original image; (<b>b</b>) the texture image of the background; (<b>c</b>) the histogram of the original image; and (<b>d</b>) the LBP histogram of the background image, which shows extensive information of the texture.</p> Full article ">Figure 4
<p>(<b>a</b>) Our moving object detecting system and the description of each component; (<b>b</b>) the high-precision intelligent holder; and (<b>c</b>) the platform of FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor).</p> Full article ">Figure 5
<p>(<b>a</b>) Frame 555 of input images; (<b>b</b>) the ground truth; (<b>c</b>) GMM (Gaussian Mixture Modeling); (<b>d</b>) ViBe (Visual Background extractor); (<b>e</b>) GMG (an algorithm for finding the Global Minimum with a Guarantee); (<b>f</b>) KDE (Kernel Density Estimation); (<b>g</b>) LBAdaptiveSOM (Local Background Adaptive Self-Organizing Modeling); and (<b>h</b>) our method.</p> Full article ">Figure 6
<p>(<b>a</b>) Frame 1252 of input images; (<b>b</b>) ground truth; (<b>c</b>) GMM (Gaussian Mixture Modeling); (<b>d</b>) ViBe (Visual Background extractor); (<b>e</b>) GMG (an algorithm for finding the Global Minimum with a Guarantee); (<b>f</b>) KDE (Kernel Density Estimation); (<b>g</b>) LBAdaptiveSOM (Local Background Adaptive Self-Organizing Modeling) ; and (<b>h</b>) our method.</p> Full article ">Figure 7
<p>(<b>a</b>) is the frame 389 of input images; (<b>b</b>) ground truth; (<b>c</b>) GMM (Gaussian Mixture Modeling); (<b>d</b>) ViBe (Visual Background extractor); (<b>e</b>) GMG (an algorithm for finding the Global Minimum with a Guarantee); (<b>f</b>) KDE (Kernel Density Estimation); (<b>g</b>) LBAdaptiveSOM (Local Background Adaptive Self-Organizing Modeling); and (<b>h</b>) our method.</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>) is the frame 389 of input images; (<b>b</b>) ground truth; (<b>c</b>) GMM (Gaussian Mixture Modeling); (<b>d</b>) ViBe (Visual Background extractor); (<b>e</b>) GMG (an algorithm for finding the Global Minimum with a Guarantee); (<b>f</b>) KDE (Kernel Density Estimation); (<b>g</b>) LBAdaptiveSOM (Local Background Adaptive Self-Organizing Modeling); and (<b>h</b>) our method.</p> Full article ">Figure 8
<p>(<b>a</b>) is the frame 15553 of input images; (<b>b</b>) ground truth; (<b>c</b>) GMM (Gaussian Mixture Modeling); (<b>d</b>) ViBe (Visual Background extractor); (<b>e</b>) GMG (an algorithm for finding the Global Minimum with a Guarantee); (<b>f</b>) KDE (Kernel Density Estimation); (<b>g</b>) LBAdaptiveSOM (Local Background Adaptive Self-Organizing Modeling); (<b>h</b>) our method.</p> Full article ">Figure 9
<p>(<b>a</b>) is the frame 485 of input images; (<b>b</b>) ground truth; (<b>c</b>) GMM (Gaussian Mixture Modeling); (<b>d</b>) ViBe (Visual Background extractor); (<b>e</b>) GMG (an algorithm for finding the Global Minimum with a Guarantee); (<b>f</b>) KDE (Kernel Density Estimation); (<b>g</b>) LBAdaptiveSOM (Local Background Adaptive Self-Organizing Modeling); (<b>h</b>) our method.</p> Full article ">Figure 10
<p>This image shows the line chart of the F-1 value of the experiment result of those methods we mentioned above.</p> Full article ">
1044 KiB  
Communication
First Results of a Detection Sensor for the Monitoring of Laying Hens Reared in a Commercial Organic Egg Production Farm Based on the Use of Infrared Technology
by Mauro Zaninelli, Veronica Redaelli, Erica Tirloni, Cristian Bernardi, Vittorio Dell’Orto and Giovanni Savoini
Sensors 2016, 16(10), 1757; https://doi.org/10.3390/s16101757 - 21 Oct 2016
Cited by 20 | Viewed by 5061
Abstract
The development of a monitoring system to identify the presence of laying hens, in a closed room of a free-range commercial organic egg production farm, was the aim of this study. This monitoring system was based on the infrared (IR) technology and had, [...] Read more.
