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Sensors, Volume 9, Issue 2 (February 2009) – 34 articles , Pages 696-1294

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201 KiB  
Article
A New Method for Node Fault Detection in Wireless Sensor Networks
by Peng Jiang
Sensors 2009, 9(2), 1282-1294; https://doi.org/10.3390/s90201282 - 24 Feb 2009
Cited by 163 | Viewed by 16180
Abstract
Wireless sensor networks (WSNs) are an important tool for monitoring distributed remote environments. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. It is well known that the distributed fault detection (DFD) scheme checks [...] Read more.
Wireless sensor networks (WSNs) are an important tool for monitoring distributed remote environments. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. It is well known that the distributed fault detection (DFD) scheme checks out the failed nodes by exchanging data and mutually testing among neighbor nodes in this network., but the fault detection accuracy of a DFD scheme would decrease rapidly when the number of neighbor nodes to be diagnosed is small and the node’s failure ratio is high. In this paper, an improved DFD scheme is proposed by defining new detection criteria. Simulation results demonstrate that the improved DFD scheme performs well in the above situation and can increase the fault detection accuracy greatly. Full article
(This article belongs to the Special Issue Wireless Sensor Technologies and Applications)
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<p>The sensor network with 200 randomly deployed nodes.</p>
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<p>The trend of node fault detection accuracy with various average numbers of neighbor nodes when 200 nodes are deployed and the node's failure ratio is 0.3.</p>
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<p>The trend of node fault detection accuracy with various node failure ratios when 200 nodes are randomly deployed and the average numbers of neighbor nodes is 5.</p>
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<p>The node fault detection accuracy with various node failure ratios when 200 nodes are randomly deployed and the average numbers of neighbor nodes is 10.</p>
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<p>The node fault detection accuracy with various node failure ratios when 100 nodes are randomly deployed and the average numbers of neighbor nodes is 10.</p>
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<p>The node fault detection accuracy with various node failure ratios when 200 nodes are randomly deployed and the average numbers of neighbor nodes is 5.</p>
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<p>The node fault detection accuracy with various node failure ratios when 50 nodes are randomly deployed and the average numbers of neighbor nodes is 5.</p>
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2970 KiB  
Review
Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review
by Weibao Zou, Yan Li, Zhilin Li and Xiaoli Ding
Sensors 2009, 9(2), 1259-1281; https://doi.org/10.3390/s90201259 - 24 Feb 2009
Cited by 21 | Viewed by 16327
Abstract
Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement [...] Read more.
Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR))
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<p>Imaging geometry of SAR interferometry.</p>
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<p>Imaging geometry of repeat-pass SAR interferometry.</p>
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<p>Imaging geometry of across-track SAR interferometry.</p>
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<p>Imaging geometry of along-track SAR interferometry.</p>
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<p>SAR image processing procedures.</p>
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<p>Procedures of InSAR image co-registration.</p>
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<p>A pair of SAR images (a) and (b) and its co-registered slave image. (a) a master image; (b) a slave image; (c) a co-registered slave image.</p>
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<p>Feature points extracted by different ways. (a) feature points extracted by Interest Operators; (b) feature points extracted by wavelet transform.</p>
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<p>Feature points extracted by different ways. (a) feature points extracted by Interest Operators; (b) feature points extracted by wavelet transform.</p>
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<p>A pair of SAR images (Tai Lam in Hong Kong). (a) the master image; (b) the slave image.</p>
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843 KiB  
Article
Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation
by Xiangguo Lin, Zhengjun Liu, Jixian Zhang and Jing Shen
Sensors 2009, 9(2), 1237-1258; https://doi.org/10.3390/s90201237 - 24 Feb 2009
Cited by 31 | Viewed by 10192
Abstract
In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extractroad features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting [...] Read more.
In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extractroad features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads’ geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms. Full article
(This article belongs to the Special Issue Sensor Algorithms)
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<p>Construction of the interlaced template and detection of the road markings. (a) A subset of color aerial image and the initialization of our road tracker; (b) the interlaced template of the road surface in (a); (c) a subset of a panchromatic satellite image; (d) the interlaced template of the road surface in (c); (e) the profile transformations of the ribbon road in (b) and there are four salient peaks representing the four lane markings on the road surface; (f) the profile transformations of the ribbon road in (d) and there is only one salient peak representing a marking line on the road surface.</p>
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<p>Construction of the interlaced template and detection of the road markings. (a) A subset of color aerial image and the initialization of our road tracker; (b) the interlaced template of the road surface in (a); (c) a subset of a panchromatic satellite image; (d) the interlaced template of the road surface in (c); (e) the profile transformations of the ribbon road in (b) and there are four salient peaks representing the four lane markings on the road surface; (f) the profile transformations of the ribbon road in (d) and there is only one salient peak representing a marking line on the road surface.</p>
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<p>Angular Texture Signature. (a) Texture is computed over a set of rectangular regions rotating around a road centerline point (note that there are 72 templates but only odd ones are displayed); (b) the graph of the ATS; (c) the graph of the PATS; (d) the PATS polygon of (c).</p>
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<p>Road network extraction from a SPOT5 satellite image. (a) SPOT5 fused image with 2.5 m pixel<sup>-1</sup> resolution and an image size of 3,750 pixels by 2,499 pixels; (b) the reference road network plotted; (c) the extracted vector road network by the combination strategy.</p>
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<p>Road network extraction from an IKONOS satellite image. (a) IKONOS fused image with 1 m pixel<sup>-1</sup> resolution and 9,374 pixels by 6,246 pixels image size; (b) the reference road network plotted; (c) the vector road network extracted by the combination strategy.</p>
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<p>Road network extraction from a QuickBird satellite image. (a) QuickBird fused image with 0.61 m pixel<sup>-1</sup> resolution and the image size is 15,368 pixels by 10,240 pixels; (b) the reference road network plotted; (c) the extracted vector road network by the combination strategy.</p>
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<p>Road network extraction from an airborne SAR image. (a) Raw SAR image with 0.3 m pixel-1 resolution and 23,999 pixels by 20,172 pixels image size; (b) the reference road network plotted; (c) The extracted vector road network by PATS.</p>
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<p>Road network extraction from a DMC airborne image. (a) DMC image with 0.2 m pixel<sup>-1</sup> resolution and a 12,428 pixels by 7,780 pixels image size; (b) the reference road network plotted; (c) the extracted vector road network by combination strategy.</p>
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<p>Typical roads on the images. (a) A subset of SPOT5 image in <a href="#f3-sensors-09-01237" class="html-fig">Figure 3(a); (b)</a> a subset of IKONOS image in <a href="#f4-sensors-09-01237" class="html-fig">Figure 4(a); (c)</a> a subset of QuickBird image in <a href="#f5-sensors-09-01237" class="html-fig">Figure 5(a); (d)</a> a subset of SAR image in <a href="#f6-sensors-09-01237" class="html-fig">Figure 6(a); (e)</a> a subset of DMC image in <a href="#f7-sensors-09-01237" class="html-fig">Figure 7(a)</a>.</p>
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1100 KiB  
Article
Improving Ship Detection with Polarimetric SAR based on Convolution between Co-polarization Channels
by Haiyan Li, Yijun He and Wenguang Wang
Sensors 2009, 9(2), 1221-1236; https://doi.org/10.3390/s90201221 - 24 Feb 2009
Cited by 20 | Viewed by 10560
Abstract
The convolution between co-polarization amplitude only data is studied to improve ship detection performance. The different statistical behaviors of ships and surrounding ocean are characterized a by two-dimensional convolution function (2D-CF) between different polarization channels. The convolution value of the ocean decreases relative [...] Read more.
The convolution between co-polarization amplitude only data is studied to improve ship detection performance. The different statistical behaviors of ships and surrounding ocean are characterized a by two-dimensional convolution function (2D-CF) between different polarization channels. The convolution value of the ocean decreases relative to initial data, while that of ships increases. Therefore the contrast of ships to ocean is increased. The opposite variation trend of ocean and ships can distinguish the high intensity ocean clutter from ships’ signatures. The new criterion can generally avoid mistaken detection by a constant false alarm rate detector. Our new ship detector is compared with other polarimetric approaches, and the results confirm the robustness of the proposed method. Full article
(This article belongs to the Special Issue Ocean Remote Sensing)
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<p>The simulated HH and VV polarization images.</p>
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<p>The effects of ships' size on 2D-CF. a, b, c and d are the 2D-CF values without ship signatures, the size of ship signatures are 4×4, 6×6 and 8×8 pixels, respectively. X and Y axes represent the number of pixels in range and azimuth direction. In all the following figures, the physical interpretation of X and Y axes is the same.</p>
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<p>The effects of sea state on 2D-CF. a and b are 2D-CF values between co-polarization channels, SNR is 1.1 in <a href="#f2-sensors-09-01221" class="html-fig">Fig2 a</a>. and 3.0 in <a href="#f2-sensors-09-01221" class="html-fig">Fig2 b</a>.</p>
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<p>The effects of ships' shape on convolution. a and b are gray images with ‘line’ and ‘T’ shape, respectively, c and d are 2D-CF values between co-polarization channels corresponding different ship shapes.</p>
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<p>The span image of SIR-C SAR on 4<sup>th</sup> Oct.1994.</p>
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<p>The HH,VV and HV polarization gray images of SIR-C/X data.</p>
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<p>The span image after convolution. 1-9 mark the targets with higher power than ocean clutter.</p>
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<p>Validating the results of convolution with polarimetric parameters. a, b, c and d show the images of polarimetric entropy <span class="html-italic">H</span>, scattering angle <span class="html-italic">α</span>, polarimetric degree <span class="html-italic">PD</span> and co- polarimetric phase difference <span class="html-italic">ϕ<sub>hh−vv</sub></span>.</p>
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<p>The correlation coefficient of co-polarization.</p>
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595 KiB  
Article
SITHON: An Airborne Fire Detection System Compliant with Operational Tactical Requirements
by Charalabos Kontoes, Iphigenia Keramitsoglou, Nicolaos Sifakis and Pavlos Konstantinidis
Sensors 2009, 9(2), 1204-1220; https://doi.org/10.3390/s90201204 - 24 Feb 2009
Cited by 9 | Viewed by 10017
Abstract
In response to the urging need of fire managers for timely information on fire location and extent, the SITHON system was developed. SITHON is a fully digital thermal imaging system, integrating INS/GPS and a digital camera, designed to provide timely positioned and projected [...] Read more.
In response to the urging need of fire managers for timely information on fire location and extent, the SITHON system was developed. SITHON is a fully digital thermal imaging system, integrating INS/GPS and a digital camera, designed to provide timely positioned and projected thermal images and video data streams rapidly integrated in the GIS operated by Crisis Control Centres. This article presents in detail the hardware and software components of SITHON, and demonstrates the first encouraging results of test flights over the Sithonia Peninsula in Northern Greece. It is envisaged that the SITHON system will be soon operated onboard various airborne platforms including fire brigade airplanes and helicopters as well as on UAV platforms owned and operated by the Greek Air Forces. Full article
(This article belongs to the Section Remote Sensors)
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<p>The CESSNA 310Q two-engined aircraft which is the platform of SITHON</p>
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<p>(a) 3-D calibration tests deployed in the Photogrammetry Laboratory (NTUA) and (b) a sample image taken during the calibration test. The black and white box indicates the effective area of measurements.</p>
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<p>The MIDG II GPS/INS device of Microbotics.</p>
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<p>The SITHON airborne imaging system configuration.</p>
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<p>(a) Camera opening at the bottom of the aircraft CESSNA 310Q using an inertial gyroscopic platform GSM300, (b) Design for camera fit to the camera opening at the bottom of the aircraft, (c) Camera and IMU devices rigidly attached.</p>
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<p>(a) S/W operations for sensor control and input raw data processing; (b) operations applied on the server PC station for data package decoding, image thresholding, and image geo-referencing.</p>
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<p>(a) S/W operations for sensor control and input raw data processing; (b) operations applied on the server PC station for data package decoding, image thresholding, and image geo-referencing.</p>
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<p>(a) The Sithonia peninsula study area over which the SITHON system demonstrations were carried out. (b) Pre-designed navigation paths inserted to aircraft's navigation control system for acquiring imagery with no gaps in terrain coverage.</p>
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<p>The operation of the SITHON airborne imaging system on May 24, 2006 over the Sithonia peninsula: (a) and (b) dynamic surface viewing and image stereoscopic acquisition with 60% image overlap, (c) a 3×3 m<sup>2</sup> burning surface detected from 2,000 m ASL and (d) a 3×3 m<sup>2</sup> burning surface detected from 1,000 m ASL</p>
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406 KiB  
Article
Flexible Electronics Sensors for Tactile Multi-Touching
by Wen-Yang Chang, Te-Hua Fang, Shao-Hsing Yeh and Yu-Cheng Lin
Sensors 2009, 9(2), 1188-1203; https://doi.org/10.3390/s9021188 - 24 Feb 2009
Cited by 52 | Viewed by 14139
Abstract
Flexible electronics sensors for tactile applications in multi-touch sensing and large scale manufacturing were designed and fabricated. The sensors are based on polyimide substrates, with thixotropy materials used to print organic resistances and a bump on the top polyimide layer. The gap between [...] Read more.
