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Sensors, Volume 15, Issue 1 (January 2015) – 121 articles , Pages 1-2231

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604 KiB  
Editorial
Sensors Best Paper Award 2015
by Vittorio M.N. Passaro, W. Rudolf Seitz, Assefa M. Melesse, Alexander Star and Leonhard Reindl
Sensors 2015, 15(1), 2228-2231; https://doi.org/10.3390/s150102228 - 20 Jan 2015
Viewed by 7946
Abstract
Since 2011, an annual award system was instituted to recognize outstanding Sensors papers that are related to sensing technologies and applications and meet the aims, scope and high standards of this journal [1–4]. This year, the winners were chosen by the Section Editor-in-Chiefs [...] Read more.
Since 2011, an annual award system was instituted to recognize outstanding Sensors papers that are related to sensing technologies and applications and meet the aims, scope and high standards of this journal [1–4]. This year, the winners were chosen by the Section Editor-in-Chiefs of Sensors from among all the papers published in 2011 to track citations. Reviews and full research articles were considered separately. We gladly announce that the following eight papers were awarded the Sensors Best Paper Award in 2015.[...] Full article
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1774 KiB  
Article
PET and PVC Separation with Hyperspectral Imagery
by Monica Moroni, Alessandro Mei, Alessandra Leonardi, Emanuela Lupo and Floriana La Marca
Sensors 2015, 15(1), 2205-2227; https://doi.org/10.3390/s150102205 - 20 Jan 2015
Cited by 95 | Viewed by 12966
Abstract
Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in [...] Read more.
Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET) and polyvinyl chloride (PVC)—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry. Full article
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<p>Images of (<b>a</b>) virgin, waste and regenerated PET samples; (<b>b</b>) virgin, waste and regenerated PVC samples; (<b>c</b>) PET and PVC samples in pieces (for the nomenclature refer to <a href="#t1-sensors-15-02205" class="html-table">Table 1</a>).</p>
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<p>(<b>a</b>) Diagram of the hyperspectral device with two spectrometers. CL stands for Camera Link, eST for eSATA connection, SYNC for synchronization signal; (<b>b</b>) Sketch of the laboratory facility. S1 stands for VIS spectrometer and S2 for NIR spectrometer.</p>
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<p>Calibration curve of the VIS spectrometer interfaced with interference filters tuning in the VIS and SNIR regions.</p>
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<p>Calibration curve of the NIR spectrometer interfaced with interference filters tuning in the SNIR and LNIR regions.</p>
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<p>Hyperspectral cube.</p>
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<p>Representative NIR signatures of PET samples on conveyor belt.</p>
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<p>Representative NIR signatures of PVC samples on conveyor belt.</p>
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<p>NIR signatures after continuum removal of PET samples on conveyor belt.</p>
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<p>NIR signatures after continuum removal of PVC samples on conveyor belt.</p>
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3390 KiB  
Article
An Activity Recognition Model Using Inertial Sensor Nodes in a Wireless Sensor Network for Frozen Shoulder Rehabilitation Exercises
by Hsueh-Chun Lin, Shu-Yin Chiang, Kai Lee and Yao-Chiang Kan
Sensors 2015, 15(1), 2181-2204; https://doi.org/10.3390/s150102181 - 19 Jan 2015
Cited by 45 | Viewed by 9928
Abstract
This paper proposes a model for recognizing motions performed during rehabilitation exercises for frozen shoulder conditions. The model consists of wearable wireless sensor network (WSN) inertial sensor nodes, which were developed for this study, and enables the ubiquitous measurement of bodily motions. The [...] Read more.
This paper proposes a model for recognizing motions performed during rehabilitation exercises for frozen shoulder conditions. The model consists of wearable wireless sensor network (WSN) inertial sensor nodes, which were developed for this study, and enables the ubiquitous measurement of bodily motions. The model employs the back propagation neural network (BPNN) algorithm to compute motion data that are formed in the WSN packets; herein, six types of rehabilitation exercises were recognized. The packets sent by each node are converted into six components of acceleration and angular velocity according to three axes. Motor features such as basic acceleration, angular velocity, and derivative tilt angle were input into the training procedure of the BPNN algorithm. In measurements of thirteen volunteers, the accelerations and included angles of nodes were adopted from possible features to demonstrate the procedure. Five exercises involving simple swinging and stretching movements were recognized with an accuracy of 85%–95%; however, the accuracy with which exercises entailing spiral rotations were recognized approximately 60%. Thus, a characteristic space and enveloped spectrum improving derivative features were suggested to enable identifying customized parameters. Finally, a real-time monitoring interface was developed for practical implementation. The proposed model can be applied in ubiquitous healthcare self-management to recognize rehabilitation exercises. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
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<p>(<b>a</b>) Configuration of WSN ISN components and (<b>b</b>) payload format of a WSN packet for an ISN.</p>
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<p>Rehabilitation exercises for frozen shoulder: (<b>1</b>) scapula exercise; (<b>2</b>) Codman's pendulum exercise; (<b>3</b>) finger wall-climbing exercise; (<b>4</b>) back shoulder circling exercise; (<b>5</b>) towel exercise; and (<b>6</b>) spiral rotation exercise in four steps.</p>
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<p>Portions of the arm for wearing inertial sensor node: (<b>a</b>) Node 1 at upper arm; (<b>b</b>) Node 2 at wrist.</p>
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<p>Variation of signals of Node 2 measured using ISNs for the six exercises. (<b>a</b>) Acceleration group; (<b>b</b>) Included angle group derived by acceleration.</p>
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<p>Variation of signals of Node 2 measured using ISNs for the six exercises. (<b>a</b>) Acceleration group; (<b>b</b>) Included angle group derived by acceleration.</p>
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<p>Flow chart of the WSN-ISN-based measurement and recognition procedure with BPNN.</p>
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<p>Format of entry data set of input packets for machine learning.</p>
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<p>Recognition rates of exercises <span class="html-italic">versus</span> performance goal when various types of input data sets were used: blue (<b>left</b>) bar: 18 sets; green (<b>middle</b>) bar: 36 sets; and red (<b>right</b>) bar: 72 sets.</p>
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<p>Feature distribution in characteristic space of accelerations of all exercises in <span class="html-italic">z</span> axis.</p>
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<p>Frequency histogram of acceleration component of Ex.3 (finger wall-climbing exercise) in which their primary and secondary peaks are marked by circle and box in dash line. (<b>a</b>) Acceleration <span class="html-italic">a<sub>x</sub></span> of Ex.3; (<b>b</b>) Acceleration <span class="html-italic">a<sub>y</sub></span> of Ex.3; (<b>c</b>) Acceleration <span class="html-italic">a<sub>z</sub></span> of Ex.3.</p>
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<p>Frequency histogram of acceleration component of Ex.3 (finger wall-climbing exercise) in which their primary and secondary peaks are marked by circle and box in dash line. (<b>a</b>) Acceleration <span class="html-italic">a<sub>x</sub></span> of Ex.3; (<b>b</b>) Acceleration <span class="html-italic">a<sub>y</sub></span> of Ex.3; (<b>c</b>) Acceleration <span class="html-italic">a<sub>z</sub></span> of Ex.3.</p>
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1476 KiB  
Article
Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature
by Shouyi Yin, Peng Ouyang, Leibo Liu, Yike Guo and Shaojun Wei
Sensors 2015, 15(1), 2161-2180; https://doi.org/10.3390/s150102161 - 19 Jan 2015
Cited by 45 | Viewed by 8344
Abstract
Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to [...] Read more.
Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. Full article
(This article belongs to the Section Physical Sensors)
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<p>Different kinds of traffic signs from GTSRB data set.</p>
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<p>Illustration of the rotation invariant binary pattern based feature computing to achieve fast and robust traffic sign detection.</p>
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<p>Matching rate.</p>
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<p>Processing time.</p>
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<p>The whole computation flow of traffic sign recognition. The image embedded in the graph is from the GTSRB data set.</p>
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<p>Parts of recognition results on GTSRB data set: (<b>a</b>) original image; (<b>b</b>) preprocessing to locate the candidate regions; (<b>c</b>) traffic sign recognition; (<b>e</b>) original image; (<b>f</b>) preprocessing to locate the candidate regions; (<b>g</b>) traffic sign recognition.</p>
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<p>Testing in real conditions to obtain performance averages, and make a comparison with the work by Tang [<a href="#b22-sensors-15-02161" class="html-bibr">22</a>].</p>
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2258 KiB  
Article
Integrated Semantics Service Platform for the Internet of Things: A Case Study of a Smart Office
by Minwoo Ryu, Jaeho Kim and Jaeseok Yun
Sensors 2015, 15(1), 2137-2160; https://doi.org/10.3390/s150102137 - 19 Jan 2015
Cited by 66 | Viewed by 13745
Abstract
The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic [...] Read more.
The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic technologies, we can build integrated semantic systems to support semantic interoperability. In this paper, we propose an integrated semantic service platform (ISSP) to support ontological models in various IoT-based service domains of a smart city. In particular, we address three main problems for providing integrated semantic services together with IoT systems: semantic discovery, dynamic semantic representation, and semantic data repository for IoT resources. To show the feasibility of the ISSP, we develop a prototype service for a smart office using the ISSP, which can provide a preset, personalized office environment by interpreting user text input via a smartphone. We also discuss a scenario to show how the ISSP-based method would help build a smart city, where services in each service domain can discover and exploit IoT resources that are wanted across domains. We expect that our method could eventually contribute to providing people in a smart city with more integrated, comprehensive services based on semantic interoperability. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Interoperability between various service domains in a smart city.</p>
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<p>A schema of the IoT-based service integration ontology (IIO) to support ontologies created from various IoT-based service domains. The rectangle represents classes. The solid line represents a relationship between individuals, and the dashed line represents a relationship between super-class and subclasses.</p>
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<p>The overview of the integrated semantic service server (ISSS).</p>
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<p>System architecture of the ISSP.</p>
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<p>A snapshot of the web-based authoring tool.</p>
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<p>Hierarchy of classes, object properties, and data properties of the IIO: (from left), class hierarchy, object properties hierarchy, and data properties hierarchy</p>
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<p>The service flow for the prototype service for the smart office.</p>
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<p>Classification of the space according to the characteristics.</p>
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<p>Hierarchy of classes and object properties.</p>
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403 KiB  
Article
Sensor Data Security Level Estimation Scheme for Wireless Sensor Networks
by Alex Ramos and Raimir Holanda Filho
Sensors 2015, 15(1), 2104-2136; https://doi.org/10.3390/s150102104 - 19 Jan 2015
Cited by 14 | Viewed by 7863
Abstract
Due to their increasing dissemination, wireless sensor networks (WSNs) have become the target of more and more sophisticated attacks, even capable of circumventing both attack detection and prevention mechanisms. This may cause WSN users, who totally trust these security mechanisms, to think that [...] Read more.