The development of a monitoring system to identify the presence of laying hens, in a closed room of a free-range commercial organic egg production farm, was the aim of this study. This monitoring system was based on the infrared (IR) technology and had, as final target, a possible reduction of atmospheric ammonia levels and bacterial load. Tests were carried out for three weeks and involved 7 ISA (Institut de Sélection Animale) brown laying hens. The first 5 days was used to set up the detection sensor, while the other 15 days were used to evaluate the accuracy of the resulting monitoring system, in terms of sensitivity and specificity. The setup procedure included the evaluation of different color background (CB) thresholds, used to discriminate the information contents of the thermographic images. At the end of this procedure, a CB threshold equal to an increase of 3 °C from the floor temperature was chosen, and a cutoff level of 196 colored pixels was identified as the threshold to use to classify a positive case. The results of field tests showed that the developed monitoring system reached a fine detection accuracy (sensitivity = 97.9% and specificity = 94.9%) and the IR technology proved to be a possible solution for the development of a detection sensor necessary to reach the scope of this study. Full article
(This article belongs to the Section Physical Sensors)
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<p>Block schema of the experimental monitoring system.</p> Full article ">Figure 2
<p>Flow diagram of the software applications of the monitoring system and detection sensor.</p> Full article ">Figure 3
<p>The picture reports the setup of the housing system. The water dispenser, that is represented with the symbol “W”, and the other main components of the system are reported. In the closed room, drawn in red, are shown: the infrared (IR) sensor (i.e., the thermographic camera) mounted on the ceiling of the room and the field of view (FOV) of the sensor highlighted on the floor of the room. Finally, dimensions of the housing system, of the IR sensor position, and of the FOV, are also reported.</p> Full article ">Figure 4
<p>Pictures (<b>A</b>,<b>B</b>) are examples of the thermographic images collected during the experiment, while pictures (<b>C</b>,<b>D</b>) are examples of the elaborations performed by the monitoring system under study. In details, picture (<b>C</b>) is the result of the elaboration performed having as input the picture (<b>A</b>) while picture (<b>D</b>) is the result of the elaboration performed having as input the picture (<b>B</b>). In picture (<b>C</b>), the number of colored pixels (CP) is 113 while in picture (<b>D</b>), the number of CP is 1832. Therefore, considering a cutoff threshold of 196 pixels (“hens detected” threshold), picture (<b>C</b>) is an example of a false negative case while picture (<b>D</b>) is an example of a true positive case.</p> Full article ">
8776 KiB  
Article
A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation
by Rui Zhang, Shiping Zhu and Qin Zhou
Sensors 2016, 16(10), 1756; https://doi.org/10.3390/s16101756 - 21 Oct 2016
Cited by 21 | Viewed by 7060
Abstract
Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model [...] Read more.
Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models. Full article
(This article belongs to the Special Issue Infrared and THz Sensing and Imaging)
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<p>Variation of coefficients when <math display="inline"> <semantics> <mi>τ</mi> </semantics> </math> = 1.</p> Full article ">Figure 2
<p>Variation of coefficients when <math display="inline"> <semantics> <mi>τ</mi> </semantics> </math> = 0.1.</p> Full article ">Figure 3
<p>The flowchart of the algorithm.</p> Full article ">Figure 4
<p>(<b>a</b>) Evolution of the contour when the initial contour is large; and (<b>b</b>) evolution of the contour when the initial contour is small.</p> Full article ">Figure 5
<p>Segmentation results of U-shape image.</p> Full article ">Figure 6
<p>LTI (Long and Thin Indentation) convergence results of all models.</p> Full article ">Figure 7
<p>The observation of relationship between segmentation accuracy and the value of τ (the valve of K is constant “1”).</p> Full article ">Figure 8
<p>Original images and initial contours used in the experiment. (<b>a</b>) “plane”; (<b>b</b>) “ship”; (<b>c</b>) “tank”; (<b>d</b>) Initial contour of “plane” (size: 165 × 75); (<b>e</b>) Initial contour of “ship” (size: 158 × 86); (<b>f</b>) Initial contour of “tank” (size: 123 × 98).</p> Full article ">Figure 9
<p>Segmentation results of usual infrared images.</p> Full article ">Figure 10
<p>Images corrupted with the salt–pepper noises and initial contours in the experiment. (<b>a</b>) “planeN”; (<b>b</b>) “shipN”; (<b>c</b>) “tankN”; (<b>d</b>) Initial contour of “planeN” (size: 160 × 72); (<b>e</b>) Initial contour of “shipN” (size: 155 × 85); (<b>f</b>) Initial contour of “tankN” (size: 120 × 95).</p> Full article ">Figure 11
<p>Images corrupted with the multiplicative noises and initial contours in the experiment. (<b>a</b>) “planeN2”; (<b>b</b>) “shipN2”; (<b>c</b>) “tankN2”; (<b>d</b>) Initial contour of “planeN2” (size: 170 × 75); (<b>e</b>) Initial contour of “shipN2” (size: 156 × 85); (<b>f</b>) Initial contour of “tankN2” (size: 125 × 95).</p> Full article ">Figure 12
<p>Segmentation results of the images corrupted with salt–pepper noises.</p> Full article ">Figure 13
<p>Segmentation results of the images corrupted with multiplicative noises.</p> Full article ">Figure 14
<p>Segmentation results in the infrared images with different ‘V’ values of noise intensity. (The noise intensity gets higher from left to right.)</p> Full article ">Figure 15
<p>Block diagrams of quantitatively analyzed segmentation results of infrared images.</p> Full article ">Figure 15 Cont.
<p>Block diagrams of quantitatively analyzed segmentation results of infrared images.</p> Full article ">Figure 16
<p>Evolution of the contour and segmentation results of natural images and medical images using proposed method.</p> Full article ">
18231 KiB  
Article
Fine-Scale Population Estimation by 3D Reconstruction of Urban Residential Buildings
by Shixin Wang, Ye Tian, Yi Zhou, Wenliang Liu and Chenxi Lin
Sensors 2016, 16(10), 1755; https://doi.org/10.3390/s16101755 - 21 Oct 2016
Cited by 30 | Viewed by 6390
Abstract
Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D [...] Read more.
Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable. Full article
(This article belongs to the Special Issue Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment)
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<p>Location of the study area.</p> Full article ">Figure 2
<p>Flowchart of the research. Note: PanTex—Built-up Presence Index; MBI—Morphological Building Index; MSI—Morphological Shadow Index; CIIT—Color Invariant Indices; POI—Point of Interests; SHP—shapefile format processed by the ArcMap 10.2 software.</p> Full article ">Figure 3
<p>Shadow post-processing procedures.</p> Full article ">Figure 4
<p>Population density in the research area.</p> Full article ">Figure 5
<p>Dasymetric mapping of the population density in the research area.</p> Full article ">Figure 6
<p>Location of the sample area.</p> Full article ">Figure 7
<p>PanTex results derived from different moving window sizes. (<b>a</b>–<b>d</b>) indicate the results from window sizes of 4, 7, 9 and 14, respectively (a higher value means higher probability of buildings).</p> Full article ">Figure 8
<p>Building extraction results through PanTex. (<b>a</b>) PanTex in research area (window size = 7); (<b>b</b>) building extraction using object-based method (red regions symbolize buildings).</p> Full article ">Figure 9
<p>MBI result derived from different structural element sizes. (<b>a</b>–<b>e</b>) represent granulometry intervals (<math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi>s</mi> </mrow> </semantics> </math>) of 30, 15, 7, 5 and 2, respectively (a higher value means higher probability of buildings).</p> Full article ">Figure 10
<p>Building extraction results through MBI. (<b>a</b>) MBI of research area (<math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi>s</mi> </mrow> </semantics> </math> = 7); (<b>b</b>) building extraction by object-based method where red regions symbolize buildings.</p> Full article ">Figure 11
<p>Shadow extraction using MSI based method in sample area. (<b>a</b>) MSI; (<b>b</b>) MSI filtered by NDVI and NDWI; (<b>c</b>) MSI after component analysis; (<b>d</b>) final shadow extraction results. (Higher value of MSI means higher probability of shadow in (<b>a</b>–<b>c</b>), the white color in (<b>d</b>) is the final shadow.)</p> Full article ">Figure 12
<p>Shadow extraction using CIIT in sample area. (<b>a</b>) CIIT; (<b>b</b>) CIIT filtered by NDVI and NDWI; (<b>c</b>) CIIT after component analysis; (<b>d</b>) final shadow extraction results. (Higher value of CIIT means higher probability of shadow in (<b>a</b>–<b>c</b>), the white color in (<b>d</b>) is the final shadow.)</p> Full article ">Figure 13
<p>Results of final shadow extraction where black regions indicate shadows. (<b>a</b>) shadows extraced by the MSI method; (<b>b</b>) shadows extraced by the CIIT method.</p> Full article ">Figure 14
<p>Results of building height using MSI and CIIT. (<b>a</b>) MSI method; (<b>b</b>) CIIT method.</p> Full article ">Figure 15
<p>Population spatialization results (superimposed by the true color synthesis of ZY-3 imagery).</p> Full article ">Figure 16
<p>Accuracy analysis of fine-scale population estimation.</p> Full article ">Figure 17
<p>Spatial distribution of RE in the research area (original one has been changed; bad regions indicate RE &gt; 30%).</p> Full article ">
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Article
Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
by Peng Jiang, Yang Feng and Feng Wu
Sensors 2016, 16(10), 1754; https://doi.org/10.3390/s16101754 - 21 Oct 2016
Cited by 17 | Viewed by 5176
Abstract
This study addresses the optimization of node redeployment coverage in underwater wireless sensor networks. Given that nodes could easily become invalid under a poor environment and the large scale of underwater wireless sensor networks, an underwater sensor network redeployment algorithm was developed based [...] Read more.
This study addresses the optimization of node redeployment coverage in underwater wireless sensor networks. Given that nodes could easily become invalid under a poor environment and the large scale of underwater wireless sensor networks, an underwater sensor network redeployment algorithm was developed based on wolf search. This study is to apply the wolf search algorithm combined with crowded degree control in the deployment of underwater wireless sensor networks. The proposed algorithm uses nodes to ensure coverage of the events, and it avoids the prematurity of the nodes. The algorithm has good coverage effects. In addition, considering that obstacles exist in the underwater environment, nodes are prevented from being invalid by imitating the mechanism of avoiding predators. Thus, the energy consumption of the network is reduced. Comparative analysis shows that the algorithm is simple and effective in wireless sensor network deployment. Compared with the optimized artificial fish swarm algorithm, the proposed algorithm exhibits advantages in network coverage, energy conservation, and obstacle avoidance. Full article
(This article belongs to the Special Issue Sensing Technologies for Autonomy and Cooperation in Underwater Networked Robot Systems)
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<p>Division map of an underwater node communication area.</p> Full article ">Figure 2
<p>Basic principle diagram of RAWS (redeployment algorithm based on wolf search).</p> Full article ">Figure 3
<p>Flowchart of the RAWS algorithm.</p> Full article ">Figure 4
<p>Comparison of network coverage rate at varying iterations.</p> Full article ">Figure 5
<p>Comparison of the number of ineffective nodes at varying iterations.</p> Full article ">Figure 6
<p>Layout of ineffective nodes in the second iteration.</p> Full article ">Figure 7
<p>Layout of ineffective nodes in the tenth iteration.</p> Full article ">Figure 8
<p>Comparison of network coverage rate at varying numbers of obstacles.</p> Full article ">Figure 9
<p>Comparison of network coverage rate at varying numbers of nodes.</p> Full article ">Figure 10
<p>Comparison of average residual energy of nodes at varying numbers of nodes.</p> Full article ">Figure 11
<p>Comparison of average residual energy of nodes at varying numbers of monitored targets.</p> Full article ">
8806 KiB  
Article
Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application
by Qifan Zhou, Hai Zhang, Zahra Lari, Zhenbo Liu and Naser El-Sheimy
Sensors 2016, 16(10), 1752; https://doi.org/10.3390/s16101752 - 21 Oct 2016
Cited by 19 | Viewed by 7232
Abstract
Wearable electronic devices have experienced increasing development with the advances in the semiconductor industry and have received more attention during the last decades. This paper presents the development and implementation of a novel inertial sensor-based foot-mounted wearable electronic device for a brand new [...] Read more.