Flexible electronics sensors for tactile applications in multi-touch sensing and large scale manufacturing were designed and fabricated. The sensors are based on polyimide substrates, with thixotropy materials used to print organic resistances and a bump on the top polyimide layer. The gap between the bottom electrode layer and the resistance layer provides a buffer distance to reduce erroneous contact during large bending. Experimental results show that the top membrane with a bump protrusion and a resistance layer had a large deflection and a quick sensitive response. The bump and resistance layer provided a concentrated von Mises stress force and inertial force on the top membrane center. When the top membrane had no bump, it had a transient response delay time and took longer to reach steady-state. For printing thick structures of flexible electronics sensors, diffusion effects and dimensional shrinkages can be improved by using a paste material with a high viscosity. Linear algorithm matrixes with Gaussian elimination and control system scanning were used for multi-touch detection. Flexible electronics sensors were printed with a resistance thickness of about 32 µm and a bump thickness of about 0.2 mm. Feasibility studies show that printing technology is appropriate for large scale manufacturing, producing sensors at a low cost. Full article
(This article belongs to the Section Chemical Sensors)
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<p>Fabrication procedures of flexible electronics sensors for large area manufacturing using screen printing technology.</p>
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<p>Schematic circuit of a flexible array sensor for use in multi-touch sensing applications, (a) Control system frame, and (b) the equivalent circuit of row 1 when columns inputs are 1,0, …, 0.</p>
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<p>Membrane mechanical deflection of a flexible electronics sensor when a load was applied.</p>
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<p>Comparison of the structural characteristics. (a) The effects of membranes without bumps, with bumps, and with bumps and a resistance layer, and (b) the effects of bump widths and thicknesses of 100, 250, and 500 μm.</p>
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<p>Simulation of the optimal pixel dimensions of the flexible electronics sensor at a constant force of 5 N. (a) Deflection and (b) stress distribution of a membrane with bumps, and (c) deflection and (d) stress distribution of a membrane with bumps and a resistance layer.</p>
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<p>Array sensors of flexible electronics based on polyimide films for large area sensing. (a) Inside view between two PI films, including a resistance layer, posts, and electrodes, and (b) the bump structures on the top film after screen printing.</p>
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<p>The printing diffusion ratios of the bump and resistance materials compared with original width. X and Y are parallel to and perpendicular to the printing directions, respectively.</p>
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<p>Comparison of the dynamic response effects of the membrane without bump, with bumps, and with bumps and a resistance layer.</p>
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<p>Characteristics output of flexible electronics sensors. (a) Voltage versus force at an operating frequency of 1 kHz at various pixels, and (b) steady-state output voltage response of the membrane with a bump and with bump and resistance layers at various temperatures.</p>
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1871 KiB  
Article
Detection of Neolithic Settlements in Thessaly (Greece) Through Multispectral and Hyperspectral Satellite Imagery
by Dimitrios Alexakis, Apostolos Sarris, Theodoros Astaras and Konstantinos Albanakis
Sensors 2009, 9(2), 1167-1187; https://doi.org/10.3390/s90201167 - 23 Feb 2009
Cited by 75 | Viewed by 14497
Abstract
Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 – 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate [...] Read more.
Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 – 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-referenced through systematic GPS surveying throughout the region. Data from four primary sensors were used, namely Landsat ETM, ASTER, EO1 - HYPERION and IKONOS. A range of image processing techniques were originally applied to the hyperspectral imagery in order to detect the settlements and validate the results of GPS surveying. Although specific difficulties were encountered in the automatic classification of archaeological features composed by a similar parent material with the surrounding landscape, the results of the research suggested a different response of each sensor to the detection of the Neolithic settlements, according to their spectral and spatial resolution. Full article
(This article belongs to the Section Remote Sensors)
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<p>The region of Thessaly is located at the center of the mainland of Greece (Top Left). Most of the magoules are distributed within the limits of the plains of Larisa and Karditsa (Top Right). Details of the magoula of Kastro (Bottom Left). Details of the magoula of Kalo Nero (Bottom Right).</p>
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<p>(a) Mosaic of ASTER images; (b) Mosaic of IKONOS images; (c) Landsat Image; (d) HYPERION image; (e) Mosaic of airphotos.</p>
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<p>RGB→3,2,5 of ASTER image –Melisa Settlement 1 (left). RGB→2,3,7 of ASTER image – Melisa Settlement 1 (right).</p>
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<p>Appearance of the Orfana settlement on the ASTER image (RGB→1,2,3) with acquisition date of 19-03-2003 (left). Right: Appearance of the same settlement on 30- 06- 2004 (right).</p>
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<p>IKONOS image. RGB – 321 - Melissa 1 Settlement (left). Melia 2 Settlement –Airphoto image (right).</p>
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<p>Comparison of spectral signatures of all the sensors from the Neolithic settlements collected from the plains of Thessaly.</p>
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<p>Classification map from the spectral signatures of ASTER images.</p>
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<p>Appearance of three settlements to the first Principal Component of ASTER image (left). Appearance of three settlements to the second Principal Component of ASTER image (middle). Bottom Appearance of three settlements to the third Principal Component of ASTER image (right).</p>
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<p>Settlement Moshohori represented in an IKONOS image (left) and the same region after image fusion between IKONOS and HYPERION (right).</p>
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4476 KiB  
Article
Piezoresistive Sensitivity, Linearity and Resistance Time Drift of Polysilicon Nanofilms with Different Deposition Temperatures
by Changzhi Shi, Xiaowei Liu and Rongyan Chuai
Sensors 2009, 9(2), 1141-1166; https://doi.org/10.3390/s90201141 - 23 Feb 2009
Cited by 25 | Viewed by 14846
Abstract
Our previous research work indicated that highly boron doped polysilicon nanofilms (≤100 nm in thickness) have higher gauge factor (the maximum is ~34 for 80 nm-thick films) and better temperature stability than common polysilicon films (≥ 200nm in thickness) at the same doping [...] Read more.
Our previous research work indicated that highly boron doped polysilicon nanofilms (≤100 nm in thickness) have higher gauge factor (the maximum is ~34 for 80 nm-thick films) and better temperature stability than common polysilicon films (≥ 200nm in thickness) at the same doping levels. Therefore, in order to further analyze the influence of deposition temperature on the film structure and piezoresistance performance, the piezoresistive sensitivity, piezoresistive linearity (PRL) and resistance time drift (RTD) of 80 nm-thick highly boron doped polysilicon nanofilms (PSNFs) with different deposition temperatures were studied here. The tunneling piezoresistive model was established to explain the relationship between the measured gauge factors (GFs) and deposition temperature. It was seen that the piezoresistance coefficient (PRC) of composite grain boundaries is higher than that of grains and the magnitude of GF is dependent on the resistivity of grain boundary (GB) barriers and the weight of the resistivity of composite GBs in the film resistivity. In the investigations on PRL and RTD, the interstitial-vacancy (IV) model was established to model GBs as the accumulation of IV pairs. And the recrystallization of metastable IV pairs caused by material deformation or current excitation is considered as the prime reason for piezoresistive nonlinearity (PRNL) and RTD. Finally, the optimal deposition temperature for the improvement of film performance and reliability is about 620 °C and the high temperature annealing is not very effective in improving the piezoresistive performance of PSNFs deposited at lower temperatures. Full article
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<p>SEM and TEM images of PSNFs deposited at different temperatures.</p>
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<p>XRD spectra of PSNF samples deposited at different temperatures.</p>
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<p>Schematic diagram of mask plates and sample wafers in the fabrication of cantilever beams. (a) The mask plate for patterning resistors. (b) The sample wafer after patterning resistors. (c) The mask plate for patterning electrodes and calibrated scales. (d) The sample wafer and the cantilever beam after fabricating electrodes and calibrated scales.</p>
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<p>Photograph of an actual cantilever beam sample.</p>
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<p>Schematic diagram of test setup for measuring gauge factor.</p>
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<p>Test system of RTD properties.</p>
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<p>Energy band structure and carrier transport mechanisms near grain boundaries.</p>
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<p>Energy band diagram and tunneling mechanism of GB barrier omitting DRBs.</p>
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<p>Distribution curve of <span class="html-italic">V<sub>δ</sub></span> normalized to <span class="html-italic">V</span><sub>0</sub> as a function of <span class="html-italic">N<sub>A</sub></span>. The trap density <span class="html-italic">N<sub>t</sub></span> is taken to be 1.0×10<sup>13</sup> cm<sup>-2</sup>. The voltage drop <span class="html-italic">V</span><sub>0</sub> on each composite GB is set to be 1mV.</p>
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271 KiB  
Article
An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies
by Xiaohuan Yang, Yaohuan Huang, Pinliang Dong, Dong Jiang and Honghui Liu
Sensors 2009, 9(2), 1128-1140; https://doi.org/10.3390/s90201128 - 20 Feb 2009
Cited by 52 | Viewed by 14301
Abstract
The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC [...] Read more.
The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable. Full article
(This article belongs to the Section Remote Sensors)
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<p>Flowchart for producing Gridded Population Dataset of China.</p>
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<p>Data process flow of SPUS.</p>
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<p>Land use data of Shandong Province, 2002.</p>
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<p>Framework of function modules.</p>
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<p>500 m by 500 m gridded population data of Shandong Peninsula in 2002.</p>
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1125 KiB  
Article
3D Digital Surveying and Modelling of Cave Geometry: Application to Paleolithic Rock Art
by Diego González-Aguilera, Angel Muñoz-Nieto, Javier Gómez-Lahoz, Jesus Herrero-Pascual and Gabriel Gutierrez-Alonso
Sensors 2009, 9(2), 1108-1127; https://doi.org/10.3390/s90201108 - 20 Feb 2009
Cited by 64 | Viewed by 15351
Abstract
3D digital surveying and modelling of cave geometry represents a relevant approach for research, management and preservation of our cultural and geological legacy. In this paper, a multi-sensor approach based on a terrestrial laser scanner, a high-resolution digital camera and a total station [...] Read more.
3D digital surveying and modelling of cave geometry represents a relevant approach for research, management and preservation of our cultural and geological legacy. In this paper, a multi-sensor approach based on a terrestrial laser scanner, a high-resolution digital camera and a total station is presented. Two emblematic caves of Paleolithic human occupation and situated in northern Spain, “Las Caldas” and “Peña de Candamo”, have been chosen to put in practise this approach. As a result, an integral and multi-scalable 3D model is generated which may allow other scientists, pre-historians, geologists…, to work on two different levels, integrating different Paleolithic Art datasets: (1) a basic level based on the accurate and metric support provided by the laser scanner; and (2) a advanced level using the range and image-based modelling. Full article
(This article belongs to the Section Remote Sensors)
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<p>Multi-sensor approach applied at caves.</p>
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<p>Location map: Nalon river middle valley. Main Paleolithic archaeological sites and rock-art stations.</p>
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<p>Detailed 3D hybrid model together with vector scale drawings (red rectangles) extracted from this model. Close-up of the 3D model (blue rectangle).</p>
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<p>Left: Top view of the laser model at “Las Caldas” cave. Centre: Vector floor plan extracted from laser scanner dataset. Right: Inaccessible galleries recorded with TLS.</p>
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<p>Laser surface model obtained from artificial shading (top) and contour map (down) extracted from this model at “Peña de Candamo” cave.</p>
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764 KiB  
Article
Enhanced Photoelectrochemical Detection of Bioaffinity Reactions by Vertically Oriented Au Nanobranches Complexed with a Biotinylated Polythiophene Derivative
by Huiqiong Zhou, Yanli Tang, Jin Zhai, Shu Wang, Zhiyong Tang and Lei Jiang
Sensors 2009, 9(2), 1094-1107; https://doi.org/10.3390/s90201094 - 19 Feb 2009
Cited by 16 | Viewed by 10469
Abstract
Four nanostructured Au electrodes were prepared by a simple and templateless electrochemical deposition technique. After complexing with a biotinylated polythiophene derivative (PTBL), photocurrent generation and performance of PTBL/Au-nanostructured electrodes as photoelectrochemical biosensors were investigated. Among these four nanostructured Au electrodes, vertically oriented nanobranches [...] Read more.