Due to their increasing dissemination, wireless sensor networks (WSNs) have become the target of more and more sophisticated attacks, even capable of circumventing both attack detection and prevention mechanisms. This may cause WSN users, who totally trust these security mechanisms, to think that a sensor reading is secure, even when an adversary has corrupted it. For that reason, a scheme capable of estimating the security level (SL) that these mechanisms provide to sensor data is needed, so that users can be aware of the actual security state of this data and can make better decisions on its use. However, existing security estimation schemes proposed for WSNs fully ignore detection mechanisms and analyze solely the security provided by prevention mechanisms. In this context, this work presents the sensor data security estimator (SDSE), a new comprehensive security estimation scheme for WSNs. SDSE is designed for estimating the sensor data security level based on security metrics that analyze both attack prevention and detection mechanisms. In order to validate our proposed scheme, we have carried out extensive simulations that show the high accuracy of SDSE estimates. Full article
(This article belongs to the Special Issue Wireless Sensor Networks and the Internet of Things)
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<p>Operation flow of the sensor data security estimator (SDSE).</p>
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<p>Key strength probability (<span class="html-italic">P<sub>F</sub></span>) <span class="html-italic">vs</span>. time <span class="html-italic">t</span>, for different values of key strength <span class="html-italic">s</span> and rate <span class="html-italic">f</span> = 394, 875, 793, 722 keys/s.</p>
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<p>Accuracy of the key strength probability (<span class="html-italic">P<sub>F</sub></span>) <span class="html-italic">vs</span>. time <span class="html-italic">t</span>, for different network sizes, for a test rate <span class="html-italic">f</span> = 394, 875, 793, 722 keys/s, and <span class="html-italic">s</span> = 64 bits.</p>
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<p>Key management resilience probability (<span class="html-italic">P<sub>R</sub></span>) vs. amount x of captured nodes, for different values of key overlap <span class="html-italic">q</span>, <span class="html-italic">k</span> = 200 keys and <span class="html-italic">p</span> = 0.33.</p>
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<p>Accuracy of the key management resilience probability (<span class="html-italic">P<sub>R</sub></span>) <span class="html-italic">vs</span>. the number <span class="html-italic">x</span> of captured nodes, for different network sizes, <span class="html-italic">k</span> = 200 keys, <span class="html-italic">p</span> = 0.33 and <span class="html-italic">q</span> = 3.</p>
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<p>Legitimacy probability of a node when the intrusion detection system (IDS) generates a global alarm, <span class="html-italic">P<sub>L</sub></span>(<span class="html-italic">alarm</span>), <span class="html-italic">vs</span>. the consensus parameter <span class="html-italic">m</span>, for different values of <span class="html-italic">P<sub>M</sub></span>, <span class="html-italic">N</span> = 10, <span class="html-italic">P<sub>tp</sub></span> = 0.6 and <span class="html-italic">P<sub>fp</sub></span> = 0.4.</p>
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<p>Accuracy of the legitimacy probability when the IDS raises a global alarm, <span class="html-italic">P<sub>L</sub></span>(<span class="html-italic">alarm</span>), <span class="html-italic">vs</span>. consensus parameter <span class="html-italic">m</span>. For different values of <span class="html-italic">P<sub>M</sub></span>, <span class="html-italic">N</span> = 10 neighbors, <span class="html-italic">P<sub>tp</sub></span> = 0.6 and <span class="html-italic">P<sub>fp</sub></span> = 0.4.</p>
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<p>Security degree of a node for which a global alarm is not generated (<span class="html-italic">A</span>−) <span class="html-italic">vs.</span> the amount x of captured nodes, for different values of base rate (<span class="html-italic">P<sub>M</sub></span>) and a network with 1000 sensor nodes.</p>
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<p>Accuracy of the security degree of a node for which a global alarm has not been generated (<span class="html-italic">A</span>−) vs. the number <span class="html-italic">x</span> of captured nodes, for different values of base rate (<span class="html-italic">P<sub>M</sub></span>) and a network with 1000 nodes.</p>
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5834 KiB  
Article
The Enhanced Formaldehyde-Sensing Properties of P3HT-ZnO Hybrid Thin Film OTFT Sensor and Further Insight into Its Stability
by Huiling Tai, Xian Li, Yadong Jiang, Guangzhong Xie and Xiaosong Du
Sensors 2015, 15(1), 2086-2103; https://doi.org/10.3390/s150102086 - 19 Jan 2015
Cited by 40 | Viewed by 10234
Abstract
A thin-film transistor (TFT) having an organic–inorganic hybrid thin film combines the advantage of TFT sensors and the enhanced sensing performance of hybrid materials. In this work, poly(3-hexylthiophene) (P3HT)-zinc oxide (ZnO) nanoparticles’ hybrid thin film was fabricated by a spraying process as the [...] Read more.
A thin-film transistor (TFT) having an organic–inorganic hybrid thin film combines the advantage of TFT sensors and the enhanced sensing performance of hybrid materials. In this work, poly(3-hexylthiophene) (P3HT)-zinc oxide (ZnO) nanoparticles’ hybrid thin film was fabricated by a spraying process as the active layer of TFT for the employment of a room temperature operated formaldehyde (HCHO) gas sensor. The effects of ZnO nanoparticles on morphological and compositional features, electronic and HCHO-sensing properties of P3HT-ZnO thin film were systematically investigated. The results showed that P3HT-ZnO hybrid thin film sensor exhibited considerable improvement of sensing response (more than two times) and reversibility compared to the pristine P3HT film sensor. An accumulation p-n heterojunction mechanism model was developed to understand the mechanism of enhanced sensing properties by incorporation of ZnO nanoparticles. X-ray photoelectron spectroscope (XPS) and atomic force microscopy (AFM) characterizations were used to investigate the stability of the sensor in-depth, which reveals the performance deterioration was due to the changes of element composition and the chemical state of hybrid thin film surface induced by light and oxygen. Our study demonstrated that P3HT-ZnO hybrid thin film TFT sensor is beneficial in the advancement of novel room temperature HCHO sensing technology. Full article
(This article belongs to the Special Issue Modern Technologies for Sensing Pollution in Air, Water, and Soil)
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<p>A schematic drawing of spraying process and organic thin-film transistor (OTFT) devices.</p>
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<p>Schematic illustration of the experimental setup.</p>
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<p>SEM graphs of sprayed (<b>a</b>) P3HT film; (<b>b</b>) ZnO film; (<b>c</b>) P3HT-ZnO hybrid film.</p>
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<p>UV-Vis absorption spectra of P3HT, ZnO and P3HT-ZnO thin films.</p>
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<p>S 2p XPS spectra of P3HT and P3HT-ZnO thin films.</p>
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<p>The typical (<b>a</b>) output (<span class="html-italic">V<sub>GS</sub></span> = −30 V) and (<b>b</b>) transfer (<span class="html-italic">V<sub>DS</sub></span> = −50 V) characteristics curves of P3HT and P3HT-ZnO films based OTFT sensors.</p>
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<p>Real-time response curves of P3HT and P3HT-ZnO film OTFT sensors exposed to 100 ppm HCHO at room temperature.</p>
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<p>The (<b>a</b>) output and (<b>b</b>) transfer characteristics curves of P3HT-ZnO film OTFT sensor exposed to 100 ppm HCHO compared with those in air.</p>
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<p>(<b>a</b>) The transient response of P3HT-ZnO hybrid film sensor when exposed to different HCHO concentrations, inset was the curve of response values <span class="html-italic">versus</span> concentration; (<b>b</b>) the detection limit (4 ppm) measurement curve at room temperature.</p>
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328 KiB  
Review
A Survey of Online Activity Recognition Using Mobile Phones
by Muhammad Shoaib, Stephan Bosch, Ozlem Durmaz Incel, Hans Scholten and Paul J.M. Havinga
Sensors 2015, 15(1), 2059-2085; https://doi.org/10.3390/s150102059 - 19 Jan 2015
Cited by 407 | Viewed by 24947
Abstract
Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous [...] Read more.
Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research. Full article
(This article belongs to the Special Issue HCI In Smart Environments)
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<p>Activity recognition steps.</p>
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<p>Local approach for activity recognition on mobile phones.</p>
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1873 KiB  
Article
Hyperspectral Imagery Super-Resolution by Compressive Sensing Inspired Dictionary Learning and Spatial-Spectral Regularization
by Wei Huang, Liang Xiao, Hongyi Liu and Zhihui Wei
Sensors 2015, 15(1), 2041-2058; https://doi.org/10.3390/s150102041 - 19 Jan 2015
Cited by 25 | Viewed by 6450
Abstract
Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a [...] Read more.
Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation. Full article
(This article belongs to the Section Remote Sensors)
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<p>Flowchart of the proposed method.</p>
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<p>Schematic view of the HSI data. Left: A three dimensional HSI data. Right: Reflected spectral curve at the corresponding pixels of each band in the HSI.</p>
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<p>Flowchart of concrete process of calculating IPD with the 3 × 3 spatial window.</p>
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<p>Experimental results of PaviaU HSI (color composite of R: 80, G: 28, B: 9). (<b>a</b>) the false color image of original HSI; (<b>b</b>) bicubic interpolation; (<b>c</b>) PCA fusion method; (<b>d</b>) WT fusion method; (<b>e</b>) P + XS fusion method; (<b>f</b>) SR-SR method; (<b>g</b>) the proposed method.</p>
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<p>Spectral curves of two typical pixels, one is metal material (the coordinate located at (72,129)), the other is meadow material (the coordinate located at (127,173)). (<b>a</b>) the spectral curves of the metal material; (<b>b</b>) the pixel curves of the metal material in difference HSI; (<b>c</b>) the spectral curves of the meadow material; (<b>d</b>) the pixel curves of the meadow material in difference HSI.</p>
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<p>Experimental results of PaviaU HSI (color composite of R: 80, G: 28, B: 9). (<b>a</b>) the false color image of the original HSI; (<b>b</b>) bicubic interpolation; (<b>c</b>) PCA fusion method; (<b>d</b>) WT fusion method; (<b>e</b>) P + XS fusion method; (<b>f</b>) SR-SR method; (<b>g</b>) the proposed method.</p>
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<p>Experimental results of Letter paper HSI dataset (color composite of R: 31, G: 20, B: 8). (<b>a</b>) original LR HSI; (<b>b</b>) bicubic interpolation method; (<b>c</b>) SR-SR method; (<b>d</b>) the proposed method.</p>
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<p>The cropped portions of the <a href="#f5-sensors-15-02041" class="html-fig">Figure 5b–d</a> (color composite of R: 31, G: 20, B: 8). (<b>a</b>) bicubic interpolation method; (<b>b</b>) SR-SR method; (<b>c</b>) the proposed method.</p>
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<p>Experimental results of FS-NUST HSI dataset (color composite of R: 52, G: 32, B: 13). (<b>a</b>) original LR HSI; (<b>b</b>) bicubic interpolation method; (<b>c</b>) SR-SR method; (<b>d</b>) the proposed method.</p>
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1124 KiB  
Article
Double Cluster Heads Model for Secure and Accurate Data Fusion in Wireless Sensor Networks
by Jun-Song Fu and Yun Liu
Sensors 2015, 15(1), 2021-2040; https://doi.org/10.3390/s150102021 - 19 Jan 2015
Cited by 31 | Viewed by 5452
Abstract
Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data [...] Read more.
Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other. Then, the results are sent to the base station where the dissimilarity coefficient is computed. If the dissimilarity coefficient of the two data fusion results exceeds the threshold preset by the users, the cluster heads will be added to blacklist, and the cluster heads must be reelected by the sensor nodes in a cluster. Meanwhile, feedback is sent from the base station to the reputation and trust system, which can help us to identify and delete the compromised sensor nodes in time. Through a series of extensive simulations, we found that the DCHM performed very well in data fusion security and accuracy. Full article
(This article belongs to the Section Sensor Networks)
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<p>Topology of WSNs.</p>
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<p>A brief mechanism to defend against threats.</p>
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<p>Overview of the Framework.</p>
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<p>Procedure performed by cluster head.</p>
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<p>Procedure performed by the candidate.</p>
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<p>Procedure performed by ordinary sensor nodes.</p>
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<p>Decay function <span class="html-italic">β</span>.</p>
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<p>The procedure performed by the base station.</p>
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<p>Probability of selecting compromised sensor nodes as cluster heads.</p>
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3975 KiB  
Article
Design and Testing of an Agricultural Implement for Underground Application of Rodenticide Bait
by Hugo Malón, A. Javier Aguirre, Antonio Boné, Mariano Vidal and F. Javier García-Ramos
Sensors 2015, 15(1), 2006-2020; https://doi.org/10.3390/s150102006 - 16 Jan 2015
Cited by 5 | Viewed by 6748
Abstract
An agricultural implement for underground application of rodenticide bait to control the Mediterranean pocket gopher (Microtus Duodecimcostatus) in fruit orchards has been designed and tested. The main objective of this research was to design and test the implement by using the [...] Read more.
An agricultural implement for underground application of rodenticide bait to control the Mediterranean pocket gopher (Microtus Duodecimcostatus) in fruit orchards has been designed and tested. The main objective of this research was to design and test the implement by using the finite element method (FEM) and considering a range of loads generated on most commonly used furrow openers in agricultural implements. As a second step, the prototype was tested in the field by analysing the effects of forward speed and application depth on the mechanical behaviour of the implement structure. The FEM was used in the design phase and a prototype was manufactured. The structural strains on the prototype chassis under working conditions were tested by using strain gauges to validate the design phase. Three forward speeds (4.5, 5.5, and 7.0 km/h), three application depths (0.12, 0.15, and 0.17 m), and two types of soil (clayey-silty-loam and clayey-silty-sandy) were considered. The prototype was validated successfully by analysing the information obtained from the strain gauges. The Von Mises stresses indicated a safety coefficient of 1.9 for the most critical load case. Although both forward speed and application depth had a significant effect on the stresses generated on the chassis, the latter parameter critically affected the structural behaviour of the implement. The effects of the application depth on the strains were linear such that strains increased with depth. In contrast, strains remained roughly constant regardless of variation in the forward speed. Full article
(This article belongs to the Special Issue Sensors in Agriculture and Forestry)
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<p>Components of the agricultural implement prototype: (1) fixed chassis; (2) mobile chassis; (3) furrow opener; (4) rodenticide bait dispenser; (5) metal roller.</p>
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<p>Finite element model of the prototype for underground application of rodenticide bait. Areas of load application (furrow opener, in blue) and boundary conditions (attachment to the tractor, in red).</p>
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<p>Results of Von Mises stress from the load case in which a force of 2610 N was applied to the furrow opener.</p>
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<p>Strain gauge locations on the agricultural implement prototype during the experimental tests.</p>
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<p>Strains (με) recorded in the experimental test in the field with loam texture (field 1), at a forward speed of 7 km/h and an application depth of 0.17 m.</p>
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<p>Strains (με) recorded in the experimental test in field 2 (sandy) at a <span class="html-italic">forward speed</span> of 4.5 km/h and an <span class="html-italic">application depth</span> of 0.15 m. Rosette: Channels 1–3; 3 Linear strain gauges: Channels 4, 6, and 7; Reference strain gauge: Channel 5.</p>
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<p>95% confidence intervals of all micro-strains standardised according to soil type.</p>
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<p>95% confidence intervals of all micro-strains standardised according to soil texture at different forward speeds.</p>
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<p>95% confidence intervals of all micro-strains standardised according to soil texture at different application depths.</p>
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5164 KiB  
Article
Design and Fabrication of Interdigital Nanocapacitors Coated with HfO2
by Gabriel González, Eleazar Samuel Kolosovas-Machuca, Edgar López-Luna, Heber Hernández-Arriaga and Francisco Javier González
Sensors 2015, 15(1), 1998-2005; https://doi.org/10.3390/s150101998 - 16 Jan 2015
Cited by 18 | Viewed by 15734
Abstract
In this article nickel interdigital capacitors were fabricated on top of silicon substrates. The capacitance of the interdigital capacitor was optimized by coating the electrodes with a 60 nm layer of HfO2. An analytical solution of the capacitance was compared to [...] Read more.
In this article nickel interdigital capacitors were fabricated on top of silicon substrates. The capacitance of the interdigital capacitor was optimized by coating the electrodes with a 60 nm layer of HfO2. An analytical solution of the capacitance was compared to electromagnetic simulations using COMSOL and with experimental measurements. Results show that modeling interdigital capacitors using Finite Element Method software such as COMSOL is effective in the design and electrical characterization of these transducers. Full article
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<p>Geometry of an interdigital capacitor.</p>
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<p>Capacitance as a function of <span class="html-italic">η</span> for <span class="html-italic">N</span> = 32 (Black), <span class="html-italic">N</span> = 40 (Red) and <span class="html-italic">N</span> = 50 (Blue). We used <span class="html-italic">ϵ</span><sub>0</sub> = 8.8 × 10<sup>−12</sup> F/m, <span class="html-italic">L</span> = 8 × 10<sup>−3</sup> m, <span class="html-italic">ϵ<sub>S</sub></span> = 11.7 F/m, <span class="html-italic">ϵ</span><sub>1</sub> = 1 y <span class="html-italic">λ</span> = 400 × 10<sup>−6</sup> m.</p>
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<p>The 3D simulation of the electrostatic potential for an Interdigital capacitor. We used <span class="html-italic">ϵ</span><sub>0</sub> = 8.8 × 10<sup>−12</sup> F/m, <span class="html-italic">L</span> = 8 × 10<sup>−3</sup> m, <span class="html-italic">ϵ<sub>S</sub></span> = 11.7 F/m, <span class="html-italic">ϵ</span><sub>1</sub> = 1 y <span class="html-italic">λ</span> = 400 × 10<sup>−6</sup> m.</p>
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<p>Numerical simulations for the capacitance for the IDCs for <span class="html-italic">N</span> = 32, <span class="html-italic">N</span> = 40 and <span class="html-italic">N</span> = 50 without and with hafnium coating. (<b>a</b>) Capacitance of the IDCs without hafnium coating; (<b>b</b>) Capacitance of the IDCs with hafnium coating.</p>
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<p>The figure shows on the left the fabrication process of the IDC, including (<b>a</b>) the silicon substrate; (<b>b</b>) photolithography; (<b>c</b>) evaporation of metal (Ni) and (<b>d</b>) HfO<sub>2</sub> deposition, and on the right the final IDC device.</p>
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<p>Plots showing the impedance for the following values: (<b>a</b>) <span class="html-italic">N</span> = 32, <span class="html-italic">N</span> = 40 and <span class="html-italic">N</span> = 50, (<b>b</b>) IDC1 with and without HfO<sub>2</sub>, (<b>c</b>) IDC2 with and without HfO<sub>2</sub> and (<b>d</b>) IDC1 with and without HfO<sub>2</sub>.</p>
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<p>The current <span class="html-italic">vs.</span> voltage measurements of the IDC sensor with <span class="html-italic">N</span> = 40 incorporating Bovine Serum Albumin (BSA) and Anti-Bovine Serum Albumin (Anti-BSA). Note the increase in the conductivity when adding BSA or Anti-BSA to the sensor.</p>
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923 KiB  
Article
A Novel Joint Spatial-Code Clustered Interference Alignment Scheme for Large-Scale Wireless Sensor Networks
by Zhilu Wu, Lihui Jiang, Guanghui Ren, Nan Zhao and Yaqin Zhao
Sensors 2015, 15(1), 1964-1997; https://doi.org/10.3390/s150101964 - 16 Jan 2015
Cited by 10 | Viewed by 5244
Abstract
Interference alignment (IA) has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs). However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will [...] Read more.
Interference alignment (IA) has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs). However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC)-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN) code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER) expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS) algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance. Full article
(This article belongs to the Section Sensor Networks)
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<p>The BPSK transmission system in the <span class="html-italic">K</span>-user MIMO interference channel.</p>
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<p>The JSCC-IA scheme with <span class="html-italic">G</span> groups and <span class="html-italic">K</span><sub>0</sub> users in each group.</p>
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<p>The propagation delay between two users.</p>
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<p>The average theoretical and experimental BER performances of the conventional IA scheme and the JSCC-IA schemes with identical/different propagation delays in the (4 × 4, 1)<sup>10</sup> symmetric networks.</p>
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<p>The average experimental BER performances of the conventional IA scheme and the JSCC-IA schemes with identical/different propagation delays in the (4 × 4, 1)<sup>20</sup> symmetric networks.</p>
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<p>The average experimental BER performances of the conventional IA scheme and the JSCC-IA schemes with identical/different propagation delays in the (6 × 6, 2)<sup>18</sup> symmetric networks.</p>
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<p>The average minimal received SINR of the conventional IA scheme and the JSCC-IA schemes with identical/different propagation delays in the (4 × 4, 1)<sup>20</sup> symmetric networks.</p>
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<p>The average minimal received SINR of the conventional IA scheme and the JSCC-IA schemes with identical/different propagation delays in the (6 × 6, 2)<sup>18</sup> symmetric networks.</p>
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<p>The average received SINR of the conventional IA scheme and the JSCC-IA schemes with identical/different propagation delays in the (4 × 4, 1)<sup>20</sup> symmetric networks.</p>
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1561 KiB  
Article
A Novel Vibration Mode Testing Method for Cylindrical Resonators Based on Microphones
by Yongmeng Zhang, Yulie Wu, Xuezhong Wu, Xiang Xi and Jianqiu Wang
Sensors 2015, 15(1), 1954-1963; https://doi.org/10.3390/s150101954 - 16 Jan 2015
Cited by 13 | Viewed by 6607
Abstract
Non-contact testing is an important method for the study of the vibrating characteristic of cylindrical resonators. For the vibratory cylinder gyroscope excited by piezo-electric electrodes, mode testing of the cylindrical resonator is difficult. In this paper, a novel vibration testing method for cylindrical [...] Read more.