Wearable electronic devices have experienced increasing development with the advances in the semiconductor industry and have received more attention during the last decades. This paper presents the development and implementation of a novel inertial sensor-based foot-mounted wearable electronic device for a brand new application: game playing. The main objective of the introduced system is to monitor and identify the human foot stepping direction in real time, and coordinate these motions to control the player operation in games. This proposed system extends the utilized field of currently available wearable devices and introduces a convenient and portable medium to perform exercise in a more compelling way in the near future. This paper provides an overview of the previously-developed system platforms, introduces the main idea behind this novel application, and describes the implemented human foot moving direction identification algorithm. Practical experiment results demonstrate that the proposed system is capable of recognizing five foot motions, jump, step left, step right, step forward, and step backward, and has achieved an over 97% accuracy performance for different users. The functionality of the system for real-time application has also been verified through the practical experiments. Full article
(This article belongs to the Section Physical Sensors)
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<p>The main concept of proposed system.</p> Full article ">Figure 2
<p>System architecture.</p> Full article ">Figure 3
<p>System hardware platform.</p> Full article ">Figure 4
<p>Overview of foot motion identification process.</p> Full article ">Figure 5
<p>Inertial sensor measurement alignment.</p> Full article ">Figure 6
<p>Stepping acceleration signal and gait phases.</p> Full article ">Figure 7
<p>Acceleration signal of different motions and data segmentation: (<b>a</b>) Forward; (<b>b</b>) Backward; (<b>c</b>) Left; (<b>d</b>) Right; and (<b>e</b>) Jump.</p> Full article ">Figure 7 Cont.
<p>Acceleration signal of different motions and data segmentation: (<b>a</b>) Forward; (<b>b</b>) Backward; (<b>c</b>) Left; (<b>d</b>) Right; and (<b>e</b>) Jump.</p> Full article ">Figure 8
<p>Decision tree graphical model.</p> Full article ">Figure 9
<p>K-nearest neighbors algorithm (kNN) algorithm concept.</p> Full article ">Figure 10
<p>Support vector machine (SVM) classifier.</p> Full article ">Figure 11
<p>Hardware platform and the sensor placement on shoe. (<b>a</b>) Hardware platform of proposed system; (<b>b</b>–<b>e</b>) Sensor placement on different testers’ shoes.</p> Full article ">Figure 11 Cont.
<p>Hardware platform and the sensor placement on shoe. (<b>a</b>) Hardware platform of proposed system; (<b>b</b>–<b>e</b>) Sensor placement on different testers’ shoes.</p> Full article ">Figure 12
<p>Accuracy comparison of three classifiers.</p> Full article ">Figure 13
<p>Precision comparison of three classifiers.</p> Full article ">Figure 14
<p>Practical game play test in running game: (<b>a</b>) Jump motion; (<b>b</b>) Forward motion; (<b>c</b>) Backward motion; (<b>d</b>) Right motion; and (<b>e</b>) Left motion.</p> Full article ">Figure 15
<p>Practical game play test in Game Subway Surfers: (<b>a</b>) Step forward; and (<b>b</b>) Step left.</p> Full article ">Figure 15 Cont.
<p>Practical game play test in Game Subway Surfers: (<b>a</b>) Step forward; and (<b>b</b>) Step left.</p> Full article ">
7213 KiB  
Article
A Low-Cost Optical Remote Sensing Application for Glacier Deformation Monitoring in an Alpine Environment
by Daniele Giordan, Paolo Allasia, Niccolò Dematteis, Federico Dell’Anese, Marco Vagliasindi and Elena Motta
Sensors 2016, 16(10), 1750; https://doi.org/10.3390/s16101750 - 21 Oct 2016
Cited by 27 | Viewed by 6730
Abstract
In this work, we present the results of a low-cost optical monitoring station designed for monitoring the kinematics of glaciers in an Alpine environment. We developed a complete hardware/software data acquisition and processing chain that automatically acquires, stores and co-registers images. The system [...] Read more.