Four nanostructured Au electrodes were prepared by a simple and templateless electrochemical deposition technique. After complexing with a biotinylated polythiophene derivative (PTBL), photocurrent generation and performance of PTBL/Au-nanostructured electrodes as photoelectrochemical biosensors were investigated. Among these four nanostructured Au electrodes, vertically oriented nanobranches on the electrode significantly improved the photoelectric conversion, because the vertically oriented nanostructures not only benefit light harvesting but also the transfer of the photogenerated charge carriers. Owing to this advantaged nanostructure, the PTBL/Au-nanobranch electrode showed higher sensitivity and faster response times in the photoelectrochemical detection of a streptavidin-biotin affinity reaction compared to a PTBL/Au-nanoparticle electrode. Full article
(This article belongs to the Section Biosensors)
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Graphical abstract

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<p>AFM image of Au nanoparticle surface formed by sputtering. The diameters of the nanoparticles were around 3 nm.</p>
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<p>SEM images of nanostructured Au electrodes by electrochemical deposition at -0.2 V for a) 400, b) 800, c) 1200 s; The inset in b) is side view from 90°, and other SEM images are side views from 45°, all scale bars are 500 nm; d) XRD patterns of Au nanobranch electrode fabricated at -0.2 V for 800 s.</p>
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<p>Distribution of angles, diameters and heights of Au nanostructures electrodeposited at -0.2 V for different time intervals: (a, b, c) 400 s, (d, e, f) 800 s, (g, h, i) 1200 s.</p>
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<p>SEM images of nanostructured Au electrodes by electrochemical deposition at <b>a)</b> -0.1, <b>b)</b> -0.3, <b>c)</b> -0.4 V for 800 s. All SEM images are side view from 45°, all scale bars are 500 nm.</p>
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<p>TEM image of a single Au nanobranch fabricated at -0.2 V for 800 s. The inset is the selected area diffraction pattern taken from [112] direction of Au nanobranch.</p>
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<p>SEM image of PTBL cast on Au nanobranch surface, which prepared by electrochemical deposition at -0.2 V for 800 s.</p>
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<p>a) Photocurrent of PTBL/Au-nanostructured electrodes in PBS buffer solution by irradiation with 110 mW cm<sup>-2</sup> of white light at a bias of 0 V; b) reflection spectra of four nanostructured Au electrodes and absorption spectrum of PTBL in DMF solvent; curves NPs, 400 s, 800 s, 1200 s represent the nanostructured Au electrodes prepared by deposition at -0.2 V for 0, 400, 800, and 1,200 s respectively.</p>
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<p>Dependence of bias potential (<span class="html-italic">E</span>) on photocurrent (<span class="html-italic">j<sub>ph</sub></span>) of the PTBL/Au-nanostructured electrodes, curves NPs, 400 s, 800 s, 1200 s represent the nanostructured Au electrodes prepared by deposition at -0.2 V for 0, 400, 800, and 1200 s respectively.</p>
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<p>Energy level diagram of PTBL/Au. The band gap of PTBL was evaluated to be 2.5 eV from the absorption edge of the absorption peak of PTBL in <a href="#f7-sensors-09-01094" class="html-fig">figure 7b</a>. The relative positions of the levels of PTBL and Au were adopted from reference [<a href="#b38-sensors-09-01094" class="html-bibr">38</a>].</p>
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5246 KiB  
Article
Error in Radar-Derived Soil Moisture due to Roughness Parameterization: An Analysis Based on Synthetical Surface Profiles
by Hans Lievens, Hilde Vernieuwe, Jesús Álvarez-Mozos, Bernard De Baets and Niko E.C. Verhoest
Sensors 2009, 9(2), 1067-1093; https://doi.org/10.3390/s90201067 - 17 Feb 2009
Cited by 67 | Viewed by 12252
Abstract
In the past decades, many studies on soil moisture retrieval from SAR demonstrated a poor correlation between the top layer soil moisture content and observed backscatter coefficients, which mainly has been attributed to difficulties involved in the parameterization of surface roughness. The present [...] Read more.
In the past decades, many studies on soil moisture retrieval from SAR demonstrated a poor correlation between the top layer soil moisture content and observed backscatter coefficients, which mainly has been attributed to difficulties involved in the parameterization of surface roughness. The present paper describes a theoretical study, performed on synthetical surface profiles, which investigates how errors on roughness parameters are introduced by standard measurement techniques, and how they will propagate through the commonly used Integral Equation Model (IEM) into a corresponding soil moisture retrieval error for some of the currently most used SAR configurations. Key aspects influencing the error on the roughness parameterization and consequently on soil moisture retrieval are: the length of the surface profile, the number of profile measurements, the horizontal and vertical accuracy of profile measurements and the removal of trends along profiles. Moreover, it is found that soil moisture retrieval with C-band configuration generally is less sensitive to inaccuracies in roughness parameterization than retrieval with L-band configuration. Full article
(This article belongs to the Section Remote Sensors)
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<p>Backscatter coefficients calculated for different values of RMS height and correlation length and a moisture content of 25 vol% for (a) an ASAR VV configuration and (b) a PALSAR HH configuration.</p>
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<p>Backscatter coefficients calculated for different values of RMS height and correlation length and a moisture content of 25 vol% for (a) an ASAR VV configuration and (b) a PALSAR HH configuration.</p>
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<p>Sensitivity of the soil moisture retrieval to RMS height (vol%/cm) for an ASAR VV configuration and a moisture content of (a) 5 vol%, (b) 15 vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to RMS height (vol%/cm) for an ASAR VV configuration and a moisture content of (a) 5 vol%, (b) 15 vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to RMS height (vol%/cm) for an ASAR VV configuration and a moisture content of (a) 5 vol%, (b) 15 vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to RMS height (vol%/cm) for an ASAR VV configuration and a moisture content of (a) 5 vol%, (b) 15 vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to correlation length (vol%/cm) for an ASAR VV configuration and a moisture content of (a) 5vol%, (b) 15vol%, (c) 25vol%, and (d) 35vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to correlation length (vol%/cm) for an ASAR VV configuration and a moisture content of (a) 5vol%, (b) 15vol%, (c) 25vol%, and (d) 35vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to correlation length (vol%/cm) for an ASAR VV configuration and a moisture content of (a) 5vol%, (b) 15vol%, (c) 25vol%, and (d) 35vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to correlation length (vol%/cm) for an ASAR VV configuration and a moisture content of (a) 5vol%, (b) 15vol%, (c) 25vol%, and (d) 35vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to RMS height (vol%/cm) for a PALSAR HH configuration and a moisture content of (a) 5 vol%, (b) 15 vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to RMS height (vol%/cm) for a PALSAR HH configuration and a moisture content of (a) 5 vol%, (b) 15 vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to RMS height (vol%/cm) for a PALSAR HH configuration and a moisture content of (a) 5 vol%, (b) 15 vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to RMS height (vol%/cm) for a PALSAR HH configuration and a moisture content of (a) 5 vol%, (b) 15 vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to correlation length (vol%/cm) for a PALSAR HH configuration and a moisture content of (a) 5vol%, (b) 15vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to correlation length (vol%/cm) for a PALSAR HH configuration and a moisture content of (a) 5vol%, (b) 15vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to correlation length (vol%/cm) for a PALSAR HH configuration and a moisture content of (a) 5vol%, (b) 15vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Sensitivity of the soil moisture retrieval to correlation length (vol%/cm) for a PALSAR HH configuration and a moisture content of (a) 5vol%, (b) 15vol%, (c) 25 vol%, and (d) 35 vol%.</p>
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<p>Mean and standard deviations of RMS height and correlation length for different profile lengths, sampled from large profiles with (a) (<span class="html-italic">s,l</span>) = (1 cm,5 cm) and (b) (<span class="html-italic">s,l</span>) = (1 cm,40 cm).</p>
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<p>Mean and standard deviations of RMS height and correlation length for different profile lengths, sampled from large profiles with (a) (<span class="html-italic">s,l</span>) = (1 cm,5 cm) and (b) (<span class="html-italic">s,l</span>) = (1 cm,40 cm).</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different profile lengths. Inverted soil moisture contents are derived using roughness parameters from sampled profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (2 cm,5 cm), (c), (d) (1 cm,5 cm), (e), (f) (2 cm,40 cm) and (g), (h) (1 cm,40 cm), and initial moisture contents of (a), (c), (e), (g) 5 vol% and (b), (d), (f), (h) 25 vol%.</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different profile lengths. Inverted soil moisture contents are derived using roughness parameters from sampled profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (2 cm,5 cm), (c), (d) (1 cm,5 cm), (e), (f) (2 cm,40 cm) and (g), (h) (1 cm,40 cm), and initial moisture contents of (a), (c), (e), (g) 5 vol% and (b), (d), (f), (h) 25 vol%.</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different profile lengths. Inverted soil moisture contents are derived using roughness parameters from sampled profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (2 cm,5 cm), (c), (d) (1 cm,5 cm), (e), (f) (2 cm,40 cm) and (g), (h) (1 cm,40 cm), and initial moisture contents of (a), (c), (e), (g) 5 vol% and (b), (d), (f), (h) 25 vol%.</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different profile lengths. Inverted soil moisture contents are derived using roughness parameters from sampled profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (2 cm,5 cm), (c), (d) (1 cm,5 cm), (e), (f) (2 cm,40 cm) and (g), (h) (1 cm,40 cm), and initial moisture contents of (a), (c), (e), (g) 5 vol% and (b), (d), (f), (h) 25 vol%.</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different profile lengths. Inverted soil moisture contents are derived using roughness parameters from sampled profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (2 cm,5 cm), (c), (d) (1 cm,5 cm), (e), (f) (2 cm,40 cm) and (g), (h) (1 cm,40 cm), and initial moisture contents of (a), (c), (e), (g) 5 vol% and (b), (d), (f), (h) 25 vol%.</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different profile lengths. Inverted soil moisture contents are derived using roughness parameters from sampled profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (2 cm,5 cm), (c), (d) (1 cm,5 cm), (e), (f) (2 cm,40 cm) and (g), (h) (1 cm,40 cm), and initial moisture contents of (a), (c), (e), (g) 5 vol% and (b), (d), (f), (h) 25 vol%.</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different profile lengths. Inverted soil moisture contents are derived using roughness parameters from sampled profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (2 cm,5 cm), (c), (d) (1 cm,5 cm), (e), (f) (2 cm,40 cm) and (g), (h) (1 cm,40 cm), and initial moisture contents of (a), (c), (e), (g) 5 vol% and (b), (d), (f), (h) 25 vol%.</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different profile lengths. Inverted soil moisture contents are derived using roughness parameters from sampled profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (2 cm,5 cm), (c), (d) (1 cm,5 cm), (e), (f) (2 cm,40 cm) and (g), (h) (1 cm,40 cm), and initial moisture contents of (a), (c), (e), (g) 5 vol% and (b), (d), (f), (h) 25 vol%.</p>
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<p>Number of profiles required to obtain a standard deviation of RMS height or correlation length less than 10% of the mean for different profile lengths. Sampled profiles originate from large profiles with (<span class="html-italic">s,l</span>) equal to (a) (1 cm,5 cm) and (b) (1 cm,40 cm).</p>
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<p>Number of profiles required to obtain a standard deviation of RMS height or correlation length less than 10% of the mean for different profile lengths. Sampled profiles originate from large profiles with (<span class="html-italic">s,l</span>) equal to (a) (1 cm,5 cm) and (b) (1 cm,40 cm).</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different numbers of profiles used. Inverted soil moisture contents are derived using roughness parameter series from sampled 4-m profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (1 cm,5 cm) and (c), (d) (1cm,40 cm), and for (a), (c) ASAR VV and (b), (d) PALSAR HH. Considered initial moisture contents are 5 vol% (crosses), 15 vol% (circles), 25 vol% (stars) and 35 vol% (squares).</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different numbers of profiles used. Inverted soil moisture contents are derived using roughness parameter series from sampled 4-m profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (1 cm,5 cm) and (c), (d) (1cm,40 cm), and for (a), (c) ASAR VV and (b), (d) PALSAR HH. Considered initial moisture contents are 5 vol% (crosses), 15 vol% (circles), 25 vol% (stars) and 35 vol% (squares).</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different numbers of profiles used. Inverted soil moisture contents are derived using roughness parameter series from sampled 4-m profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (1 cm,5 cm) and (c), (d) (1cm,40 cm), and for (a), (c) ASAR VV and (b), (d) PALSAR HH. Considered initial moisture contents are 5 vol% (crosses), 15 vol% (circles), 25 vol% (stars) and 35 vol% (squares).</p>
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<p>Mean and standard deviations of inverted soil moisture contents for different numbers of profiles used. Inverted soil moisture contents are derived using roughness parameter series from sampled 4-m profiles, originating from large profiles with (<span class="html-italic">s,l</span>) equal to (a), (b) (1 cm,5 cm) and (c), (d) (1cm,40 cm), and for (a), (c) ASAR VV and (b), (d) PALSAR HH. Considered initial moisture contents are 5 vol% (crosses), 15 vol% (circles), 25 vol% (stars) and 35 vol% (squares).</p>
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977 KiB  
Article
Discrepancy Between ASTER- and MODIS- Derived Land Surface Temperatures: Terrain Effects
by Yuanbo Liu, Yousuke Noumi and Yasushi Yamaguchi
Sensors 2009, 9(2), 1054-1066; https://doi.org/10.3390/s90201054 - 17 Feb 2009
Cited by 14 | Viewed by 11104
Abstract
The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have [...] Read more.