Non-contact testing is an important method for the study of the vibrating characteristic of cylindrical resonators. For the vibratory cylinder gyroscope excited by piezo-electric electrodes, mode testing of the cylindrical resonator is difficult. In this paper, a novel vibration testing method for cylindrical resonators is proposed. This method uses a MEMS microphone, which has the characteristics of small size and accurate directivity, to measure the vibration of the cylindrical resonator. A testing system was established, then the system was used to measure the vibration mode of the resonator. The experimental results show that the orientation resolution of the node of the vibration mode is better than 0.1°. This method also has the advantages of low cost and easy operation. It can be used in vibration testing and provide accurate results, which is important for the study of the vibration mode and thermal stability of vibratory cylindrical gyroscopes. Full article
(This article belongs to the Section Physical Sensors)
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<p>(<b>a</b>) Structure of the vibratory cylinder gyroscope; (<b>b</b>) Schematic of the operation principle.</p>
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<p>(<b>a</b>) Cylindrical resonator in a cap; (<b>b</b>) Acoustic pressure contour of the vibratory cylinder gyroscope.</p>
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<p>(<b>a</b>) Schematic of the testing system; (<b>b</b>) Photograph of the testing system.</p>
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<p>Schematic of the pasting of the piezoelectric electrodes.</p>
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<p>Electrical diagram of the driving circuit.</p>
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<p>Testing results of the piezoelectric electrode and the microphone.</p>
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<p>Testing results of the vibration mode of the cylindrical resonator.</p>
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<p>Testing the resolution of the system.</p>
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358 KiB  
Article
Fluorescent Cellular Assay for Screening Agents Inhibiting Pseudomonas aeruginosa Adherence
by Libuše Nosková, Božena Kubíčková, Lucie Vašková, Barbora Bláhová, Michaela Wimmerová, Marie Stiborová and Petr Hodek
Sensors 2015, 15(1), 1945-1953; https://doi.org/10.3390/s150101945 - 16 Jan 2015
Cited by 4 | Viewed by 5898
Abstract
Antibodies against Pseudomonas aeruginosa (PA) lectin, PAIIL, which is a virulence factor mediating the bacteria binding to epithelium cells, were prepared in chickens and purified from egg yolks. To examine these antibodies as a prophylactic agent preventing the adhesion of PA we developed [...] Read more.
Antibodies against Pseudomonas aeruginosa (PA) lectin, PAIIL, which is a virulence factor mediating the bacteria binding to epithelium cells, were prepared in chickens and purified from egg yolks. To examine these antibodies as a prophylactic agent preventing the adhesion of PA we developed a well plate assay based on fluorescently labeled bacteria and immortalized epithelium cell lines derived from normal and cystic fibrosis (CF) human lungs. The antibodies significantly inhibited bacteria adhesion (up to 50%) in both cell lines. In agreement with in vivo data, our plate assay showed higher susceptibility of CF cells towards the PA adhesion as compared to normal epithelium. This finding proved the reliability of the developed experimental system. Full article
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<p>Western blot analysis of <span class="html-italic">Pseudomonas aeruginosa</span> lectin (PAIIL). Samples containing PAIIL, cell lysate of PA grown in rich medium PS (line <b>1</b>) or in minimal mineral medium M9 (line <b>2</b>) and recombinantly expressed PAIIL (line <b>3</b>) were separated on reduced 10% SDS PAGE. Separated proteins were electrotransfered onto PVDF membranes and developed with pre-immune IgY (panel <b>A</b>) and anti-PAIIL IgY (panel <b>B</b>) at 30 μg/mL concentrations. The arrow marks the position of PAIIL protein band.</p>
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<p>Micrographs of epithelium cells (<b>A</b>, unstained; <b>C</b>, stained with PKH67) and <span class="html-italic">Pseudomonas aeruginosa</span> (<b>B</b>, unstained; <b>D</b>, stained with PKH26). Fluorescent cells were examined on a Nikon Eclipse microscope equipped with a filter 31001 FITC C87701 for epithelium cells (PKH67) and filter 31002 RdiI C87702 for PA (PKH26).</p>
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<p>Calibration plots of PKH stained cells. Relative fluorescence of cells was determined on a spectrofluorometer (Tecan Infinite M200 Pro) set for PKH67 (Ex 522 nm, Em 569 nm) and for PKH26 (Ex 470 nm, Em 505 nm). Epithelium cells CuFi and Nuli are shown in plots <b>A</b> and <b>B</b>, respectively. Plot <b>C</b> depicts the calibration line for fluorescently labeled PA. Relative fluorescence of background (blank) was subtracted from that of cell samples. The values are means ± SD of five independent determinations.</p>
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<p>Time course of the PA adherence on an epithelial cell monolayer. Fluorescently labeled PA bacteria (1.5 × 10<sup>7</sup> CFU) were applied to wells and incubated at room temperature. At distinct time points bacteria were removed and the wells washed with PBS. Adhered PA bacteria were quantified using spectrofluorometer (Tecan Infinite M200 Pro) set for PKH26 (Ex 470 nm, Em 505 nm). Plotted data are means ± SD of four independent incubations.</p>
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<p>Adhesion of PA to epithelium cells in the presence of IgYs and saccharides. Monolayers of NuLi (grey bars) and CuFi (black bars) were exposed to PA suspension containing anti-PAIIL IgY (<b>S-IgY</b>), pre-immune IgY (<b>C-IgY</b>), L-fucose (<b>Fuc</b>), D-galactose (<b>Gal</b>) or PBS as a control. After a 2-hour incubation non-adhered bacteria were discarded and retained PA and epithelial cells were quantified using spectrofluorometer (Tecan Infinite M200 Pro). Results are expressed as a relative fluorescence ratio of PA/NuLi or PA/CuFi. Plotted data are means ± SD of four independent incubations.</p>
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3326 KiB  
Article
Improvement of the Trapezoid Method Using Raw Landsat Image Digital Count Data for Soil Moisture Estimation in the Texas (USA) High Plains
by Sanaz Shafian and Stephan J. Maas
Sensors 2015, 15(1), 1925-1944; https://doi.org/10.3390/s150101925 - 16 Jan 2015
Cited by 9 | Viewed by 6277
Abstract
Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (i.e., potential crop yield). Hence, the estimation of soil moisture is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. [...] Read more.
Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (i.e., potential crop yield). Hence, the estimation of soil moisture is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. Extensive efforts to exploit the potential of remotely sensed observations to help quantify this complex variable are ongoing. This study aims at developing a new index, the Thermal Ground cover Moisture Index (TGMI), for estimating soil moisture content. This index is based on empirical parameterization of the relationship between raw image digital count (DC) data in the thermal infrared spectral band and ground cover (determined from raw image digital count data in the red and near-infrared spectral bands).The index uses satellite-derived information only, and the potential for its operational application is therefore great. This study was conducted in 18 commercial agricultural fields near Lubbock, TX (USA). Soil moisture was measured in these fields over two years and statistically compared to corresponding values of TGMI determined from Landsat image data. Results indicate statistically significant correlations between TGMI and field measurements of soil moisture (R2 = 0.73, RMSE = 0.05, MBE = 0.17 and AAE = 0.049), suggesting that soil moisture can be estimated using this index. It was further demonstrated that maps of TGMI developed from Landsat imagery could be constructed to show the relative spatial distribution of soil moisture across a region. Full article
(This article belongs to the Section Remote Sensors)
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<p>Result of plotting vegetation <span class="html-italic">GC</span> (result of plotting DC in Red and NIR spectral bands of Lnadsat-7) <span class="html-italic">versus</span> (<b>a</b>) surface temperature T<sub>s</sub>; (<b>b</b>) raw thermal infrared digital count data (<span class="html-italic">TIRDC</span>) for pixels comprising a medium-resolution multispectral satellite image (Landsat-7) of an agricultural region.</p>
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<p>Diagrammatic representation of the distribution of vegetation <span class="html-italic">GC versus</span> raw thermal infrared digital count data (<span class="html-italic">TIRDC</span>) like that presented in <a href="#f1-sensors-15-01925" class="html-fig">Figure 1b</a>.</p>
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<p><span class="html-italic">TIRDC<sub>norm</sub></span>–<span class="html-italic">GC</span> space used for determining the vertex <span class="html-italic">d</span> of the trapezoid. Line “B” passes through point <span class="html-italic">a</span> with a slope of −1 and serves as a baseline for measuring perpendicular distance across the <span class="html-italic">TIRDC<sub>norm</sub></span>–<span class="html-italic">GC</span> space. In this example, point <span class="html-italic">f</span> is the point in the distribution of observed pixel values that has the maximum perpendicular distance from line “B”.</p>
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<p>An illustration of the <span class="html-italic">TIRDC<sub>norm</sub></span>–<span class="html-italic">GC</span> space used for determining Thermal Ground Cover Moisture Index (<span class="html-italic">TGMI</span>). For a given pixel, CD and AB are used to calculate the <span class="html-italic">TGMI</span>.</p>
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<p>Map and experiment stations in the study area.</p>
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<p>Plots of <span class="html-italic">GC</span> as functions of either <span class="html-italic">TIRDC</span> and T<sub>s</sub> for each Landsat image acquisition.</p>
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<p>Identification of <span class="html-italic">TIRDC<sub>max</sub></span> and <span class="html-italic">TIRDC<sub>min</sub></span> used to normalized <span class="html-italic">TIRDC</span> values in the <span class="html-italic">TIRDC-GC</span> scatterplot.</p>
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<p>Simple linear regression between <span class="html-italic">TIRDC<sub>norm</sub></span> and T<sub>s</sub>,<sub>norm</sub>.</p>
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<p>Dry edge slopes for the 10 images used in the study.</p>
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2504 KiB  
Article
A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios
by João C. Monteiro and Jaime S. Cardoso
Sensors 2015, 15(1), 1903-1924; https://doi.org/10.3390/s150101903 - 16 Jan 2015
Cited by 5 | Viewed by 5490
Abstract
Humans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is [...] Read more.
Humans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is severely compromised in non-ideal image acquisition scenarios. In an attempt to deal with conditions, such as occlusion and heterogeneous illumination, we propose a new approach motivated by the global precedent hypothesis of the human brain’s cognitive mechanisms of perception. An automatic modeling of SIFT keypoint descriptors using a Gaussian mixture model (GMM)-based universal background model method is proposed. A decision is, then, made in an innovative hierarchical sense, with holistic information gaining precedence over a more detailed local analysis. The algorithm was tested on the ORL, ARand Extended Yale B Face databases and presented state-of-the-art performance for a variety of experimental setups. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic representation of the proposed algorithm and its main blocks: (<b>a</b>) training of the universal background models using data from multiple individuals; (<b>b</b>) maximum <span class="html-italic">a posteriori</span> (MAP) adaptation of the universal background models (UBM) to generate individual specific models; and (<b>c</b>) testing with new data from unknown sources.</p>
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<p>Example images from two subjects of the ORLdatabase.</p>
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<p>All images from a single subject enrolled in the Extended Yale B database. Images (<b>a</b>) to (<b>e</b>) correspond to Subsets 1 to 5, respectively.</p>
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<p>Example images from one subject of the ARdatabase.</p>
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<p>Main results obtained with the proposed methodology for the sunglasses and scarf images of the ORL face database. Each plotted point represent a specific value of parameter <span class="html-italic">θ<sub>l</sub></span>, ranging from [0, ∞].</p>
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<p>Main results obtained with the proposed methodology for Subsets 2–5 of the Extended Yale B Face database.</p>
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<p>Main results obtained with the proposed methodology for the sunglasses and scarf images of the AR face database. Each plotted point represent a specific value of parameter θ<span class="html-italic"><sub>l</sub></span>, ranging from [0, ∞].</p>
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3118 KiB  
Article
Development of Fabric-Based Chemical Gas Sensors for Use as Wearable Electronic Noses
by Thara Seesaard, Panida Lorwongtragool and Teerakiat Kerdcharoen
Sensors 2015, 15(1), 1885-1902; https://doi.org/10.3390/s150101885 - 16 Jan 2015
Cited by 86 | Viewed by 12528
Abstract
Novel gas sensors embroidered into fabric substrates based on polymers/ SWNT-COOH nanocomposites were proposed in this paper, aiming for their use as a wearable electronic nose (e-nose). The fabric-based chemical gas sensors were fabricated by two main processes: drop coating and embroidery. Four [...] Read more.