In this work, we present the results of a low-cost optical monitoring station designed for monitoring the kinematics of glaciers in an Alpine environment. We developed a complete hardware/software data acquisition and processing chain that automatically acquires, stores and co-registers images. The system was installed in September 2013 to monitor the evolution of the Planpincieux glacier, within the open-air laboratory of the Grandes Jorasses, Mont Blanc massif (NW Italy), and collected data with an hourly frequency. The acquisition equipment consists of a high-resolution DSLR camera operating in the visible band. The data are processed with a Pixel Offset algorithm based on normalized cross-correlation, to estimate the deformation of the observed glacier. We propose a method for the pixel-to-metric conversion and present the results of the projection on the mean slope of the glacier. The method performances are compared with measurements obtained by GB-SAR, and exhibit good agreement. The system provides good support for the analysis of the glacier evolution and allows the creation of daily displacement maps. Full article
(This article belongs to the Section Remote Sensors)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Aerial view of the Ferret Valley. The Planpicieux glacier is located in the upper part of the image. In the bottom part of the Ferret Valley, the Planpincieux hamlet represents the main element at risk of possible ice avalanche activation. On the opposite side of the valley, at a distance of 3.8 km, the monitoring station is located on the top of Mon de La Saxe.</p> Full article ">Figure 2
<p>Overview of the Grandes Jorasses massif (northern sector of the Mount Blanc massif) with the Grandes Jorasses Glacier (on the right side of the image) and the Planpincieux Glacier (highlighted in dashed red line). The image was acquired by the webcam of the monitoring station. The yellow box indicates the part acquired by the ZOOM camera, which is the most active region of the Planpincieux glacier.</p> Full article ">Figure 3
<p>Monitoring station: (A) Box containing the two cameras. (B) Webcam and weather station. (C) Solar panels. The area acquired is highlighted in blue. In the yellow box: detail of the acquisition system within the shelter box.</p> Full article ">Figure 4
<p>Work flow of the image processing.</p> Full article ">Figure 5
<p>(<b>a</b>) In red the reference window used for the co-registration process, lying on the rock outcrop. In blue the two windows used for error estimation; (<b>b</b>) Frequency distributions of Mean Absolute Error computed separately for the vertical (blue) and horizontal (orange) dimensions.</p> Full article ">Figure 6
<p>Visual analysis of the glacier flow on 2 August 2015 (<b>left</b>) and and 8 October 2015 (<b>right</b>). The superimposed grid simplifies the comparison between different areas. In particular the movement of the pattern highlighted by the letter A appears evident. The B pattern appears less changed.</p> Full article ">Figure 7
<p>(<b>a</b>) Representation of the Pixel Offset iterative algorithm: the tile of the Master image is compared with the same tile of the Slave image. The procedure is applied to all the tiles. (<b>b</b>) Example of a result of the Pixel Offset algorithm with the vertical displacement between images computed in pixels.</p> Full article ">Figure 8
<p>On the left, the DTM produced by the LiDAR survey in June 2014. Blue line identifies the location of the selected vertical profile. The red line highlights the glacier margins. On the right, representation of the vertical profile of the glacier. On average, the glacier lies on a flat plane with a mean slope of 32°. Different colours highlight the sectors of the glacier characterized by different physical features. The right vertical axis reports the pixel dimension increasing upward within the image.</p> Full article ">Figure 9
<p>EAPOE output. On the left, there is a mean vertical velocity map (<b>top</b>), a fraction of days with acceptable coherence map (<b>bottom left</b>) and a cumulative vertical displacement map (<b>bottom right</b>). The time series of cumulative displacement and daily velocities of the selected tile are plotted on the right. At the bottom, the images show the glacier evolution.</p> Full article ">Figure 10
<p>Projected vertical cumulative displacement maps for 2014 and 2015 and time series of displacement of different sectors. From the comparison one can note the behaviour changes and anomalies during time and space and identifies critical data.</p> Full article ">
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Article
A Multifunctional Sensor in Ternary Solution Using Canonical Correlations for Variable Links Assessment
by Dan Liu, Qisong Wang, Xin Liu, Ruixin Niu, Yan Zhang and Jinwei Sun
Sensors 2016, 16(10), 1661; https://doi.org/10.3390/s16101661 - 21 Oct 2016
Cited by 4 | Viewed by 4978
Abstract
Accurately measuring the oil content and salt content of crude oil is very important for both estimating oil reserves and predicting the lifetime of an oil well. There are some problems with the current methods such as high cost, low precision, and difficulties [...] Read more.
Accurately measuring the oil content and salt content of crude oil is very important for both estimating oil reserves and predicting the lifetime of an oil well. There are some problems with the current methods such as high cost, low precision, and difficulties in operation. To solve these problems, we present a multifunctional sensor, which applies, respectively, conductivity method and ultrasound method to measure the contents of oil, water, and salt. Based on cross sensitivity theory, these two transducers are ideally integrated for simplifying the structure. A concentration test of ternary solutions is carried out to testify its effectiveness, and then Canonical Correlation Analysis is applied to evaluate the data. From the perspective of statistics, the sensor inputs, for instance, oil concentration, salt concentration, and temperature, are closely related to its outputs including output voltage and time of flight of ultrasound wave, which further identify the correctness of the sensing theory and the feasibility of the integrated design. Combined with reconstruction algorithms, the sensor can realize the content measurement of the solution precisely. The potential development of the proposed sensor and method in the aspect of online test for crude oil is of important reference and practical value. Full article
(This article belongs to the Special Issue Smart Sensor Interface Circuits and Systems)
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<p>Sensor probe: (<b>1</b>) acrylic resin substrate; (<b>2</b>) stainless steel electrode; (<b>3</b>) piezoelectric transducer; (<b>4</b>) thermometer; (<b>5</b>) support beam; (<b>6</b>) ultrasonic transducer wire; (<b>7</b>) conductivity sensor wire; and (<b>8</b>) Thermometer wire.</p> Full article ">Figure 2
<p>Equivalent circuit of conductivity measurement.</p> Full article ">Figure 3
<p>Schematic diagram of TOF measurement.</p> Full article ">Figure 4
<p>Sketch of transmitted and reflected waves.</p> Full article ">Figure 5
<p>Experimental data: (<b>a</b>) Output voltage; and (<b>b</b>) Time of Flight.</p> Full article ">Figure 6
<p>Schematic structure of multifunctional sensing technique.</p> Full article ">Figure 7
<p>Relative errors of reconstructed oil concentrations at: (<b>a</b>) 5 °C; (<b>b</b>) 15 °C; (<b>c</b>) 25 °C; and (<b>d</b>) 35 °C. Black stars (*) stand for the relative errors of the training data, and red circles (o) represent those of the testing data.</p> Full article ">Figure 7 Cont.