The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan)
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<p>(a) The false color image and (b) DEM of ASTER (RGB: band 132) for the study area, dated on 8 June 2004.</p>
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<p>(a) The ASTER LST image and (b) MODIS image for the study area, dated on 8 June 2004.</p>
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<p>(a) ASTER LST versus original MODIS LST, (b) spatial distribution of the ASTER-to-MODIS LST discrepancy, (c) local slope versus ASTER-to-MODIS LST, and (d) angle of emitted radiance versus ASTER-to-MODIS LST, for the study area.</p>
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<p>(a) ASTER LST versus the rectified MODIS LST, (b) spatial distribution of ASTER-to-MODIS LST discrepancy, (c) local slope versus ASTER-to-rectified MODIS LST, and (d) angle of emitted radiance versus ASTER-to-rectified MODIS LST.</p>
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<p>(a) ASTER LST versus rectified MODIS LST with terrain correction, (b) spatial distribution of ASTER-to-MODIS LST discrepancy, (c) local slope versus ASTER-to-MODIS LST, and (d) angle of emitted radiance versus ASTER-to-MODIS LST.</p>
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<p>(a) ASTER narrowband emissivity versus MODIS emissivity (band 31), (b) local slope versus ASTER-to-MODIS emissivity, (c) angle of emitted radiance versus ASTER-to-MODIS emissivity, (d) MODIS-to-ASTER emissivity versus LST without any correction, (e) MODIS-to-ASTER emissivity versus LST rectified with GSW based approach, and (f) MODIS-to-ASTER emissivity versus the rectified LST with terrain correction.</p>
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651 KiB  
Review
Recent Advances in Nanotechnology Applied to Biosensors
by Xueqing Zhang, Qin Guo and Daxiang Cui
Sensors 2009, 9(2), 1033-1053; https://doi.org/10.3390/s90201033 - 17 Feb 2009
Cited by 328 | Viewed by 26171
Abstract
In recent years there has been great progress the application of nanomaterials in biosensors. The importance of these to the fundamental development of biosensors has been recognized. In particular, nanomaterials such as gold nanoparticles, carbon nanotubes, magnetic nanoparticles and quantum dots have been [...] Read more.
In recent years there has been great progress the application of nanomaterials in biosensors. The importance of these to the fundamental development of biosensors has been recognized. In particular, nanomaterials such as gold nanoparticles, carbon nanotubes, magnetic nanoparticles and quantum dots have been being actively investigated for their applications in biosensors, which have become a new interdisciplinary frontier between biological detection and material science. Here we review some of the main advances in this field over the past few years, explore the application prospects, and discuss the issues, approaches, and challenges, with the aim of stimulating a broader interest in developing nanomaterial-based biosensors and improving their applications in disease diagnosis and food safety examination. Full article
(This article belongs to the Section Chemical Sensors)
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<p>Absorption spectra illustrating the protamine-induced aggregation and heparin-driven de-aggregation of AuNPs. (a) AuNPs alone; (b, c) after the addition of protamine: (b) 0.7 μg/ml and (c) 1.6 μg/ml; (d) after the addition of heparin (10.2 μg/mL). Inset shows the corresponding colorimetric response [<a href="#b14-sensors-09-01033" class="html-bibr">14</a>].</p>
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<p>AuNPs colorimetric strategy for thrombin detection [<a href="#b16-sensors-09-01033" class="html-bibr">16</a>].</p>
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<p>The immunoassay procedure of GNPs/PDCNTs modified immunosensor using HRP–GNPs–Ab<sub>2</sub> conjugates as label [<a href="#b24-sensors-09-01033" class="html-bibr">24</a>].</p>
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<p>Schematic of the construction of type A and type B sensors. (A) Fabrication of type A sensors in which a film of SWNTs was first cast onto a bare glassy carbon electrode and allowed to dry, before an alquot of the redox hydrogel was cast on top of the SWNT-coated electrode. (B) Fabrication of type B sensors in which SWNTs were first incubated with an enzyme solution before they were incorporated into the redox hydrogel. An aliquot of the redox hydrogel solution containing the enzyme-modified SWNTs was then cast on top of a bare glassy carbon electrode [<a href="#b31-sensors-09-01033" class="html-bibr">31</a>].</p>
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<p>Electrochemical characterization of glucose oxidase sensors. (A) Cyclic voltammograms of a GCE modified with the redox hydrogel alone (-); a GCE modified first with a film of SWNT and then coated with the redox hydrogel (----) (type A sensor); (III) a GCE modified with a redox hydrogel containing GOX-treated SWNTs (-) (type B sensor). Scan rate 50 mV/s. (B) Glucose calibration curves for the three types of sensors described in (A). T = 25C, E = 0.5 V vs SCE. Values are mean ±SEM [<a href="#b31-sensors-09-01033" class="html-bibr">31</a>].</p>
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<p>Surface functionalization of CNT (or QD) with oligonucleotide/Angibody (Ab), forming CNT-DNA (or -Ab) probe and QD-DNA (or-Ab) probe, and subsequent addition of target oligonucleotide (or Antigen) to form CNT-QD assembly. The unbound QD probe was obtained by simple centrifugation separation and the supernatant fluorescence intensity of QDs was monitored by spectrofluorometer. (System 1) Formation of CNT-QD hybrid in the presence of complementary DNA target; (System 2) Three-component CNT-QD system with the purpose to detect three different DNA target simultaneously; (System 3) CNT-QD protein detection system based on antigen-antibody immunoreactions [<a href="#b44-sensors-09-01033" class="html-bibr">44</a>].</p>
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<p>(a) Basic design of QD biosensors based on F0F1-ATPase: (1) antibody of β-subunit; (2) the antibody of MHV68; (3) MHV68; (4) the antibody of H9 avian influenza virus; (5) H9 avian influenza virus; (6) CdTe QDs with emission wavelength at 585 nm; (7) CdTe QDs with emission wavelength at 535 nm; (8) F0F1-ATPase within chromatophores; (9) chromatophores. (b) Changes of fluorescence intensity of QD biosensors with and without viruses. Curve a: The changes of fluorescence intensity of orange QD biosensors without MHV68 when the ADP is added to initialize reaction. Curve b: The changes of fluorescence intensity of green QD biosensors without H9 avian influenza virus when the ADP is added to initialize reaction. Curve c:The changes of fluorescence intensity of orange QD biosensors with capturing MHV68 when the ADP is added to initialize reaction. Curve d: The changes of fluorescence intensity of green QD biosensors with capturing H9 avian influenza virus when the ADP is added to initialize reaction [<a href="#b70-sensors-09-01033" class="html-bibr">70</a>].</p>
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<p>SEM images of as-prepared porous nanosheet-based ZnO microsphere with low (left) and high magnification (right) [<a href="#b83-sensors-09-01033" class="html-bibr">83</a>].</p>
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972 KiB  
Article
Efficient Chemical Sensing by Coupled Slot SOI Waveguides
by Vittorio M. N. Passaro, Francesco Dell’Olio, Caterina Ciminelli and Mario N. Armenise
Sensors 2009, 9(2), 1012-1032; https://doi.org/10.3390/s90201012 - 16 Feb 2009
Cited by 70 | Viewed by 13234
Abstract
A guided-wave chemical sensor for the detection of environmental pollutants or biochemical substances has been designed. The sensor is based on an asymmetric directional coupler employing slot optical waveguides. The use of a nanometer guiding structure where optical mode is confined in a [...] Read more.
A guided-wave chemical sensor for the detection of environmental pollutants or biochemical substances has been designed. The sensor is based on an asymmetric directional coupler employing slot optical waveguides. The use of a nanometer guiding structure where optical mode is confined in a low-index region permits a very compact sensor (device area about 1200 μm2) to be realized, having the minimum detectable refractive index change as low as 10-5. Silicon-on-Insulator technology has been assumed in sensor design and a very accurate modelling procedure based on Finite Element Method and Coupled Mode Theory has been pointed out. Sensor design and optimization have allowed a very good trade-off between device length and sensitivity. Expected device sensitivity to glucose concentration change in an aqueous solution is of the order of 0.1 g/L. Full article
(This article belongs to the Special Issue Nanotechnological Advances in Biosensors)
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<p>(a) Slot waveguide typical structure (BOX: Buried Silicon Oxide). (b) Profiles of quasi-TE and quasi-TM modes confined in a slot waveguide.</p>
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<p>Architecture of proposed integrated optical sensor.</p>
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<p>Confinement factor in a circular region versus <span class="html-italic">R</span> (cover medium: Teflon or aqueous solution).</p>
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<p>Main electric field component distribution related to supermodes supported by the asymmetrical directional coupler (<span class="html-italic">d</span> = 1 μm). (a) Quasi-TE symmetric supermode. (b) Quasi-TE antisymmetric supermode. (c) Quasi-TM symmetric supermode. (d) Quasi-TM antisymmetric supermode.</p>
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<p>Quasi-TE mode optical propagation in the designed asymmetrical directional coupler (<span class="html-italic">d</span> = 1 μm). Optical field intensity plot obtained by EME method.</p>
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<p>Optical intensity (quasi-TE mode by EME method) distribution in the cross-section of asymmetrical directional coupler (<span class="html-italic">d</span> = 1 μm) at: (a) <span class="html-italic">z</span> = 23.5 μm; (b) <span class="html-italic">z</span> = 47 μm.</p>
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<p>Normalized optical power confined in the two coupled waveguides (with <span class="html-italic">d</span> = 1 μm) versus propagation length for quasi-TE mode (Solid curves: modelling technique of this paper based on CMT and FEM; Dashed curves: results by 3D EME method).</p>
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<p>Dependence of <span class="html-italic">β<sub>c</sub></span> on <span class="html-italic">n<sub>cs</sub></span>: a) <span class="html-italic">d</span> = 0.8 μm; b) <span class="html-italic">d</span> = 1 μm; c) <span class="html-italic">d</span> = 1.2 μm.</p>
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<p><span class="html-italic">δ</span> as a function of <span class="html-italic">n<sub>cs</sub></span> for quasi-TE and quasi-TM modes.</p>
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505 KiB  
Article
Apparent Thixotropic Properties of Saline/Glycerol Drops with Biotinylated Antibodies on Streptavidin-Coated Glass Slides: Implications for Bacterial Capture on Antibody Microarrays
by David M. Albin, Andrew G. Gehring, Sue A. Reed and Shu-I Tu
Sensors 2009, 9(2), 995-1011; https://doi.org/10.3390/s90200995 - 16 Feb 2009
Cited by 2 | Viewed by 10702
Abstract
The thixotropic-like properties of saline/glycerol drops, containing biotinylated capture antibodies, on streptavidin-coated glass slides have been investigated, along with their implications for bacterial detection in a fluorescent microarray immunoassay. The thixotropic-like nature of 60:40 saline-glycerol semisolid droplets (with differing amounts of antibodies) was [...] Read more.