Novel gas sensors embroidered into fabric substrates based on polymers/ SWNT-COOH nanocomposites were proposed in this paper, aiming for their use as a wearable electronic nose (e-nose). The fabric-based chemical gas sensors were fabricated by two main processes: drop coating and embroidery. Four potential polymers (PVC, cumene-PSMA, PSE and PVP)/functionalized-SWCNT sensing materials were deposited onto interdigitated electrodes previously prepared by embroidering conductive thread on a fabric substrate to make an optimal set of sensors. After preliminary trials of the obtained sensors, it was found that the sensors yielded a electrical resistance in the region of a few kilo-Ohms. The sensors were tested with various volatile compounds such as ammonium hydroxide, ethanol, pyridine, triethylamine, methanol and acetone, which are commonly found in the wastes released from the human body. These sensors were used to detect and discriminate between the body odors of different regions and exist in various forms such as the urine, armpit and exhaled breath odor. Based on a simple pattern recognition technique, we have shown that the proposed fabric-based chemical gas sensors can discriminate the human body odor from two persons. Full article
(This article belongs to the Section Chemical Sensors)
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<p>Structures of polyvinyl chloride (PVC), cumene terminated polystyrene-co-maleic anhydride (cumene-PSMA), poly(styrene-co-maleic acid) partial isobutyl/methyl mixed ester (PSE) and polyvinylpyrrolidone (PVP).</p>
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<p>Pictures (<b>top</b>) and diagram (<b>bottom</b>) of the fabric-based chemical gas sensors.</p>
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<p>Body odor collection from volunteers for measurement.</p>
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<p>Static measurement system for detecting volatile organic compounds.</p>
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<p>Schematic diagram of the fabric-based e-nose system.</p>
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<p>Schematic circuit diagram of data acquisition for the fabric-based e-nose.</p>
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<p>SEM pictures of (<b>a</b>) the surface of conductive thread (functioning as interdigitate electrodes) embroidered on the cotton satin fabric substrate at a magnification of 30×; (<b>b</b>) the surface of cotton satin fabric substrate at a magnification of 100×; (<b>c</b>) the cross-section of the conductive thread and the cotton satin fabrics as coated by a thick film of the polymer/SWNT-COOH nanocomposites materials 300×; and (<b>d</b>) the cross-section at 600× magnification.</p>
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<p>The average of percent change in resistance of fabric-based chemical gas sensors in the static measurement system when exposed to ammonium hydroxide, triethylamine, ethanol, pyridine, methanol and acetone at the concentrations of 50–1000 ppm.</p>
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<p>The dynamic response to urine odor by four fabric-based chemical gas sensors.</p>
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2986 KiB  
Article
Resource-Efficient Fusion over Fading and Non-Fading Reporting Channels for Cooperative Spectrum Sensing
by Dayan Adionel Guimarães and Guilherme Pedro Aquino
Sensors 2015, 15(1), 1861-1884; https://doi.org/10.3390/s150101861 - 16 Jan 2015
Cited by 8 | Viewed by 4944
Abstract
Recently, a novel resource-efficient technique for the reporting channel transmissions in cooperative spectrum sensing was proposed. In this technique, secondary users are allowed to simultaneously send their local decisions to the fusion center, saving time and frequency resources. Expressions for the probabilities of [...] Read more.
Recently, a novel resource-efficient technique for the reporting channel transmissions in cooperative spectrum sensing was proposed. In this technique, secondary users are allowed to simultaneously send their local decisions to the fusion center, saving time and frequency resources. Expressions for the probabilities of detection and false alarm for the unitary-gain AWGN reporting channels were derived, while simulation results were given for both the AWGN and Rayleigh fading channels. Here, we provide an expression that is applicable to AWGN channels with different real-valued gains and to time-varying real-valued gains. A simple suboptimum receiver is proposed for the general complex-valued fading and non-fading channels, with an improved performance in the low signal-to-noise ratio condition. Numerical results are shown for both the AWGN and Rayleigh fading reporting channels, demonstrating the accuracy of the derived expressions and the attractive performance of the proposed receiver. Full article
(This article belongs to the Section Sensor Networks)
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<p>A realization of the received symbols (or levels), the likelihood functions and decision regions. Real valued channel gains are considered.</p>
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<p>Spectrum sensing performance for the AWGN reporting channel. <span class="html-italic">M</span> = 3 and <span class="html-italic">K</span> = 1 (<b>top</b>), <span class="html-italic">K</span> = 2 (<b>middle</b>) and <span class="html-italic">K</span> = 3 (<b>bottom</b>).</p>
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<p>Spectrum sensing performance for the AWGN reporting channel. <span class="html-italic">M</span> = 3 and <span class="html-italic">K</span> = 1 (<b>top</b>), <span class="html-italic">K</span> = 2 (<b>middle</b>) and <span class="html-italic">K</span> = 3 (<b>bottom</b>).</p>
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<p>Spectrum sensing performance for the AWGN reporting channel. <span class="html-italic">M</span> = 5 and <span class="html-italic">K</span> = 1 (<b>top</b>); <span class="html-italic">K</span> = 3 (<b>middle</b>); and <span class="html-italic">K</span> = 5 (<b>bottom</b>).</p>
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<p>Spectrum sensing performance for the AWGN reporting channel. <span class="html-italic">M</span> = 5 and <span class="html-italic">K</span> = 1 (<b>top</b>); <span class="html-italic">K</span> = 3 (<b>middle</b>); and <span class="html-italic">K</span> = 5 (<b>bottom</b>).</p>
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<p>Spectrum sensing performance for the real-valued Rayleigh reporting channel. <span class="html-italic">M</span> = 3 and <span class="html-italic">K</span> = 1 (<b>top</b>); <span class="html-italic">K</span> = 2 (<b>middle</b>); and <span class="html-italic">K</span> = 3 (<b>bottom</b>).</p>
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<p>Spectrum sensing performance for the real-valued Rayleigh reporting channel. <span class="html-italic">M</span> = 5 and <span class="html-italic">K</span> = 1 (<b>top</b>); <span class="html-italic">K</span> = 3 (<b>middle</b>); and <span class="html-italic">K</span> = 5 (<b>bottom</b>).</p>
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<p>Received symbol constellation and decision regions for a given channel realization. Shaded areas with horizontal lines correspond to <span class="html-italic">H</span><sub>1</sub> and the approximate ML rule. Shaded areas with vertical lines correspond to <span class="html-italic">H</span><sub>1</sub> and the true ML rule.</p>
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<p>Proportion of matching between the true ML decisions and the approximate ML decisions with (solid) and without (dashed) correction for the low <span class="html-italic">E</span><sub>b</sub>/<span class="html-italic">N</span><sub>0</sub> regime.</p>
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<p>Suboptimum receiver with improved performance in low SNR.</p>
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<p>Spectrum sensing performance for the complex-valued Rayleigh reporting channel. <span class="html-italic">M</span> = 3 and <span class="html-italic">K</span> = 1 (<b>top</b>); <span class="html-italic">K</span> = 2 (<b>middle</b>); and <span class="html-italic">K</span> = 3 (<b>bottom</b>).</p>
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<p>Spectrum sensing performance for the complex-valued Rayleigh reporting channel. <span class="html-italic">M</span> = 3 and <span class="html-italic">K</span> = 1 (<b>top</b>); <span class="html-italic">K</span> = 2 (<b>middle</b>); and <span class="html-italic">K</span> = 3 (<b>bottom</b>).</p>
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6495 KiB  
Article
Inertial Sensor Self-Calibration in a Visually-Aided Navigation Approach for a Micro-AUV
by Francisco Bonin-Font, Miquel Massot-Campos, Pep Lluis Negre-Carrasco, Gabriel Oliver-Codina and Joan P. Beltran
Sensors 2015, 15(1), 1825-1860; https://doi.org/10.3390/s150101825 - 16 Jan 2015
Cited by 24 | Viewed by 8504
Abstract
This paper presents a new solution for underwater observation, image recording, mapping and 3D reconstruction in shallow waters. The platform, designed as a research and testing tool, is based on a small underwater robot equipped with a MEMS-based IMU, two stereo cameras and [...] Read more.
This paper presents a new solution for underwater observation, image recording, mapping and 3D reconstruction in shallow waters. The platform, designed as a research and testing tool, is based on a small underwater robot equipped with a MEMS-based IMU, two stereo cameras and a pressure sensor. The data given by the sensors are fused, adjusted and corrected in a multiplicative error state Kalman filter (MESKF), which returns a single vector with the pose and twist of the vehicle and the biases of the inertial sensors (the accelerometer and the gyroscope). The inclusion of these biases in the state vector permits their self-calibration and stabilization, improving the estimates of the robot orientation. Experiments in controlled underwater scenarios and in the sea have demonstrated a satisfactory performance and the capacity of the vehicle to operate in real environments and in real time. Full article
(This article belongs to the Special Issue Inertial Sensors and Systems)
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<p>Fugu-C. (<b>a</b>) The CAD model of the vehicle; (<b>b</b>) the hardware and structure; (<b>c</b>,<b>d</b>) two different views of the robot.</p>
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<p>The schematic of the Fugu-C hardware connection.</p>
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<p>The successive steps of the on-line image processing task.</p>
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<p>Coordinate frames and notations. {G} is the inertial global frame; {L} is the coordinate frame attached to the vehicle. u,v and d represent the position coordinates referenced to {L}.</p>
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<p>The navigation and control architecture.</p>
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<p>(<b>a</b>) The poster covering the pool bottom, with some artificial reliefs on it; (<b>b</b>) Fugu-C navigating in the pool.</p>
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<p>Experiments 1. (<b>a</b>) Robot trajectory in 3D; (<b>b</b>) depth; (<b>c</b>) robot trajectory in the x-y plane; (<b>d</b>) acceleration bias; (<b>e</b>) gyro drift.</p>
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<p>Vehicle attitude corresponding to Experiment 1. (<b>a</b>) Ground truth; (<b>b</b>) visual odometry; (<b>c</b>) multiplicative error state Kalman filter (MESKF).</p>
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<p>Experiment 2. A sweeping trajectory. (<b>a</b>) Robot trajectory in 3D; (<b>b</b>) depth; (<b>c</b>) robot trajectory in the x-y plane; (<b>d</b>) acceleration bias; (<b>e</b>) gyro drift.</p>
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1326 KiB  
Article
A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine
by Han Zou, Xiaoxuan Lu, Hao Jiang and Lihua Xie
Sensors 2015, 15(1), 1804-1824; https://doi.org/10.3390/s150101804 - 15 Jan 2015
Cited by 128 | Viewed by 10043
Abstract
Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide [...] Read more.
Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics. Full article
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
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<p>Methodology of the online sequential extreme learning machine (OS-ELM) approach.</p>
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<p>Positions of the WiFi access points, offline calibration points, online calibration points and online testing points in the simulated field.</p>
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<p>Cumulative percentile of error distance.</p>
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<p>Positions of the WiFi access points, offline calibration points, online calibration points and online testing points in the test-bed.</p>
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<p>The test mobile device Samsung I929 and developed Android apps.</p>
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<p>Localization accuracy regarding different activation functions and different numbers of hidden nodes.</p>
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<p>Deviation of distance error with different numbers of hidden nodes.</p>
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<p>Cumulative percentile of error distance for different methods.</p>
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<p>Comparison of the distance error distribution for different methods. (<b>a</b>) KNN; (<b>b</b>) fuzzy KNN; (<b>c</b>) batch ELM; (<b>d</b>) OS-ELM.</p>
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4271 KiB  
Article
Pose Estimation with a Kinect for Ergonomic Studies: Evaluation of the Accuracy Using a Virtual Mannequin
by Pierre Plantard, Edouard Auvinet, Anne-Sophie Le Pierres and Franck Multon
Sensors 2015, 15(1), 1785-1803; https://doi.org/10.3390/s150101785 - 15 Jan 2015
Cited by 91 | Viewed by 10224
Abstract
Analyzing human poses with a Kinect is a promising method to evaluate potentials risks of musculoskeletal disorders at workstations. In ecological situations, complex 3D poses and constraints imposed by the environment make it difficult to obtain reliable kinematic information. Thus, being able to [...] Read more.
Analyzing human poses with a Kinect is a promising method to evaluate potentials risks of musculoskeletal disorders at workstations. In ecological situations, complex 3D poses and constraints imposed by the environment make it difficult to obtain reliable kinematic information. Thus, being able to predict the potential accuracy of the measurement for such complex 3D poses and sensor placements is challenging in classical experimental setups. To tackle this problem, we propose a new evaluation method based on a virtual mannequin. In this study, we apply this method to the evaluation of joint positions (shoulder, elbow, and wrist), joint angles (shoulder and elbow), and the corresponding RULA (a popular ergonomics assessment grid) upper-limb score for a large set of poses and sensor placements. Thanks to this evaluation method, more than 500,000 configurations have been automatically tested, which would be almost impossible to evaluate with classical protocols. The results show that the kinematic information obtained by the Kinect software is generally accurate enough to fill in ergonomic assessment grids. However inaccuracy strongly increases for some specific poses and sensor positions. Using this evaluation method enabled us to report configurations that could lead to these high inaccuracies. As a supplementary material, we provide a software tool to help designers to evaluate the expected accuracy of this sensor for a set of upper-limb configurations. Results obtained with the virtual mannequin are in accordance with those obtained from a real subject for a limited set of poses and sensor placements. Full article
(This article belongs to the Section Biosensors)
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<p>Overall method pipeline. (<b>a</b>) 3D meshes in specific poses, joint position values of reference (x<sup>ref</sup>, y<sup>ref</sup>, z<sup>ref</sup> in meters), computed joint angle values of reference (α<sup>ref</sup>, β<sup>ref</sup>, γ<sup>ref</sup> in degrees), and computed RULA score (Rula<sup>ref</sup>); (<b>b</b>) Depth images of the mesh in specific sensor positions; (<b>c</b>) Analysis of the depth images with the Kinect software for joint localization [<a href="#b15-sensors-15-01785" class="html-bibr">15</a>]; (<b>d</b>) Estimated joint position values (x′, y′, z′ in meters), computed estimated joint angle values (α′, β′, γ′ in degrees), and computed estimated RULA score (Rula′); (<b>e</b>) Measure error between values of reference and estimated values.</p>
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<p>(<b>a</b>) Parameters of the experimental setup, the left hand reaching volume in blue (azimuth, elevation and elbow flexion) and the positions and orientations of the sensor in dark (azimuth and elevation); (<b>b</b>) The type of grip parameter: Grip from below at the left (0° swivel angle), grip from the side at the middle (90° swivel angle) and grip form above at the right (135° swivel angle).</p>
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<p>Examples of movement performed by the subject with 0° of elevation.</p>
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<p>Experimental setup of the comparison of joint angles estimated with an optoelectronic motion capture system (MBS), the corresponding actual Kinect measurements (RK), and the simulated outputs using a virtual mannequin (VK).</p>
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<p>Accuracy of the Kinect measurement of the 135° swivel angle poses relative to azimuth and elevation pose parameters and with a zero elbow flexion (<b>a</b>) Error distribution of the shoulder (left), elbow (center) and wrist (right) joint positions estimated; (<b>b</b>) Error distribution of the shoulder (left) and elbow (right) joint angles calculated; (<b>c</b>) Error distribution of the resulting upper-body RULA score.</p>
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<p>Accuracy of the Kinect measurement of the shoulder angle (0° swivel angle) according to three parameters: azimuth, elevation, and elbow flexion.</p>
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<p>Example of misestimated poses with a Kinect placed in front of the subject for the shoulder joint (left column) and the elbow joint (right column). Dotted squares show the graphic areas selected. (<b>a</b>) Swivel angle at 0°; (<b>b</b>) Swivel angle at 90°; (<b>c</b>) Swivel angle at 135°.</p>
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<p>Root mean square error (RMSE) of all poses relative to the sensor placement (azimuth and elevation). (<b>a</b>) RMSE distribution of the shoulder (left), elbow (center), and wrist (right) joint positions estimated; (<b>b</b>) RMSE distribution of the shoulder (left) and elbow (right) joint angles calculated; (<b>c</b>) RMSE distribution of the resulting upper-body RULA score.</p>
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1055 KiB  
Review
Research Trends in Wireless Visual Sensor Networks When Exploiting Prioritization
by Daniel G. Costa, Luiz Affonso Guedes, Francisco Vasques and Paulo Portugal
Sensors 2015, 15(1), 1760-1784; https://doi.org/10.3390/s150101760 - 15 Jan 2015
Cited by 33 | Viewed by 6857
Abstract
The development of wireless sensor networks for control and monitoring functions has created a vibrant investigation scenario, where many critical topics, such as communication efficiency and energy consumption, have been investigated in the past few years. However, when sensors are endowed with low-power [...] Read more.
The development of wireless sensor networks for control and monitoring functions has created a vibrant investigation scenario, where many critical topics, such as communication efficiency and energy consumption, have been investigated in the past few years. However, when sensors are endowed with low-power cameras for visual monitoring, a new scope of challenges is raised, demanding new research efforts. In this context, the resource-constrained nature of sensor nodes has demanded the use of prioritization approaches as a practical mechanism to lower the transmission burden of visual data over wireless sensor networks. Many works in recent years have considered local-level prioritization parameters to enhance the overall performance of those networks, but global-level policies can potentially achieve better results in terms of visual monitoring efficiency. In this paper, we make a broad review of some recent works on priority-based optimizations in wireless visual sensor networks. Moreover, we envisage some research trends when exploiting prioritization, potentially fostering the development of promising optimizations for wireless sensor networks composed of visual sensors. Full article
(This article belongs to the Special Issue Visual Sensor Networks)
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<p>Exploiting global-level prioritization in wireless visual sensor networks. The monitoring of cars is more relevant in this example, but other configurations are possible. WVSN, wireless visual sensor networks. (<b>a</b>) A typical WVSN; (<b>b</b>) A WVSN exploiting global-level prioritization.</p>
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<p>A one-level 2D DWT.</p>
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1949 KiB  
Article
Embroidered Electrode with Silver/Titanium Coating for Long-Term ECG Monitoring
by Markus Weder, Dirk Hegemann, Martin Amberg, Markus Hess, Luciano F. Boesel, Roger Abächerli, Veronika R. Meyer and René M. Rossi
Sensors 2015, 15(1), 1750-1759; https://doi.org/10.3390/s150101750 - 15 Jan 2015
Cited by 104 | Viewed by 13474
Abstract
For the long-time monitoring of electrocardiograms, electrodes must be skin-friendly and non-irritating, but in addition they must deliver leads without artifacts even if the skin is dry and the body is moving. Today’s adhesive conducting gel electrodes are not suitable for such applications. [...] Read more.
For the long-time monitoring of electrocardiograms, electrodes must be skin-friendly and non-irritating, but in addition they must deliver leads without artifacts even if the skin is dry and the body is moving. Today’s adhesive conducting gel electrodes are not suitable for such applications. We have developed an embroidered textile electrode from polyethylene terephthalate yarn which is plasma-coated with silver for electrical conductivity and with an ultra-thin titanium layer on top for passivation. Two of these electrodes are embedded into a breast belt. They are moisturized with a very low amount of water vapor from an integrated reservoir. The combination of silver, titanium and water vapor results in an excellent electrode chemistry. With this belt the long-time monitoring of electrocardiography (ECG) is possible at rest as well as when the patient is moving. Full article
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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<p>Detail of the embroidered electrode made from Ag/Ti-coated PET yarn.</p>
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<p>(<b>a</b>) Sketch of the wetting device; (<b>b</b>) Prototype of the wetting pad (<b>above</b>) and the ECG belt with embroidered electrodes (<b>below</b>).</p>
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<p>Positioning of the electrodes on the human thorax during our tests. (<b>a</b>) Moist textile electrodes placed horizontally within the belt (red #1 and #2); gel electrodes diagonally over the heart, <span class="html-italic">i.e.</span> the classical position for best signals (green #3 and #4); ground centered on the waist; (<b>b</b>) Gel electrodes placed as close to the belt as possible; (<b>c</b>) Signals obtained with arrangement (b); The test person was at rest with a pulse of 70.</p>
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<p>Electrode signals. <b>Red</b>: embroidered moist electrodes made from Ag-coated yarn worn with a chest belt; <b>Green</b>: classical Ag/AgCl gel electrodes. The test person wore the electrodes as shown in <a href="#f3-sensors-15-01750" class="html-fig">Figure 3a</a> and was in light motion with a mean pulse of 60. For details see also the legend of <a href="#f5-sensors-15-01750" class="html-fig">Figure 5</a>.</p>
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<p>Example of “not suitable” signals (<b>red curve</b>) as obtained by embroidered Ag/Ti electrodes under dry conditions and in motion. The test person wore the electrodes as shown in <a href="#f3-sensors-15-01750" class="html-fig">Figure 3a</a> and its mean pulse was 86.</p>
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<p>Electrode signals. <b>Red</b>: 2 embroidered moist electrodes with Ag/Ti coating worn with the belt; <b>Green</b>: 2 classical Ag/AgCl gel electrodes placed diagonally over the heart (Ambu Blue Sensor by Synmedic AG, Zurich, Switzerland). For details see <a href="#f3-sensors-15-01750" class="html-fig">Figure 3</a>. (<b>a</b>) At rest (pulse 63); (<b>b</b>) In motion (pulse 146).</p>
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1513 KiB  
Article
An Image Stabilization Optical System Using Deformable Freeform Mirrors
by Qun Hao, Xuemin Cheng, Jiqiang Kang and Yuhua Jiang
Sensors 2015, 15(1), 1736-1749; https://doi.org/10.3390/s150101736 - 15 Jan 2015
Cited by 16 | Viewed by 6978
Abstract
An image stabilization optical system using deformable freeform mirrors is proposed that enables the ray sets to couple dynamically in the object and image space. It aims to correct image blurring and degradation when there is relative movement between the imaging optical axis [...] Read more.