<p>Relative errors of reconstructed oil concentrations at: (<b>a</b>) 5 °C; (<b>b</b>) 15 °C; (<b>c</b>) 25 °C; and (<b>d</b>) 35 °C. Black stars (*) stand for the relative errors of the training data, and red circles (o) represent those of the testing data.</p> Full article ">Figure 8
<p>Relative error of reconstructed salt concentration at: (<b>a</b>) 5 °C; (<b>b</b>) 15 °C; (<b>c</b>) 25 °C; and (<b>d</b>) 35 °C. Black stars (*) stand for the relative errors of the training data, and red circles (o) represent those of the testing data.</p> Full article ">
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Article
A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics
by Cristian Axenie, Christoph Richter and Jörg Conradt
Sensors 2016, 16(10), 1751; https://doi.org/10.3390/s16101751 - 20 Oct 2016
Cited by 10 | Viewed by 6687
Abstract
Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a [...] Read more.
Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity. All the different sensory streams enter the system through multiple parallel channels. The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning. The proposed framework introduces mechanisms for self-creation and learning of the functional relations between the computational maps, encoding sensorimotor streams, directly from the data. Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems. We demonstrate this by applying our model to a 3D motion estimation on a quadrotor. Full article
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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<p>Model architecture. (<b>a</b>) Input data resembling a nonlinear relation and its distribution; (<b>b</b>) Basic architecture; (<b>c</b>) Model internal structure; (<b>d</b>) Processing stages.</p> Full article ">Figure 2
<p>Extracted sensory relation and data statistics using the proposed model: (<b>a</b>) Input data statistics and hidden relation; (<b>b</b>) Learned preferred values and underlying relation.</p> Full article ">Figure 3
<p>Experimental setup: (<b>a</b>) Quadrotor platform; (<b>b</b>) Reference system alignment and ground truth camera tracking system; (<b>c</b>) Sensors used in the experiement for Roll-Pitch-Yaw (RPY) estimation.</p> Full article ">Figure 4
<p>Network inference algorithm: (<b>a</b>) Algorithm pipeline: feed time-series sensory input; compute statistics for individual and pairs of sensors (entropy and mutual information); compute statistical distance and conditional entropies to extract statistical relatedness; create connectivity array using entropy reduction (minimisation); (<b>b</b>) Network structure evolution: initial connectivity; intermediate statistically clustered variables; final structure and inferred connectivity.</p> Full article ">Figure 5
<p>Network inference analysis: (<b>a</b>) Sensory data, inferred network structure, and associations for each motion component; (<b>b</b>) Individual estimates of mutual information, on a per sensory variable basis, motivating the established network connections for sensory associations.</p> Full article ">Figure 6
<p>Network instantiation for 3D egomotion estimation: inferred network structure and sensory associations for learning. (<b>a</b>) On-board sensory configuration; (<b>b</b>) Inferred network connectivity; (<b>c</b>) Sensory associations for learning.</p> Full article ">Figure 7
<p>Basic system analysis. Sample scenario with a 3-dimensional network with a circular correlation structure. (<b>a</b>) Input data and decoded learned representation: the inputs are encoded in distributed neural activation profiles using Self-Organising Maps (SOM); the temporal coincidence of these activations strengthen the connection weights in the representation space using Hebbian learning (HL); (<b>b</b>) Learned relations.</p> Full article ">Figure 8
<p>Network instantiation for 3D egomotion estimation: a decoupled view analysis. (<b>a</b>) Learned relation for roll; (<b>b</b>) Learned relation for pitch; (<b>c</b>) Learned relation for yaw. (yellow traces depict highest connection strengths).</p> Full article ">Figure 9
<p>Network instantiation for 3D egomotion estimation: a decoupled view analysis. (<b>a</b>) Inferred sensory estimates for roll; (<b>b</b>) Inferred sensory estimates for pitch; (<b>c</b>) Inferred sensory estimates for yaw.</p> Full article ">Figure 10
<p>Network instantiation for 3D egomotion estimation: fused sensory data. (<b>a</b>) Fused estimate for roll; (<b>b</b>) Fused estimate for pitch; (<b>c</b>) Fused estimate for yaw.</p> Full article ">
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Article
Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS
by Tara Blakey, Assefa Melesse, Michael C. Sukop, Georgio Tachiev, Dean Whitman and Fernando Miralles-Wilhelm
Sensors 2016, 16(10), 1749; https://doi.org/10.3390/s16101749 - 20 Oct 2016
Cited by 7 | Viewed by 4463
Abstract
This study evaluated the ability to improve Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels’ benthic class. The form of the Ocean Color (OC) algorithm was assumed for this study. The operational [...] Read more.