The thixotropic-like properties of saline/glycerol drops, containing biotinylated capture antibodies, on streptavidin-coated glass slides have been investigated, along with their implications for bacterial detection in a fluorescent microarray immunoassay. The thixotropic-like nature of 60:40 saline-glycerol semisolid droplets (with differing amounts of antibodies) was observed when bacteria were captured, and their presence detected using a fluorescently-labeled antibody. Semisolid, gel-like drops of biotinylated capture antibody became liquefied and moved, and then returned to semisolid state, during the normal immunoassay procedures for bacterial capture and detection. Streaking patterns were observed that indicated thixotropic-like characteristics, and this appeared to have allowed excess biotinylated capture antibody to participate in bacterial capture and detection. When developing a microarray for bacterial detection, this must be considered for optimization. For example, with the appropriate concentration of antibody (in this study, 0.125 ng/nL), spots with increased diameter at the point of contact printing (and almost no streaking) were produced, resulting in a maximal signal. With capture antibody concentrations greater than 0.125 ng/nL, the excess biotinylated capture antibody (i.e., that which was residing in the three-dimensional, semisolid droplet space above the surface) was utilized to capture more bacteria. Similarly, when the immunoassay was performed within a hydrophobic barrier (i.e., without a coverslip), brighter spots with increased signal were observed. In addition, when higher concentrations of cells (~108 cells/mL) were available for capture, the importance of unbound capture antibody in the semisolid droplets became apparent because washing off the excess, unbound biotinylated capture antibody before the immunoassay was performed reduced the signal intensity by nearly 50%. This reduction in signal was not observed with lower concentrations of cells (~106 cells/mL). With increased volumes of capture antibody, abnormal spots were visualized, along with decreased signal intensity, after bacterial detection, indicating that the increased droplet volume detrimentally affected the immunoassay. Full article
(This article belongs to the Special Issue Pathogen Sensors)
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<p>(a) Spread of differing concentrations of biotinylated anti-<span class="html-italic">E. coli</span> O157:H7 capture antibodies (white box indicates site of contact printing by microarray printer), in 135 μm diameter spots (1.1 nL) of 60% PBS:40% glycerol solution (v/v), across streptavidin-coated microscope slides following shearing force (100 μL PBS plus 1% BSA solution [wt/v] applied to one end of a microarray coverslip for blocking purposes; white arrow indicates directionality of flow). After 1 h incubation, the slide was used to capture and detect 1.6 × 10<sup>9</sup> <span class="html-italic">E. coli</span> O157:H7 cells using a sandwich immunoassay. (b) Correlation between fluorescence intensity (shown in arbitrary fluorescence units, or AFU) and relative spot diameter (measured with a ruler and shown in arbitrary units, or AU), following completion of sandwich immunoassay, at the site of contact printing in <a href="#f1-sensors-09-00995" class="html-fig">Figure 1a</a>.</p>
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<p>(a) Spread of differing concentrations of biotinylated anti-<span class="html-italic">E. coli</span> O157:H7 capture antibodies (white box indicates site of contact printing by microarray printer), in 135 μm diameter spots (1.1 nL) of 60% PBS:40% glycerol solution (v/v), across streptavidin-coated microscope slides following shearing force (100 μL PBS plus 1% BSA solution [wt/v] applied to one end of a microarray coverslip for blocking purposes; white arrow indicates directionality of flow). After 1 h incubation, the slide was used to capture and detect 1.6 × 10<sup>9</sup> <span class="html-italic">E. coli</span> O157:H7 cells using a sandwich immunoassay. (b) Correlation between fluorescence intensity (shown in arbitrary fluorescence units, or AFU) and relative spot diameter (measured with a ruler and shown in arbitrary units, or AU), following completion of sandwich immunoassay, at the site of contact printing in <a href="#f1-sensors-09-00995" class="html-fig">Figure 1a</a>.</p>
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<p>Bacterial capture as affected by the use of a coverslip. (a) Detection of 4.7 × 10<sup>8</sup> cells/mL (with 1 ng capture antibody/nL spots) exhibited lower AFU (arbitrary fluorescence units, background corrected) values with the use of a coverslip versus a hydrophobic barrier. (b) Fluorescence microscopic images of covered (left) and uncovered (right) microarray spots (Triplicate 135 μm diameter spots per slide, each on 2 slides) following capture and detection of bacteria. The use of a coverslip produced more uniform, compact spots (and lower AFU measurements), while the hydrophobic barrier approach produced less uniform spots (and higher AFU measures<b>).</b> (c) With the array slide scanner, uncovered spots (right) had wider diameters, and were more intense, after capture and detection of bacteria. Note, the presented spots (scan images and fluorescent micrographs) are representative of day-to-day replicated experimental observations and are displayed at an identical scale for each left-right pair of images. In addition, the images in (b) are more tightly cropped relative to those in (c).</p>
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<p>Bacterial capture as affected by the use of a coverslip. (a) Detection of 4.7 × 10<sup>8</sup> cells/mL (with 1 ng capture antibody/nL spots) exhibited lower AFU (arbitrary fluorescence units, background corrected) values with the use of a coverslip versus a hydrophobic barrier. (b) Fluorescence microscopic images of covered (left) and uncovered (right) microarray spots (Triplicate 135 μm diameter spots per slide, each on 2 slides) following capture and detection of bacteria. The use of a coverslip produced more uniform, compact spots (and lower AFU measurements), while the hydrophobic barrier approach produced less uniform spots (and higher AFU measures<b>).</b> (c) With the array slide scanner, uncovered spots (right) had wider diameters, and were more intense, after capture and detection of bacteria. Note, the presented spots (scan images and fluorescent micrographs) are representative of day-to-day replicated experimental observations and are displayed at an identical scale for each left-right pair of images. In addition, the images in (b) are more tightly cropped relative to those in (c).</p>
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<p>Effects due to scanning at 175 or 125 PMT gain, with or without slide washing before bacterial capture (Five 135 μm diameter, 1 ng/nL spots per slide, each on 2 slides, using a coverslip), at 4.2 × 10<sup>6</sup> (a) or 4.2 × 10<sup>8</sup> (b) cells/mL. (a) Slide washing before capture did not affect AFU (arbitrary fluorescence units, background corrected) when scanned at 175 gain. Lowering the gain to 125 reduced fluorescence intensity. (b) <b>A</b>t 10<sup>8</sup> cells/mL, slide washing before capture reduced AFU values with 175 gain scanning, which was similar to the unwashed, 125 gain scanned slides.</p>
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<p>Effects due to scanning at 175 or 125 PMT gain, with or without slide washing before bacterial capture (Five 135 μm diameter, 1 ng/nL spots per slide, each on 2 slides, using a coverslip), at 4.2 × 10<sup>6</sup> (a) or 4.2 × 10<sup>8</sup> (b) cells/mL. (a) Slide washing before capture did not affect AFU (arbitrary fluorescence units, background corrected) when scanned at 175 gain. Lowering the gain to 125 reduced fluorescence intensity. (b) <b>A</b>t 10<sup>8</sup> cells/mL, slide washing before capture reduced AFU values with 175 gain scanning, which was similar to the unwashed, 125 gain scanned slides.</p>
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<p>Effects due to spot size. (a) Biotinylated fluorescein was spotted at four spot diameters (duplicate spots of each size, micron diameters shown in legend, each on 3 slides), scanned, and washed twice with scanning after each wash. Washing reduced fluorescence intensity (AFU = arbitrary fluorescence units, background corrected) at all spot diameters. At 100 μm, AFU values were not reduced from the first wash to second wash. However, at 200 μm, AFU was reduced from the first to second wash. At 335 and 500 μm, washing twice versus once did not reduce AFU measurements, but these largest two spot diameters consistently exhibited lower AFU values after the washings compared with 100 and 200 μm diameters. (b) Bacterial cells (3.3 × 10<sup>8</sup> cells/mL) exhibited increased signal with smaller, 135 μm spots, as opposed to larger, 500 μm spot diameters (duplicate spots of each size, each on 3 uncovered slides). Also, larger spots appeared to have altered spot morphology (fluorescent microscopic images shown above appropriate bars). (c) Five hundred micron spot images, produced with fluorescence laser scanner, showed expected spot morphology with biotinylated fluorescein (left image), and altered spot morphology after capture and detection of bacteria (right image). Note, the presented spots (scan images and fluorescent micrographs) are representative of day-to-day replicated experimental observations and are displayed at an identical scale for each left-right pair of images.</p>
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<p>Effects due to spot size. (a) Biotinylated fluorescein was spotted at four spot diameters (duplicate spots of each size, micron diameters shown in legend, each on 3 slides), scanned, and washed twice with scanning after each wash. Washing reduced fluorescence intensity (AFU = arbitrary fluorescence units, background corrected) at all spot diameters. At 100 μm, AFU values were not reduced from the first wash to second wash. However, at 200 μm, AFU was reduced from the first to second wash. At 335 and 500 μm, washing twice versus once did not reduce AFU measurements, but these largest two spot diameters consistently exhibited lower AFU values after the washings compared with 100 and 200 μm diameters. (b) Bacterial cells (3.3 × 10<sup>8</sup> cells/mL) exhibited increased signal with smaller, 135 μm spots, as opposed to larger, 500 μm spot diameters (duplicate spots of each size, each on 3 uncovered slides). Also, larger spots appeared to have altered spot morphology (fluorescent microscopic images shown above appropriate bars). (c) Five hundred micron spot images, produced with fluorescence laser scanner, showed expected spot morphology with biotinylated fluorescein (left image), and altered spot morphology after capture and detection of bacteria (right image). Note, the presented spots (scan images and fluorescent micrographs) are representative of day-to-day replicated experimental observations and are displayed at an identical scale for each left-right pair of images.</p>
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585 KiB  
Article
Surface Acoustic WaveAmmonia Sensors Based on ST-cut Quartz under Periodic Al Structure
by Cheng-Liang Hsu, Chi-Yen Shen, Rume-Tze Tsai and Ming-Yau Su
Sensors 2009, 9(2), 980-994; https://doi.org/10.3390/s90200980 - 16 Feb 2009
Cited by 18 | Viewed by 10723
Abstract
Surface acoustic wave (SAW) devices are key components for sensing applications. SAW propagation under a periodic grating was investigated in this work. The theoretical method used here is the space harmonic method. We also applied the results of SAW propagation studied in this [...] Read more.