An image stabilization optical system using deformable freeform mirrors is proposed that enables the ray sets to couple dynamically in the object and image space. It aims to correct image blurring and degradation when there is relative movement between the imaging optical axis and the object. In this method, Fermat’s principle and matrix methods are used to describe the optical path of the entire optical system with a shift object plane and a fixed corresponding image plane in the carrier coordinate system. A constant optical path length is determined for each ray set, so the correspondence between the object and the shift free image point is used to calculate the solution to the points on the surface profile of the deformable mirrors (DMs). Off-axis three-mirror anastigmats are used to demonstrate the benefits of optical image stabilization with one- and two-deformable mirrors. Full article
(This article belongs to the Section Physical Sensors)
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<p>(<b>a</b>) Illustrates the ray tracing paths for image shift compensation using DMs. Double wavy lines in the optical system indicate optical elements on either side of the DMs. Green solid lines show the segments on the original profiles of DM surfaces <span class="html-italic">S</span><sub><span class="html-italic">D</span><sub>1</sub></sub> and <span class="html-italic">S</span><sub><span class="html-italic">D</span><sub>2</sub></sub> placed between the object plane and the image plane. Red dotted lines show the segments on the changed profiles of DM surfaces <span class="html-italic">S′</span><sub><span class="html-italic">D</span><sub>1</sub></sub> and <span class="html-italic">S′</span><sub><span class="html-italic">D</span><sub>2</sub></sub> for image shift compensation. Green solid lines trace rays from point <span class="html-italic">O</span>, the initial location, through the optical system towards point <span class="html-italic">O<sub>S<sub>D</sub>I</sub></span>. Points <span class="html-italic">O</span><sub><span class="html-italic">S</span><sub><span class="html-italic">D</span>1</sub></sub> and <span class="html-italic">O</span><sub><span class="html-italic">S</span><sub><span class="html-italic">D</span>2</sub></sub> are the intersections of the incident rays and the reflected rays on the DMs. The blue dashed lines from points <span class="html-italic">P</span><sub><span class="html-italic">S</span><sub><span class="html-italic">D</span>1</sub></sub>, <span class="html-italic">P</span><sub><span class="html-italic">S</span><sub><span class="html-italic">D</span>2</sub></sub> and <span class="html-italic">P<sub>S<sub>D</sub>I</sub></span> represent ray tracing from the shift point location <span class="html-italic"><span class="html-small-caps">p</span></span> in the initial optical path and locate a shift point <span class="html-italic">P<sub>S<sub>D</sub>I</sub></span> on the image plane. Red solid lines are the rays from point <span class="html-italic">P</span>, the shift point location on the object plane, tracing the compensated optical path towards point <span class="html-italic">P<sub>S′<sub>D</sub>I</sub></span> using the changed profiles of DM surfaces; (<b>b</b>) Illustrates the relationship between the vectors of the reflected rays and the normal line on one point of the mirror surface in space.</p>
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<p>OIS DM segment design through the use of ray tracing in 3D space.</p>
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<p>Schematic illustration of surface profile calculation for DMs.</p>
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<p>(<b>a</b>) Ray tracing paths overlay the configurations for the initial system and the one DM OIS system with the calculated surface profile; and (<b>b</b>) an enlarged drawing showing the initial DM surface profile in the system as dark blue segments, and the calculated DM surface profile for OIS as the light blue segments, indicating the initial incident and the OIS incident ray paths.</p>
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<p>Deflections of the surface at the DM center point for the variations along seven directions, with different colors for the corresponding DMs.</p>
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<p>The overlayed drawing of the configurations for the initial ray paths using surface <span class="html-italic">S<sub>D</sub></span> and the OIS ray paths using surface <span class="html-italic">S</span>′<span class="html-italic"><sub>D</sub></span>.</p>
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<p>Maximum deflections of the DM surface as the field of view is varied along five directions.</p>
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<p>(<b>a</b>) the vibrating images; (<b>b</b>) those compensated by the optical stabilization method, using two DMs, the car in the image sequences was marked in red line.</p>
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<p>Peak Signal to Noise Ratios (PSNR) for two experiments: Those for the original image and the proposed method, using two DMs.</p>
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2110 KiB  
Article
Trajectory-Based Visual Localization in Underwater Surveying Missions
by Antoni Burguera, Francisco Bonin-Font and Gabriel Oliver
Sensors 2015, 15(1), 1708-1735; https://doi.org/10.3390/s150101708 - 14 Jan 2015
Cited by 28 | Viewed by 7507
Abstract
We present a new vision-based localization system applied to an autonomous underwater vehicle (AUV) with limited sensing and computation capabilities. The traditional EKF-SLAM approaches are usually expensive in terms of execution time; the approach presented in this paper strengthens this method by adopting [...] Read more.
We present a new vision-based localization system applied to an autonomous underwater vehicle (AUV) with limited sensing and computation capabilities. The traditional EKF-SLAM approaches are usually expensive in terms of execution time; the approach presented in this paper strengthens this method by adopting a trajectory-based schema that reduces the computational requirements. The pose of the vehicle is estimated using an extended Kalman filter (EKF), which predicts the vehicle motion by means of a visual odometer and corrects these predictions using the data associations (loop closures) between the current frame and the previous ones. One of the most important steps in this procedure is the image registration method, as it reinforces the data association and, thus, makes it possible to close loops reliably. Since the use of standard EKFs entail linearization errors that can distort the vehicle pose estimations, the approach has also been tested using an iterated Kalman filter (IEKF). Experiments have been conducted using a real underwater vehicle in controlled scenarios and in shallow sea waters, showing an excellent performance with very small errors, both in the vehicle pose and in the overall trajectory estimates. Full article
(This article belongs to the Section Physical Sensors)
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<p>Summary of the proposed image registration process.</p>
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<p>Image processing prior to the image registration. (<b>a</b>,<b>c</b>) Original images; (<b>b</b>,<b>d</b>) filtered images.</p>
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<p>Feature matching using underwater images. Yellow lines represent correspondences between features. (<b>a</b>) Overlapping images; (<b>b</b>) non-overlapping images.</p>
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<p>RANSAC underwater image registration using 2D features.</p>
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<p>Simple camera model to determine whether two images overlap or not. Given two images gathered at times <span class="html-italic">t<sub>i</sub></span> and <span class="html-italic">t<sub>j</sub></span> and heights <span class="html-italic">A<sub>i</sub></span> and <span class="html-italic">A<sub>j</sub></span> using a camera with an angle of vision of <span class="html-italic">α</span> degrees, the observed regions have a diameter of <span class="html-italic">w<sub>i</sub></span> and <span class="html-italic">w<sub>j</sub></span>, respectively. The term <span class="html-italic">d</span> denotes the distance between the image acquisition points.</p>
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<p>Illustration of a measurement (thick red arrow) and the corresponding observation function (dashed blue arrows)</p>
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<p>Errors in meters and 2<span class="html-italic">σ</span> bound. Noise levels represent the covariance of the synthetic zero-mean Gaussian noise added to odometry, ranging from Noise Level 1 ([Σ<span class="html-italic"><sub>x</sub></span>, Σ<span class="html-italic"><sub>y</sub></span>, Σ<span class="html-italic"><sub>θ</sub></span>] = [0,0,0]) to Noise Level 5 ([Σ<span class="html-italic"><sub>x</sub></span>, Σ<span class="html-italic"><sub>y</sub></span>, Σ<span class="html-italic"><sub>θ</sub></span>] = [4 × 10<sup>−5</sup>, 4 × 10<sup>−5</sup>, 5 × 10<sup>−4</sup>]). (<b>a</b>) Using a keyframe separation of five; (<b>b</b>) using a keyframe separation of 10.</p>
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<p>Example of the results obtained with Noise Level 2 and keyframe separation 10. GT and DR denote ground truth and dead reckoning. (<b>a</b>) Trajectories; (<b>b</b>) registered images.</p>
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<p>The Fugu-C.</p>
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5419 KiB  
Article
An X-Band Radar System for Bathymetry and Wave Field Analysis in a Harbour Area
by Giovanni Ludeno, Ferdinando Reale, Fabio Dentale, Eugenio Pugliese Carratelli, Antonio Natale, Francesco Soldovieri and Francesco Serafino
Sensors 2015, 15(1), 1691-1707; https://doi.org/10.3390/s150101691 - 14 Jan 2015
Cited by 39 | Viewed by 7545
Abstract
Marine X-band radar based systems are well tested to provide information about sea state and bathymetry. It is also well known that complex geometries and non-uniform bathymetries provide a much bigger challenge than offshore scenarios. In order to tackle this issue a retrieval [...] Read more.
Marine X-band radar based systems are well tested to provide information about sea state and bathymetry. It is also well known that complex geometries and non-uniform bathymetries provide a much bigger challenge than offshore scenarios. In order to tackle this issue a retrieval method is proposed, based on spatial partitioning of the data and the application of the Normalized Scalar Product (NSP), which is an innovative procedure for the joint estimation of bathymetry and surface currents. The strategy is then applied to radar data acquired around a harbour entrance, and results show that the reconstructed bathymetry compares well with ground truth data obtained by an echo-sounder campaign, thus proving the reliability of the whole procedure. The spectrum thus retrieved is then analysed to show the evidence of reflected waves from the harbour jetties, as confirmed by chain of hydrodynamic models of the sea wave field. The possibility of using a land based radar to reveal sea wave reflection is entirely new and may open up new operational applications of the system. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Italy 2014)
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<p>Block diagram of the reconstruction procedure.</p>
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<p>A view of Salerno Harbour and its two main jetties. The red arrow denotes the position of the acquisition system installed on the Caronte &amp; Tourist ferry ship.</p>
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<p>A sample image of the raw radar data set.</p>
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<p>Reconstructed bathymetry map. The red circle denotes the radar location.</p>
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<p>Standard deviation of radar bathymetry. The red circle denotes the radar location.</p>
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<p>Echo-sounder bathymetry map. The red circle denotes the radar location.</p>
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<p>Map of differences between the echo sounder survey and the radar bathymetry.</p>
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<p>(<b>Left</b>) Radar depth <span class="html-italic">vs.</span> echo-sounder measurements. The red line denotes the regression line, whose equation is given in the left top side; (<b>Right</b>) Histogram of differences between the radar depth and echo-sounder measurements. The mean value (μ) and the standard deviation (σ) of the differences are given on the left top side.</p>
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<p>Tide level as measured by Italian Environmental Agency tide gauge during the test.</p>
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964 KiB  
Article
Optimal Base Station Placement for Wireless Sensor Networks with Successive Interference Cancellation
by Lei Shi, Jianjun Zhang, Yi Shi, Xu Ding and Zhenchun Wei
Sensors 2015, 15(1), 1676-1690; https://doi.org/10.3390/s150101676 - 14 Jan 2015
Cited by 13 | Viewed by 5970
Abstract
We consider the base station placement problem for wireless sensor networks with successive interference cancellation (SIC) to improve throughput. We build a mathematical model for SIC. Although this model cannot be solved directly, it enables us to identify a necessary condition for SIC [...] Read more.