This study evaluated the ability to improve Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels’ benthic class. The form of the Ocean Color (OC) algorithm was assumed for this study. The operational atmospheric correction producing Level 2 SeaWiFS data was retained since the focus of this study was on establishing the benefit from the alternative specification of the bio-optical algorithm. Benthic class was determined through satellite image-based classification methods. Accuracy of the chl-a algorithms evaluated was determined through comparison with coincident in situ measurements of chl-a. The regionally-tuned models that were allowed to vary by benthic class produced more accurate estimates of chl-a than the single, unified regionally-tuned model. Mean absolute percent difference was approximately 70% for the regionally-tuned, benthic class-specific algorithms. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Atmospheric correction procedures specialized to coastal environments were recognized as areas for future improvement as these procedures would improve both classification and algorithm tuning. Full article
(This article belongs to the Special Issue Sensors and Sensing in Water Quality Assessment and Monitoring)
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<p>Location of study area sample stations in Florida Bay, FL, USA showing bathymetry contours as colored lines. The contours were created by the Florida Fish and Wildlife Commission based on trackline data collected in 1990 [<a href="#B12-sensors-16-01749" class="html-bibr">12</a>].</p> Full article ">Figure 2
<p>In situ measured <span class="html-italic">chl-a</span> versus (<b>A</b>) OC4v6 <span class="html-italic">chl-a</span> product and (<b>B</b>) Unified regionally-tuned model <span class="html-italic">chl-a</span> based on <span class="html-italic">X</span>.</p> Full article ">Figure 3
<p>Residuals from (<b>A</b>) Sparse-low and (<b>B</b>) Medium-dense models with markers distinguished by season. The x-axis is the value of the band ratio <span class="html-italic">X</span> (defined in Equation (2)).</p> Full article ">Figure 4
<p>Residuals from (<b>A</b>) Sparse-low and (<b>B</b>) Medium-dense models with markers distinguished by station.</p> Full article ">
7734 KiB  
Article
A Readout IC Using Two-Step Fastest Signal Identification for Compact Data Acquisition of PET Systems
by Sung-Jin Jung, Seong-Kwan Hong and Oh-Kyong Kwon
Sensors 2016, 16(10), 1748; https://doi.org/10.3390/s16101748 - 20 Oct 2016
Cited by 3 | Viewed by 6608
Abstract
A readout integrated circuit (ROIC) using two-step fastest signal identification (FSI) is proposed to reduce the number of input channels of a data acquisition (DAQ) block with a high-channel reduction ratio. The two-step FSI enables the proposed ROIC to filter out useless input [...] Read more.
A readout integrated circuit (ROIC) using two-step fastest signal identification (FSI) is proposed to reduce the number of input channels of a data acquisition (DAQ) block with a high-channel reduction ratio. The two-step FSI enables the proposed ROIC to filter out useless input signals that arise from scattering and electrical noise without using complex and bulky circuits. In addition, an asynchronous fastest signal identifier and a self-trimmed comparator are proposed to identify the fastest signal without using a high-frequency clock and to reduce misidentification, respectively. The channel reduction ratio of the proposed ROIC is 16:1 and can be extended to 16 × N:1 using N ROICs. To verify the performance of the two-step FSI, the proposed ROIC was implemented into a gamma photon detector module using a Geiger-mode avalanche photodiode with a lutetium-yttrium oxyorthosilicate array. The measured minimum detectable time is 1 ns. The difference of the measured energy and timing resolution between with and without the two-step FSI are 0.8% and 0.2 ns, respectively, which are negligibly small. These measurement results show that the proposed ROIC using the two-step FSI reduces the number of input channels of the DAQ block without sacrificing the performance of the positron emission tomography (PET) systems. Full article
(This article belongs to the Special Issue Smart Sensor Interface Circuits and Systems)
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<p>(<b>a</b>) Block diagram of a positron emission tomography (PET) system based on the proposed readout integrated circuit (ROIC) and (<b>b</b>) timing diagram of the analog signal processing (ASP) block.</p> Full article ">Figure 2
<p>Block diagram of the proposed ROIC.</p> Full article ">Figure 3
<p>(<b>a</b>) Schematic and (<b>b</b>) timing diagram of the self-trimmed comparator.</p> Full article ">Figure 4
<p>Monte-Carlo simulation results with and without self-trimming.</p> Full article ">Figure 5
<p>Block diagrams of (<b>a</b>) the asynchronous fastest signal identifier (AFSI) and (<b>b</b>) the faster pulse identification unit (FPIU).</p> Full article ">Figure 6
<p>Simulation results of the asynchronous fastest signal identifier (AFSI).</p> Full article ">Figure 7
<p>Schematic of the all-pass filter.</p> Full article ">Figure 8
<p>Microphotograph of the fabricated ROIC.</p> Full article ">Figure 9
<p>Photograph of a (<b>a</b>) test board of the ASP block; (<b>b</b>) 4 × 4 Geiger-mode avalanche photodiode (GAPD) array; and (<b>c</b>) 4 × 4 lutetium-yttrium oxyorthosilicate (LYSO) array.</p> Full article ">Figure 10
<p>Measured input and output waveforms of the (<b>a</b>) ROIC and (<b>b</b>) ASP block.</p> Full article ">Figure 11
<p>Measured energy spectra with and without the two-step FSI.</p> Full article ">Figure 12
<p>Measured energy resolution (<b>a</b>) without and (<b>b</b>) with two-step FSI.</p> Full article ">Figure 13
<p>Measured timing spectra with and without the two-step FSI.</p> Full article ">Figure 14
<p>Measured timing resolution (<b>a</b>) without and (<b>b</b>) with the two-step FSI.</p> Full article ">
3291 KiB  
Article
Decoupling Control of Micromachined Spinning-Rotor Gyroscope with Electrostatic Suspension
by Boqian Sun, Shunyue Wang, Haixia Li and Xiaoxia He
Sensors 2016, 16(10), 1747; https://doi.org/10.3390/s16101747 - 20 Oct 2016
Cited by 11 | Viewed by 6189
Abstract
A micromachined gyroscope in which a high-speed spinning rotor is suspended electrostatically in a vacuum cavity usually functions as a dual-axis angular rate sensor. An inherent coupling error between the two sensing axes exists owing to the angular motion of the spinning rotor [...] Read more.