Surface acoustic wave (SAW) devices are key components for sensing applications. SAW propagation under a periodic grating was investigated in this work. The theoretical method used here is the space harmonic method. We also applied the results of SAW propagation studied in this work to design a two-port resonator with an Al grating on ST-cut quartz. The measured frequency responses of the resonator were similar to the simulation ones. Then, the chemical interface of polyaniline/WO3 composites was coated on the SAW sensor for ammonia detection. The SAW sensor responded to ammonia gas and could be regenerated using dry nitrogen. Full article
(This article belongs to the Section Chemical Sensors)
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<p>The coordinate system.</p>
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<p>Boundary conditions for theoretical analysis: (a) Mechanical boundary conditions. (b) Electrical boundary conditions.</p>
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<p>Dispersion curves of Rayleigh wave under the shorted and open grating on <span class="html-italic">Al</span>/ST-cut quartz at <span class="html-italic">M/p</span> = 0.5: (a) The real part of normalized wave number. (b) The imaginary part of normalized wave number.</p>
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<p>The COM parameters under the Al periodic grating on ST-cut quartz: (a) |<span class="html-italic">κ<sub>12</sub></span>·2<span class="html-italic">p</span>| and <span class="html-italic">κ<sub>11</sub></span>·2<span class="html-italic">p</span> and (b) |<span class="html-italic">ξ</span>(<span class="html-italic">2p</span>)|<span class="html-italic"><sup>2</sup></span>/(<span class="html-italic">ω<sub>0</sub>C<sub>s</sub></span>).</p>
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<p>Frequency responses of two-port SAW resonator.</p>
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<p>The photograph of a dual-device configuration.</p>
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<p>Schematic diagram of a gas testing system for ammonia detection.</p>
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<p>Frequency responses of the SAW sensor to 77 ppm ammonia.</p>
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<p>Frequency responses of the SAW sensor to 40 ppm ammonia.</p>
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412 KiB  
Article
A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery
by Yonggang Qian, Guangjian Yan, Sibo Duan and Xiangsheng Kong
Sensors 2009, 9(2), 961-979; https://doi.org/10.3390/s90200961 - 13 Feb 2009
Cited by 8 | Viewed by 10742
Abstract
The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide [...] Read more.
The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m2 and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m2, only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m2, the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection. Full article
(This article belongs to the Section Remote Sensors)
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<p>Retrieval of land surface emissivity and temperature (K) of HJ-1B from simulated AHS data, (a) land surface emissivity, (b) land surface temperature.</p>
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<p>The difference of brightness temperature between MIR and TIR channel of HJ-1B data.</p>
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<p>An illustration of the histogram of <span class="html-italic">δ</span><sub>34</sub><span class="html-italic"><sub>B</sub></span></p>
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<p>The flow chart of fire detection.</p>
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<p>Probabilities of fire detection in nadir view for different fire area (m<sup>2</sup>) and temperature (K) under US standard atmospheric conditions with a visibility of 23 km. The solar zenith angles are 0, 30, 45 and 60 degree for (a), (b), (c) and (d), respectively.</p>
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<p>Probabilities of fire detection in nadir view for different fire areas (m<sup>2</sup>) and temperatures (K) under standard atmospheric conditions with a visibility of 23 km; (a) tropical; (b) midwinter; (c) midsummer.</p>
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<p>Omission errors of fire detection for different fire areas (m<sup>2</sup>) and temperatures (K) under US standard atmospheric conditions with a visibility of 23 km. The solar zenith angles are 0, 30, 45 and 60 degree for (a), (b), (c) and (d), respectively.</p>
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<p>Commission errors of fire detection for different fire areas (m<sup>2</sup>) and temperatures (K) Under the US standard atmospheric conditions with a visibility of 23 km. The solar zenith angles are 0 and 30 degree for (a) and (b), respectively</p>
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<p>Commission errors of fire detection for different fire areas (m<sup>2</sup>) and temperatures (K) Under the US standard atmospheric conditions with a visibility of 23 km. The solar zenith angles are 0 and 30 degree for (a) and (b), respectively</p>
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900 KiB  
Review
Passive and Self-Powered Autonomous Sensors for Remote Measurements
by Emilio Sardini and Mauro Serpelloni
Sensors 2009, 9(2), 943-960; https://doi.org/10.3390/s90200943 - 13 Feb 2009
Cited by 37 | Viewed by 16212
Abstract
Autonomous sensors play a very important role in the environmental, structural, and medical fields. The use of this kind of systems can be expanded for several applications, for example in implantable devices inside the human body where it is impossible to use wires. [...] Read more.
Autonomous sensors play a very important role in the environmental, structural, and medical fields. The use of this kind of systems can be expanded for several applications, for example in implantable devices inside the human body where it is impossible to use wires. Furthermore, they enable measurements in harsh or hermetic environments, such as under extreme heat, cold, humidity or corrosive conditions. The use of batteries as a power supply for these devices represents one solution, but the size, and sometimes the cost and unwanted maintenance burdens of replacement are important drawbacks. In this paper passive and self-powered autonomous sensors for harsh or hermetical environments without batteries are discussed. Their general architectures are presented. Sensing strategies, communication techniques and power management are analyzed. Then, general building blocks of an autonomous sensor are presented and the design guidelines that such a system must follow are given. Furthermore, this paper reports different proposed applications of autonomous sensors applied in harsh or hermetic environments: two examples of passive autonomous sensors that use telemetric communication are proposed, the first one for humidity measurements and the second for high temperatures. Other examples of self-powered autonomous sensors that use a power harvesting system from electromagnetic fields are proposed for temperature measurements and for airflow speeds. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Italy)
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<p>Block diagram of a passive autonomous sensor.</p>
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<p><b>(a)</b> Physical model of an autonomous sensor. <b>(b)</b> Module and phase of the impedance as seen from the terminal of the readout [<a href="#b32-sensors-09-00943" class="html-bibr">32</a>].</p>
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<p>Block diagram of self-powered autonomous sensors.</p>
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<p>(a) Picture of the passive autonomous sensor and readout system for RH measurement and (b) block diagram of the experimental set-up.</p>
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<p>The calculated capacitance values as a function of RH and for different distance values.</p>
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<p>Capacitance values calculated using the proposed conditioning electronics as a function of RH and distance.</p>
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<p>(a) Sketch of the passive autonomous sensor for high temperature measurement and (b) block diagram of the experimental set-up.</p>
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<p>Sensor capacitance directly measured by HP4194A and the data obtained by the telemetric system.</p>
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<p>(a) The experimental set-up of the self-powered autonomous sensor for temperature measurement and (b) the block diagram of the autonomous sensor and readout system.</p>
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475 KiB  
Article
Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model
by Mirco Migliavacca, Michele Meroni, Lorenzo Busetto, Roberto Colombo, Terenzio Zenone, Giorgio Matteucci, Giovanni Manca and Guenther Seufert
Sensors 2009, 9(2), 922-942; https://doi.org/10.3390/s90200922 - 13 Feb 2009
Cited by 42 | Viewed by 12593
Abstract
In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse [...] Read more.
In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale. Full article
(This article belongs to the Section Remote Sensors)
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<p>Flow chart of the PROSAILH-BGC model. Yellow blocks represent the models, parallelepipeds represent the input parameters, grey boxes represent the state variables passed between the coupled models, while the red boxes are the model outputs (NDVI and GPP).</p>
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<p>Flow chart of first-step optimization. Yellow blocks represent the models, parallelepipeds represent the model input parameters and the data for model optimization, grey boxes represent the state variables passed between coupled models while the red box is the model output.</p>
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<p>Flow chart of second-step optimization. Yellow blocks represent the models, parallelepipeds represent the model input parameters and the data for model optimization, grey boxes represent the state variables passed between coupled models while the red box is the model output.</p>
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<p>Relationship between modeled and observed PAR<sub>t</sub>. Red circles represent data modeled with BIOME-BGC while white circles represent data modeled with PROSAIL-BGC. Dashed lines represent the 95% confidence intervals of the linear regression between PAR<sub>t</sub> modeled (with BIOME-BGC in red and PROSAIL-BGC in black) and observed data. Grey line is the 1:1 line. b[0] is the intercept, blsqb;1] is the slope and <span class="html-italic">p</span> is the significance of the linear regression analysis.</p>
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<p>a) Time series of NDVI<sub>MODIS</sub> (full circles) and NDVI<sub>PROSAILH-BGC</sub> (open circles) for the time period 2002-2003. b) Scatterplot of NDVI<sub>MODIS</sub> and NDVI<sub>PROSAILH-BGC</sub>. Black triangles are the NDVI data for the growing season (for the days between ONDAY and OFFDAY) while open triangles are data for the dormant period. The black straight line is the regression line calculated on the whole dataset, the dashed lines represent the 95 confidence intervals, the grey line is the 1:1 line. b[0rsqb; is the intercept, blsqb;1rsqb; is the slope and <span class="html-italic">p</span> is the significance of the linear regression analysis observed vs modeled.</p>
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<p>a) Time courses of modeled (red straight line) and observed (blue dotted line) GPP for 2002 and 2003. b) Scatterplot of observed and modeled GPP, data from both the growing seasons were plotted with exclusion of data of the dormant period. The black straight line is the regression line, the dashed lines represent the 95 confidence intervals, the grey line is the 1:1 line. blsqb;0rsqb; is the intercept, blsqb;1rsqb; is the slope and <span class="html-italic">p</span> is the significance of the linear regression analysis observed vs modeled.</p>
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100 KiB  
Article
Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip
by Selcuk Okdem and Dervis Karaboga
Sensors 2009, 9(2), 909-921; https://doi.org/10.3390/s90200909 - 13 Feb 2009
Cited by 148 | Viewed by 15109
Abstract
Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely [...] Read more.
Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks. Full article
(This article belongs to the Section Remote Sensors)
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<p>Raw data splits.</p>
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<p>Data package content.</p>
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<p>ACO parameters memorized in the nodes.</p>
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<p>Acknowledgement signal content.</p>
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<p>Transmission an acknowledgement signal.</p>
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<p>Average residual energy for different WSNs having various number of nodes.</p>
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<p>Average residual energy after 256 packets are received.</p>
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<p>State Diagram of Communication Protocol.</p>
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260 KiB  
Article
A Real-Time De-Noising Algorithm for E-Noses in a Wireless Sensor Network
by Jianfeng Qu, Yi Chai and Simon X. Yang
Sensors 2009, 9(2), 895-908; https://doi.org/10.3390/s90200895 - 11 Feb 2009
Cited by 25 | Viewed by 10795
Abstract
A wireless e-nose network system is developed for the special purpose of monitoring odorant gases and accurately estimating odor strength in and around livestock farms. This system is to simultaneously acquire accurate odor strength values remotely at various locations, where each node is [...] Read more.
A wireless e-nose network system is developed for the special purpose of monitoring odorant gases and accurately estimating odor strength in and around livestock farms. This system is to simultaneously acquire accurate odor strength values remotely at various locations, where each node is an e-nose that includes four metal-oxide semiconductor (MOS) gas sensors. A modified Kalman filtering technique is proposed for collecting raw data and de-noising based on the output noise characteristics of those gas sensors. The measurement noise variance is obtained in real time by data analysis using the proposed slip windows average method. The optimal system noise variance of the filter is obtained by using the experiments data. The Kalman filter theory on how to acquire MOS gas sensors data is discussed. Simulation results demonstrate that the proposed method can adjust the Kalman filter parameters and significantly reduce the noise from the gas sensors. Full article
(This article belongs to the Special Issue Gas Sensors 2009)
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<p>Block diagram of the e-nose prototype.</p>
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<p>The gas chamber, pump and sensor array in the e-nose.</p>
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<p>The interface board and the MicaZ for the e-nose.</p>
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<p>Block diagram of the data acquisition circuit.</p>
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<p>Block diagram of the inputs and outputs of an MOS gas sensor.</p>
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<p>Filter results at different variance ratio factors <span class="html-italic">λ</span>. (a) <span class="html-italic">λ</span>=0.1, the filtered result fluctuates like the raw data; (b), <span class="html-italic">λ</span>=1 the filtered result is still fluctuating in some sections; (c) <span class="html-italic">λ</span>=10, the filtered result is still fluctuating in some sections; (d) <span class="html-italic">λ</span>=100, the filter result is robust for all the filtered ranges and sensitive to the changes in odor strength; (e) <b><span class="html-italic">λ</span></b> =1,000, a lag phenomenon appears in sections A and B in the filtered result.</p>
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<p>Filter results of the four sensors. (a) Sensor 1 at variance ratio factor <span class="html-italic">λ</span>=100; (b) Sensor 2 at variance ratio factor <span class="html-italic">λ</span>=70; (c) Sensor 3 at variance ratio factor <span class="html-italic">λ</span>=65; (d) Sensor 4 at variance ratio factor <span class="html-italic">λ</span>=120.</p>
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<p>Comparison results between the conventional and the modified Kalman filter algorithm. (a) Estimated value of measurement noise variance; (b) The filtered results.</p>
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309 KiB  
Article
Direct Electrochemistry of Horseradish Peroxidase-Gold Nanoparticles Conjugate
by Gautham Kumar Ahirwal and Chanchal K. Mitra
Sensors 2009, 9(2), 881-894; https://doi.org/10.3390/s90200881 - 10 Feb 2009
Cited by 48 | Viewed by 14487
Abstract
We have studied the direct electrochemistry of horseradish peroxidase (HRP) coupled to gold nanoparticles (AuNP) using electrochemical techniques, which provide some insight in the application of biosensors as tools for diagnostics because HRP is widely used in clinical diagnostics kits. AuNP capped with [...] Read more.