A micromachined gyroscope in which a high-speed spinning rotor is suspended electrostatically in a vacuum cavity usually functions as a dual-axis angular rate sensor. An inherent coupling error between the two sensing axes exists owing to the angular motion of the spinning rotor being controlled by a torque-rebalance loop. In this paper, a decoupling compensation method is proposed and investigated experimentally based on an electrostatically suspended micromachined gyroscope. In order to eliminate the negative spring effect inherent in the gyroscope dynamics, a stiffness compensation scheme was utilized in design of the decoupled rebalance loop to ensure loop stability and increase suspension stiffness. The experimental results show an overall stiffness increase of 30.3% after compensation. A decoupling method comprised of inner- and outer-loop decoupling compensators is proposed to minimize the cross-axis coupling error. The inner-loop decoupling compensator aims to attenuate the angular position coupling. The experimental frequency response shows a position coupling attenuation by 14.36 dB at 1 Hz. Moreover, the cross-axis coupling between the two angular rate output signals can be attenuated theoretically from −56.2 dB down to −102 dB by further appending the outer-loop decoupling compensator. The proposed dual-loop decoupling compensation algorithm could be applied to other dual-axis spinning-rotor gyroscopes with various suspension solutions. Full article
(This article belongs to the Section Physical Sensors)
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<p>Micromachined dual-axis spinning-rotor gyroscopes (MESG): (<b>a</b>) the exploded view of the device and (<b>b</b>) a fabricated device.</p> Full article ">Figure 2
<p>Block diagram of the rebalance loop without decoupling compensation.</p> Full article ">Figure 3
<p>Block diagram of the rebalance loop with full decoupling compensation.</p> Full article ">Figure 4
<p>Comparison of the cross-axis frequency responses with and without decoupling compensation. The calculation of 20 lg(<math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mi mathvariant="normal">x</mi> </msub> <mo>/</mo> <msub> <mover accent="true"> <mi>φ</mi> <mo>˙</mo> </mover> <mi mathvariant="normal">x</mi> </msub> </mrow> </semantics> </math>), 20 lg(<math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>dx</mi> </mrow> </msub> <mo>/</mo> <msub> <mover accent="true"> <mi>φ</mi> <mo>˙</mo> </mover> <mi mathvariant="normal">x</mi> </msub> </mrow> </semantics> </math>), and 20 lg(<math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>ox</mi> </mrow> </msub> <mo>/</mo> <msub> <mover accent="true"> <mi>φ</mi> <mo>˙</mo> </mover> <mi mathvariant="normal">x</mi> </msub> </mrow> </semantics> </math>) are based on <a href="#sensors-16-01747-f002" class="html-fig">Figure 2</a> and <a href="#sensors-16-01747-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>Block diagram of the rebalance loop in verification of the inner-loop decoupling compensation.</p> Full article ">Figure 6
<p>Simulated coupling error of the angular position that can be attenuated by the inner loop decoupling <b><span class="html-italic">D</span></b>(s) (denoted by I2) compared with the rebalance loop without decoupling (I1).</p> Full article ">Figure 7
<p>Block diagram of the rebalance loop in verification of the outer loop decoupling compensation.</p> Full article ">Figure 8
<p>Simulated cross-axis coupling of the gyroscope output responses with (O2) and without (O1) the outer loop decoupling compensator.</p> Full article ">Figure 9
<p>The MESG setup to test the decoupling compensation performance of the gyro rebalance loop.</p> Full article ">Figure 10
<p>Close-loop frequency responses with different stiffness compensations.</p> Full article ">Figure 11
<p>Experimental results of the suspension stiffness, which is improved by 30.3% with proper compensation.</p> Full article ">Figure 12
<p>Experimental results with the inner-loop compensation. The angular position coupling is reduced by 14.36 dB at 1 Hz and 8.58 dB at 10 Hz.</p> Full article ">
3865 KiB  
Article
A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors
by Minglin Wu, Sheng Zhang and Yuhan Dong
Sensors 2016, 16(10), 1746; https://doi.org/10.3390/s16101746 - 20 Oct 2016
Cited by 54 | Viewed by 6663
Abstract
In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data [...] Read more.
In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution) and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden) can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects. Full article
(This article belongs to the Section Physical Sensors)
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<p>Setup of the board. (<b>a</b>) The axis pointing of motion sensor; (<b>b</b>) The placement of the board in the car.</p> Full article ">Figure 2
<p>Driving map depicts a part of the covered routes in the data collection process.</p> Full article ">Figure 3
<p>Architecture of proposed driving behavior recognition system.</p> Full article ">Figure 4
<p>Physical model depicting car motion in the Northern Hemisphere.</p> Full article ">Figure 5
<p>Data change rule of different driving behaviors.</p> Full article ">Figure 6
<p>The actual 9-axis data portraying ACC.</p> Full article ">Figure 7
<p>The actual nine-axis data portraying LT.</p> Full article ">Figure 8
<p>Data comparison of <span class="html-italic">Y</span>-axis acceleration between normal and aggressive LT, RT and UT.</p> Full article ">Figure 9
<p>The performance of low pass and Kalman filters.</p> Full article ">Figure 10
<p>The performance of Kalman filter in filtering all nine-axis signals.</p> Full article ">Figure 11
<p>Four samples extracted depicting ACC.</p> Full article ">Figure 12
<p>The performances of different classifiers.</p> Full article ">
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