We have studied the direct electrochemistry of horseradish peroxidase (HRP) coupled to gold nanoparticles (AuNP) using electrochemical techniques, which provide some insight in the application of biosensors as tools for diagnostics because HRP is widely used in clinical diagnostics kits. AuNP capped with (i) glutathione and (ii) lipoic acid was covalently linked to HRP. The immobilized HRP/AuNP conjugate showed characteristic redox peaks at a gold electrode. It displayed good electrocatalytic response to the reduction of H2O2, with good sensitivity and without any electron mediator. The covalent linking of HRP and AuNP did not affect the activity of the enzyme significantly. The response of the electrode towards the different concentrations of H2O2 showed the characteristics of Michaelis Menten enzyme kinetics with an optimum pH between 7.0 to 8.0. The preparation of the sensor involves single layer of enzyme, which can be carried out efficiently and is also highly reproducible when compared to other systems involving the layer-by-layer assembly, adsorption or encapsulation of the enzyme. The immobilized AuNP-HRP can be used for immunosensor applications Full article
(This article belongs to the Section Chemical Sensors)
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Graphical abstract

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<p>Mechanism of the direct bioelectrocatalytic reduction of hydrogen peroxidase at peroxidase-modified electrodes. P<sup>+</sup> is a cation radical localized on the porphyrin ring or polypeptide chain.</p>
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<p>UV‐Visible spectrum of (1) gold nanoparticles (AuNP), (2) glutathione capped AuNP and (3) lipoic acid capped AuNP.</p>
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<p>UV-Visible spectrum of (a) glutathione capped Au‐NP, and (b) lipoic acid capped Au‐NP in pH (1) 5.0, (2) 5.5, (3) 6.0, (4) 7.0 solutions.</p>
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<p>FT‐IR (JASCO FT/IR‐5300) spectrum in KBr of (a) gold nanoparticles, (b) glutathione powder and (c) gold nanoparticles capped with glutathione and (d) gold nanoparticles, (e) lipoic acid powder and (f) gold nanoparticles capped with lipoic acid.</p>
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<p>Cyclic voltammogram of HRP coupled to (a) glutathione capped AuNP, (b) lipoic acid capped AuNP. Using gold electrode, 10.2 μM H<sub>2</sub>O<sub>2</sub>, 20 mV/s scan rate, Ag/AgCl reference electrode. Plot (1) is blank electrode, (2) AuNP modified electrode and (3) is HRP coupled AuNP modified electrode</p>
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<p>Cyclic voltammogram of HRP coupled to (a) glutathione capped AuNP, (b) lipoic acid capped AuNP at scan rates of 5, 10, 20, 40 and 80 mV/s.</p>
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<p>Dependence of the peak current values obtained from cyclic voltammogram of HRP coupled to lipoic acid capped (-●-) measured at the potential of 0.06 V and glutathione capped AuNP (-▲-) measured at a potential 0.01 V using (a) different concentrations of H<sub>2</sub>O<sub>2</sub> and at (b) different pH.</p>
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<p>The reactions in the enzymatic catalytic cycle of HRP</p>
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<p>Schematic representation of the synthesis and stabilization of AuNP.</p>
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534 KiB  
Article
Manufacture of a Polyaniline Nanofiber Ammonia Sensor Integrated with a Readout Circuit Using the CMOS-MEMS Technique
by Mao-Chen Liu, Ching-Liang Dai, Chih-Hua Chan and Chyan-Chyi Wu
Sensors 2009, 9(2), 869-880; https://doi.org/10.3390/s90200869 - 10 Feb 2009
Cited by 65 | Viewed by 14053
Abstract
This study presents the fabrication of a polyaniline nanofiber ammonia sensor integrated with a readout circuit on a chip using the commercial 0.35 mm complementary metal oxide semiconductor (CMOS) process and a post-process. The micro ammonia sensor consists of a sensing resistor and [...] Read more.
This study presents the fabrication of a polyaniline nanofiber ammonia sensor integrated with a readout circuit on a chip using the commercial 0.35 mm complementary metal oxide semiconductor (CMOS) process and a post-process. The micro ammonia sensor consists of a sensing resistor and an ammonia sensing film. Polyaniline prepared by a chemical polymerization method was adopted as the ammonia sensing film. The fabrication of the ammonia sensor needs a post-process to etch the sacrificial layers and to expose the sensing resistor, and then the ammonia sensing film is coated on the sensing resistor. The ammonia sensor, which is of resistive type, changes its resistance when the sensing film adsorbs or desorbs ammonia gas. A readout circuit is employed to convert the resistance of the ammonia sensor into the voltage output. Experimental results show that the sensitivity of the ammonia sensor is about 0.88 mV/ppm at room temperature Full article
(This article belongs to the Special Issue Gas Sensors 2009)
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<p>Schematic structure of the ammonia sensor integrated with readout circuit.</p>
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<p>Energy band diagram of the sensor in <b>(a)</b> air and <b>(b)</b> ammonia. E<sub>F</sub> is the Fermi level, E<sub>c-PANi</sub> is the conduction band of polyaniline, E<sub>v- PANi</sub> is the valence band of polyaniline, E<sub>Fi-PANi</sub> is the intrinsic Fermi level of polyaniline, E<sub>c-PolySi</sub> is the conduction band of polysilicon, E<sub>v-PolySi</sub> is the valence band of polysilicon, and E<sub>Fi-PolySi</sub> is the intrinsic Fermi of polysilicon.</p>
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<p>Readout circuit for the ammonia sensor.</p>
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<p>Design of the operational amplifier circuit.</p>
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<p>Frequency response of the operational amplifier.</p>
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<p>Simulated results of the readout circuit.</p>
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<p>Process flow of the ammonia sensor; (a) after the CMOS process, (b) etching sacrificial layers, and (c) coating the sensing film.</p>
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<p>photograph of the integrated ammonia sensor chip after the wet etching process.</p>
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<p>Scanning electron microscope image of polyaniline nanofiber film.</p>
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861 KiB  
Article
Performance of a Diaphragmed Microlens for a Packaged Microspectrometer
by Joe Lo, Shih-Jui Chen, Qiyin Fang, Thanassis Papaioannou, Eun-Sok Kim, Martin Gundersen and Laura Marcu
Sensors 2009, 9(2), 859-868; https://doi.org/10.3390/s90200859 - 6 Feb 2009
Cited by 2 | Viewed by 11604
Abstract
This paper describes the design, fabrication, packaging and testing of a microlens integrated in a multi-layered MEMS microspectrometer. The microlens was fabricated using modified PDMS molding to form a suspended lens diaphragm. Gaussian beam propagation model was used to measure the focal length [...] Read more.
This paper describes the design, fabrication, packaging and testing of a microlens integrated in a multi-layered MEMS microspectrometer. The microlens was fabricated using modified PDMS molding to form a suspended lens diaphragm. Gaussian beam propagation model was used to measure the focal length and quantify M2 value of the microlens. A tunable calibration source was set up to measure the response of the packaged device. Dual wavelength separation by the packaged device was demonstrated by CCD imaging and beam profiling of the spectroscopic output. We demonstrated specific techniques to measure critical parameters of microoptics systems for future optimization of spectroscopic devices Full article
(This article belongs to the Special Issue BioMEMS)
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<p>Cross-sectional view of the microspectrometer, showing the 5 mm silicon packaging with microlens, the MEMS grating, and other optical components.</p>
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<p>(a) Process flow of the microlens starts from photoresist reflow and is realized through soft-lithography molding. (b) Photomicrograph of a finished microlens, top down.</p>
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<p>(a) Multi-layer silicon cavity packaging of microlens and other MEMS components of the micro-spectrometer. (b) The 5 mm by 5 mm package with cross-section (top) and perspective (bottom) view.</p>
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<p>(a) Schematic of beam profiling with a CCD camera. (b) Focusing of Gaussian beam (dotted line) and non-Gaussian beam (solid line).</p>
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<p>(a) Measured radii along the focal axis of six microlens diaphragms with arrow showing increasing focal length (<span class="html-italic">f</span> in mm). (b) M<sup>2</sup> value is plotted on the left scale, while focal length is plotted on the right scale. Higher speed on the horizontal axes yields thinner initial resist, with a fixed 1.2 mm diameter, higher speed also yields lower aspect ratio. Where both curves cross indicates a balance of lens M<sup>2</sup> quality and 2 mm focal length required in our spectrometer. (c) Sequence of intensity profiles as the cross sections of the focal axis are taken with 50 μm steps, scale bar 10 μm (for M<sup>2</sup> 1.98, <span class="html-italic">f</span> 3.5 mm lens).</p>
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<p>(a) Measured radii along the focal axis of six microlens diaphragms with arrow showing increasing focal length (<span class="html-italic">f</span> in mm). (b) M<sup>2</sup> value is plotted on the left scale, while focal length is plotted on the right scale. Higher speed on the horizontal axes yields thinner initial resist, with a fixed 1.2 mm diameter, higher speed also yields lower aspect ratio. Where both curves cross indicates a balance of lens M<sup>2</sup> quality and 2 mm focal length required in our spectrometer. (c) Sequence of intensity profiles as the cross sections of the focal axis are taken with 50 μm steps, scale bar 10 μm (for M<sup>2</sup> 1.98, <span class="html-italic">f</span> 3.5 mm lens).</p>
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<p>Spectroscopic measurement of efficiency with a custom tunable calibration source.</p>
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<p>System response of the packaged microspectrometer. The 540 nm response has efficiency of 15%, for reference, cured SU8 transmission in the visible is above 95%.</p>
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<p>(a) Separation of collinear laser sources at 543 nm and 594 nm. (b) The intensity profile along line drawn in (a), providing resolution and straylight interpretations.</p>
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341 KiB  
Article
Enhanced TDMA Based Anti-Collision Algorithm with a Dynamic Frame Size Adjustment Strategy for Mobile RFID Readers
by Kwang Cheol Shin, Seung Bo Park and Geun Sik Jo
Sensors 2009, 9(2), 845-858; https://doi.org/10.3390/s90200845 - 6 Feb 2009
Cited by 31 | Viewed by 9722
Abstract
In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID) is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for [...] Read more.
In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID) is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for and checkout of items in warehouses, supermarkets, libraries and other industrial fields. In using Mobile RFID, since the readers are continuously moving, they can interfere with each other when they attempt to read the tags. In this study, we suggest a Time Division Multiple Access (TDMA) based anti-collision algorithm for Mobile RFID readers. Our algorithm automatically adjusts the frame size of each reader without using manual parameters by adopting the dynamic frame size adjustment strategy when collisions occur at a reader. Through experiments on a simulated environment for Mobile RFID readers, we show that the proposed method improves the number of successful transmissions by about 228% on average, compared with Colorwave, a representative TDMA based anti-collision algorithm. Full article
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<p>Frame structure of TDMA based anti-collision algorithm.</p>
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<p>Example of reader collision.</p>
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<p>Interference and read range of a reader.</p>
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<p>Interference and read range of a reader.</p>
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<p>A reader's entering the matrix and its moving direction.</p>
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<p>Comparison of results of average frame size of readers.</p>
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<p>Comparison of results of frame utilization.</p>
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<p>Comparison of results of average number of messages.</p>
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<p>Comparison of results of successful transmissions.</p>
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1044 KiB  
Article
Detection of Seagrass Distribution Changes from 1991 to 2006 in Xincun Bay, Hainan, with Satellite Remote Sensing
by Dingtian Yang and Chaoyu Yang
Sensors 2009, 9(2), 830-844; https://doi.org/10.3390/s90200830 - 5 Feb 2009
Cited by 79 | Viewed by 13283
Abstract
Seagrass distribution is a very important index for costal management and protection. Seagrass distribution changes can be used as indexes to analyze the reasons for the changes. In this paper, in situ hyperspectral observation and satellite images of QuickBird, CBERS (China Brazil Earth [...] Read more.
Seagrass distribution is a very important index for costal management and protection. Seagrass distribution changes can be used as indexes to analyze the reasons for the changes. In this paper, in situ hyperspectral observation and satellite images of QuickBird, CBERS (China Brazil Earth Resources Satellite data) and Landsat data were used to retrieve bio-optical models and seagrass (Enhalus acoroides,Thalassia hemperichii) distribution in Xincun Bay, Hainan province, and seagrass distribution changes from 1991 to 2006 were analyzed. Hyperspectral results showed that the spectral bands at 555, 635, 650 and 675 nm are sensitive to leaf area index (LAI). Seagrass detection with QuickBird was more accurate than that with Landsat TM and CBERS; five classes could be classified clearly and used as correction for seagrass remote sensing data from Landsat TM and CBERS. In order to better describe seagrass distribution changes, the seagrass distribution area was divided as three regions: region A connected with region B in 1991, however it separated in 1999 and was wholly separated in 2001; seagrass in region C shrank gradually and could not be detected in 2006. Analysis of the reasons for seagrass reduction indicated it was mainly affected by aquaculture and typhoons and in recent years, by land use changes. Full article
(This article belongs to the Special Issue Sensor Algorithms)
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<p>Study area in Xincun Bay, Hainan Province, China.</p>
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<p>Experiments carried out in Sanya Bay for the training algorithm.</p>
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<p>Flow chart of data processing for seagrass retrieval.</p>
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<p>Relationship between spectral bands and seagrass LAI.</p>
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<p>Relationship between LAI and NDVI.</p>
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<p>Seagrass density retrieved with QuickBird.</p>
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<p>Seagrass distribution change from 1991 to 2006.</p>
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<p>Shrimp ponds around Xincun Bay in 2001.</p>
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<p>Pathway of typhoons affecting Xincun Bay since 2000.</p>
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1932 KiB  
Article
A Fast Level Set Method for Synthetic Aperture Radar Ocean Image Segmentation
by Xiaoxia Huang, Bo Huang and Hongga Li
Sensors 2009, 9(2), 814-829; https://doi.org/10.3390/s90200814 - 3 Feb 2009
Cited by 14 | Viewed by 12527
Abstract
Segmentation of high noise imagery like Synthetic Aperture Radar (SAR) images is still one of the most challenging tasks in image processing. While level set, a novel approach based on the analysis of the motion of an interface, can be used to address [...] Read more.
Segmentation of high noise imagery like Synthetic Aperture Radar (SAR) images is still one of the most challenging tasks in image processing. While level set, a novel approach based on the analysis of the motion of an interface, can be used to address this challenge, the cell-based iterations may make the process of image segmentation remarkably slow, especially for large-size images. For this reason fast level set algorithms such as narrow band and fast marching have been attempted. Built upon these, this paper presents an improved fast level set method for SAR ocean image segmentation. This competent method is dependent on both the intensity driven speed and curvature flow that result in a stable and smooth boundary. Notably, it is optimized to track moving interfaces for keeping up with the point-wise boundary propagation using a single list and a method of fast up-wind scheme iteration. The list facilitates efficient insertion and deletion of pixels on the propagation front. Meanwhile, the local up-wind scheme is used to update the motion of the curvature front instead of solving partial differential equations. Experiments have been carried out on extraction of surface slick features from ERS-2 SAR images to substantiate the efficacy of the proposed fast level set method. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR))
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<p>Illustration of level sets.</p>
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<p>Narrow-band of level set.</p>
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<p>SAR image segmentation using the proposed fast level set method</p>
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<p>Example of processing stages by the proposed fast level set method.</p>
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<p>The proposed fast level set method with single seed for initialization of level sets.</p>
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<p>The proposed fast level set method with single seed for initialization of level sets.</p>
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<p>The proposed fast level set method with multiple seeds for initialization of level sets.</p>
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<p>The proposed fast level set method with multiple seeds for initialization of level sets.</p>
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<p>Comparison of the proposed method with the ordinary level set and fast marching methods</p>
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3784 KiB  
Article
Performance of Three Reflectance Calibration Methods for Airborne Hyperspectral Spectrometer Data
by Tomoaki Miura and Alfredo R. Huete
Sensors 2009, 9(2), 794-813; https://doi.org/10.3390/s90200794 - 3 Feb 2009
Cited by 36 | Viewed by 12207
Abstract
In this study, the performances and accuracies of three methods for converting airborne hyperspectral spectrometer data to reflectance factors were characterized and compared. The “reflectance mode (RM)” method, which calibrates a spectrometer against a white reference panel prior to mounting on an aircraft, [...] Read more.
In this study, the performances and accuracies of three methods for converting airborne hyperspectral spectrometer data to reflectance factors were characterized and compared. The “reflectance mode (RM)” method, which calibrates a spectrometer against a white reference panel prior to mounting on an aircraft, resulted in spectral reflectance retrievals that were biased and distorted. The magnitudes of these bias errors and distortions varied significantly, depending on time of day and length of the flight campaign. The “linear-interpolation (LI)” method, which converts airborne spectrometer data by taking a ratio of linearly-interpolated reference values from the preflight and postflight reference panel readings, resulted in precise, but inaccurate reflectance retrievals. These reflectance spectra were not distorted, but were subject to bias errors of varying magnitudes dependent on the flight duration length. The “continuous panel (CP)” method uses a multi-band radiometer to obtain continuous measurements over a reference panel throughout the flight campaign, in order to adjust the magnitudes of the linear-interpolated reference values from the preflight and post-flight reference panel readings. Airborne hyperspectral reflectance retrievals obtained using this method were found to be the most accurate and reliable reflectance calibration method. The performances of the CP method in retrieving accurate reflectance factors were consistent throughout time of day and for various flight durations. Based on the dataset analyzed in this study, the uncertainty of the CP method has been estimated to be 0.0025 ± 0.0005 reflectance units for the wavelength regions not affected by atmospheric absorptions. The RM method can produce reasonable results only for a very short-term flight (e.g., < 15 minutes) conducted around a local solar noon. The flight duration should be kept shorter than 30 minutes for the LI method to produce results with reasonable accuracies. An important advantage of the CP method is that the method can be used for long-duration flight campaigns (e.g., 1-2 hours). Although this study focused on reflectance calibration of airborne spectrometer data, the methods evaluated in this study and the results obtained are directly applicable to ground spectrometer measurements. Full article
(This article belongs to the Section Remote Sensors)
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<p>Mean reflectance spectra for the 10:30 a.m. MST transect run derived with three reflectance calibration methods: (a) reflectance factors, (b) mean differences, and (c) % relative mean differences. For (b) and (c), the y-axes on the left are for the linear-interpolation and continuous panel results and the y-axes on the right are for the reflectance mode method results.</p>
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<p>Mean reflectance spectra for the 10:30 a.m. MST transect run derived with three reflectance calibration methods: (a) reflectance factors, (b) mean differences, and (c) % relative mean differences. For (b) and (c), the y-axes on the left are for the linear-interpolation and continuous panel results and the y-axes on the right are for the reflectance mode method results.</p>
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<p>Same as <a href="#f1-sensors-09-00794" class="html-fig">Figure 1</a>, but for the 12:30 p.m. MST transect run.</p>
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<p>Changes in performance statistics of the reflectance mode method across different time of day: (a) mean differences (MD), (b) root mean square errors (RMSE), and (c) standard deviations about MD (STD). These are for one hour flight scenarios.</p>
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<p>Same as <a href="#f3-sensors-09-00794" class="html-fig">Figure 3</a>, but for the linear-interpolation method.</p>
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<p>Same as <a href="#f3-sensors-09-00794" class="html-fig">Figure 3</a>, but for the continuous panel method.</p>
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<p>Same as <a href="#f3-sensors-09-00794" class="html-fig">Figure 3</a>, but for the continuous panel method.</p>
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<p>Changes in performance statistics of the reflectance mode method across different flight durations (time lengths): (a) mean reflectance factors, (b) mean differences (MD), (c) % relative MD, (b) root mean square errors (RMSE), and (c) standard deviations about MD (STD). The numbers in parentheses in the legends are the time in minutes between the preflight panel readings and the target measurements.</p>
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<p>Same as <a href="#f6-sensors-09-00794" class="html-fig">Figure 6</a>, but for the linear-interpolation method.</p>
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<p>Same as <a href="#f6-sensors-09-00794" class="html-fig">Figure 6</a>, but for the linear-interpolation method.</p>
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<p>Same as <a href="#f6-sensors-09-00794" class="html-fig">Figure 6</a>, but for the continuous panel method.</p>
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<p>Comparison of reflectance spectra derived with three different reflectance calibration methods obtained at the beginning of the airborne measurements (∼30 minutes after the preflight ASD reference panel readings) for (a) tropical forest and (d) savanna, at the middle of the flight (∼75 minutes after the preflight panel readings) for (b) tropical forest and (e) savanna, and at the end of the flight (∼100 minutes after the panel readings) for (c) tropical forest and (f) savanna.</p>
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1350 KiB  
Article
Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area
by Juan C. Jiménez-Muñoz, José A. Sobrino, Antonio Plaza, Luis Guanter, José Moreno and Pablo Martinez
Sensors 2009, 9(2), 768-793; https://doi.org/10.3390/s90200768 - 2 Feb 2009
Cited by 158 | Viewed by 16273
Abstract
In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using [...] Read more.
In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixel elements, called Endmembers (EMs). These EMs were extracted from the image using three different methods: i) manual extraction using a land cover map, ii) Pixel Purity Index (PPI) and iii) Automated Morphological Endmember Extraction (AMEE). The different methodologies for FVC retrieval were applied to one PROBA/CHRIS image acquired over an agricultural area in Spain, and they were calibrated and tested against in situ measurements of FVC estimated with hemispherical photographs. The results obtained from VIs show that VARI correlates better with FVC than NDVI does, with standard errors of estimation of less than 8% in the case of VARI and less than 13% in the case of NDVI when calibrated using the in situ measurements. The results obtained from the SMA-LSU technique show Root Mean Square Errors (RMSE) below 12% when EMs are extracted from the AMEE method and around 9% when extracted from the PPI method. A RMSE value below 9% was obtained for manual extraction of EMs using a land cover use map. Full article
(This article belongs to the Section Remote Sensors)
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<p>Schematic view of PROBA/CHRIS acquisition geometry.</p>
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<p>Acquisition geometries and illumination angles for the CHRIS/PROBA images acquired over Barrax on the 12th and the 14th of July 2003.</p>
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<p>Test site as viewed by PROBA/CHRIS. The image shows a RGB composition in natural colour using CHRIS bands 25 (674.419 nm), 14 (563.373 nm) and 8 (501.531 nm). Green and dark tones are vegetated plots.</p>
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<p>Land use map for the Barrax test site. Red crosses indicate the points where hemispherical photographs (HP) were taken.</p>
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<p>At-surface reflectivity spectra extracted from CHRIS image for the different samples (see <a href="#t1-sensors-09-00768" class="html-table">Table 1</a>).</p>
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<p>NDVI histogram extracted from the CHRIS image.</p>
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<p><b>(a)</b> GVI and <b>(b)</b> VARIgreen histograms extracted from the CHRIS image.</p>
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<p>Empirical approaches between different vegetation indices and the fractional vegetation cover measured <span class="html-italic">in situ</span>. Fitted lines, correlation coefficients (r) and standard errors of estimation (σ) are also represented.</p>
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<p><b>(a)</b> Normalized Difference Vegetation Index (NDVI), <b>(b)</b> Variable Atmospherically Resistant Index (VARI) and <b>(c)</b> Fractional Vegetation Cover (FVC) retrieved from VARIgreen. Maps obtained from PROBA/CHRIS image acquired at near nadir view (see <a href="#f3-sensors-09-00768" class="html-fig">Figure 3</a>).</p>
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