We consider the base station placement problem for wireless sensor networks with successive interference cancellation (SIC) to improve throughput. We build a mathematical model for SIC. Although this model cannot be solved directly, it enables us to identify a necessary condition for SIC on distances from sensor nodes to the base station. Based on this relationship, we propose to divide the feasible region of the base station into small pieces and choose a point within each piece for base station placement. The point with the largest throughput is identified as the solution. The complexity of this algorithm is polynomial. Simulation results show that this algorithm can achieve about 25% improvement compared with the case that the base station is placed at the center of the network coverage area when using SIC. Full article
(This article belongs to the Section Sensor Networks)
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<p>Suitable and unsuitable positions for the base station.</p>
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<p>An example for the feasible region.</p>
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<p>The pseudocode for finding the feasible region for <span class="html-italic">B</span>.</p>
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<p>An example for finding a feasible region of <span class="html-italic">B</span>.</p>
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<p>Three situations when dividing the region.</p>
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<p>A example of shrinking.</p>
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<p>A 20-node network.</p>
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<p>Feasible region for the 20-node network.</p>
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<p>Dividing the feasible region into pieces for the 20-node network.</p>
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2539 KiB  
Review
Design of Surface Modifications for Nanoscale Sensor Applications
by Erik Reimhult and Fredrik Höök
Sensors 2015, 15(1), 1635-1675; https://doi.org/10.3390/s150101635 - 14 Jan 2015
Cited by 75 | Viewed by 12167
Abstract
Nanoscale biosensors provide the possibility to miniaturize optic, acoustic and electric sensors to the dimensions of biomolecules. This enables approaching single-molecule detection and new sensing modalities that probe molecular conformation. Nanoscale sensors are predominantly surface-based and label-free to exploit inherent advantages of physical [...] Read more.
Nanoscale biosensors provide the possibility to miniaturize optic, acoustic and electric sensors to the dimensions of biomolecules. This enables approaching single-molecule detection and new sensing modalities that probe molecular conformation. Nanoscale sensors are predominantly surface-based and label-free to exploit inherent advantages of physical phenomena allowing high sensitivity without distortive labeling. There are three main criteria to be optimized in the design of surface-based and label-free biosensors: (i) the biomolecules of interest must bind with high affinity and selectively to the sensitive area; (ii) the biomolecules must be efficiently transported from the bulk solution to the sensor; and (iii) the transducer concept must be sufficiently sensitive to detect low coverage of captured biomolecules within reasonable time scales. The majority of literature on nanoscale biosensors deals with the third criterion while implicitly assuming that solutions developed for macroscale biosensors to the first two, equally important, criteria are applicable also to nanoscale sensors. We focus on providing an introduction to and perspectives on the advanced concepts for surface functionalization of biosensors with nanosized sensor elements that have been developed over the past decades (criterion (iii)). We review in detail how patterning of molecular films designed to control interactions of biomolecules with nanoscale biosensor surfaces creates new possibilities as well as new challenges. Full article
(This article belongs to the Section Biosensors)
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<p>Schematic illustration of (<b>left</b>) small-scale surface-stress sensitive cantilevers, (<b>middle</b>) semiconductor nanowires and (<b>right</b>) nanoplasmonically active gold discs designed with immobilized probe molecules for selective detection of suspended analyte molecules. The different colors represent capture agents for different molecules immobilized on the respective sensors.</p>
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<p>Example of the higher sensitivity at the edges of nanoplasmonic sensor elements. A 100-nm gold cone embedded in a nanocavity display the highest enhancement and measurement sensitivity at the base and the apex. The geometry was used to selectively measure molecules interacting only at the tip or at the base of the sensor element [<a href="#b38-sensors-15-01635" class="html-bibr">38</a>]. (<b>A</b>) Shows the sensor response at the apex and (<b>B</b>) shows the sensor response at the base. Simulations (right) show the highly localized electric field enhancement that explains the localized sensitivity.</p>
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<p>Schematic illustration of the relatively similar size of sensor element, surface coating, ligand and the sensitive region of a nanoscale sensor. A nanoplasmonic particle is used as example. A typical surface coating functionalizing the sensor might consume the greater part of the highest sensitivity region of the sensor element. A random distribution of functional groups might not correspond to the localized sensitivity of the sensor element, here, e.g., at high aspect ratio parts of the structure.</p>
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<p>Schematic illustration of the different molecular components required to create a biosensor surface. The surface anchor ensures the binding to the underlying substrate material, the spacer's role is to screen all interactions of the target with the substrate, and the optional functional unit (recognition element) is used to selectively capture the target. (<b>Left</b>): A surface modification scheme on a single substrate material, representative for macro-scale biosensors; (<b>Right</b>): A biosensor surface consisting of two different substrate materials, as often encountered in small (nano) scale sensors. Two different anchors, each specific to one of the substrate materials, are required to modify the substrate.</p>
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<p>Schematic illustration of the original SMAP method [<a href="#b79-sensors-15-01635" class="html-bibr">79</a>]. Material contrast created in oxides by lithographic techniques is converted, in a series of dip-and-rinse processes performed in aqueous solutions, into a contrast with respect to protein adsorption: (<b>a</b>) a polished silicon or transparent glass wafer is coated first with a 50 nm titanium oxide intermediate layer and then with a 12 nm thin silicon oxide top layer; (<b>b</b>) The desired patterns are created in the metal oxide layer by a combination of lithographic and etching processes; (<b>c</b>) Adsorption of dodecyl phosphate (DDP) from aqueous solution leads to the formation of an oriented self-assembled monolayer on TiO<sub>2</sub>, making it hydrophobic. There is no interaction between DDP and the SiO<sub>2</sub> surface, which is left completely bare; (<b>d</b>) After rinsing with water, PLL-<span class="html-italic">g</span>-PEG adsorbs from a buffered solution to the bare SiO<sub>2</sub>, and to a lesser extent also to the DDP (not shown). After rinsing, the PLL-<span class="html-italic">g</span>-PEG-coated SiO<sub>2</sub> regions repel proteins completely, while the PLL-<span class="html-italic">g</span>-PEG adsorbed on the DDP is weakly bound and is exchanged with the adsorbing protein(s); (<b>e</b>) The chemical contrast between hydrophobic and protein-resistant areas can then be converted into an adhesive/biofunctional contrast by simply exposing the surface to proteins.</p>
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<p>Schematic of materials selective surface functionalization at the interface between two materials. A nanoscale region where the materials contrast is not ideally defined can be created by interdiffusion of the materials or by an additional bridging material (e.g., adhesion layer). The surface functionalization is then likely affected in any of several ways: for multivalent weak anchors (<b>a</b>) loop formation producing charged or hydrophobic defects can occur; (<b>b</b>) intermixing and a diffuse biochemical contrast can be created; for chemisorbing single anchors; (<b>c</b>) voids due to insufficient binding can open the substrate to nonspecific binding (can also occur for multivalent weak anchors); (<b>d</b>) intermixing and a diffuse biochemical contrast can be created.</p>
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<p>A “master curve” compiled for the efficiency of different anchoring strategies and PEG spacers with respect to preventing serum protein adsorption. A mono-, di-, or trivalent DOPA or a PLL-chain multivalent anchor was used. These combined results from several studies for short linear and dendron PEG show that serum protein adsorption is prevented above a cut-off projected surface density of ∼15 EG units (<span class="html-italic">N<sub>EG</sub></span>) per nm<sup>2</sup>, regardless of architecture. (<b>a</b>) Comparison of dendron (D(valency of anchor)) and linear PEG (L(valency of anchor) with a molecular weight of the PEG part of M<sub>w</sub> = 2.5 kDa): D(<span class="html-italic">n</span> = 1), green diamond, D(<span class="html-italic">n</span> = 2), red triangle, D(<span class="html-italic">n</span> = 3), blue circle, and L(<span class="html-italic">n</span> = 3), black square; (<b>b</b>) Comparison of PEG-5 kDa-DOPA (D(<span class="html-italic">n</span> = 1–3)) (on TiO2) and PLL-<span class="html-italic">g</span>-PEG (on Nb2O5) [<a href="#b93-sensors-15-01635" class="html-bibr">93</a>].</p>
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<p>Schematic of “grafting to” and “grafting from” as standard techniques to create a sterically repulsive polymer coating at a sensor interface. (<b>a</b>) In grafting to a pre-synthesized anchor and spacer molecule is linked to the interface and the spacing between anchor groups (<span class="html-italic">s</span>) will at best on average be close to the radius of gyration of the polymer (<span class="html-italic">R<sub>G</sub></span>). The polymer will be in mushroom configuration; (<b>b</b>) Grafting to can be performed in a poor solvent which reduces <span class="html-italic">R<sub>G</sub></span> and allows for closer packing. Changing to a good solvent a polymer brush is created by swelling; (<b>c</b>) In grafting from an initiator is linked by an anchor to the surface. The polymer spacer is created by living polymerization from monomers in solution to create a dense brush.</p>
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<p>Schematic of the MAPL process, showing how it can be used to convert a photoresist patterned oxide substrate into a surface with well-controlled biointeractive patches in a non-interactive background [<a href="#b122-sensors-15-01635" class="html-bibr">122</a>]. The sample composed of a photoresist pattern (stage <b>I</b>) is dipped into a solution of functionalized (e.g., biotin or RGD) PLL-<span class="html-italic">g</span>-PEG (stage <b>II</b>). The functionalized PLL-<span class="html-italic">g</span>-PEG adsorbs strongly to the bare oxide surface. Next, the PR is removed with an organic solvent without altering or desorbing the polymer immobilized on the oxide, but lifts off polymer on top of the resist (stage <b>III</b>). Finally, the sample is incubated in a solution of nonfunctionalized PLL-<span class="html-italic">g</span>-PEG to render the background resistant to the adsorption of biomolecules (stage <b>IV</b>).</p>
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2066 KiB  
Article
Ethanol Microsensors with a Readout Circuit Manufactured Using the CMOS-MEMS Technique
by Ming-Zhi Yang and Ching-Liang Dai
Sensors 2015, 15(1), 1623-1634; https://doi.org/10.3390/s150101623 - 14 Jan 2015
Cited by 19 | Viewed by 5995
Abstract
The design and fabrication of an ethanol microsensor integrated with a readout circuit on-a-chip using the complementary metal oxide semiconductor (CMOS)-microelectro -mechanical system (MEMS) technique are investigated. The ethanol sensor is made up of a heater, a sensitive film and interdigitated electrodes. The [...] Read more.
The design and fabrication of an ethanol microsensor integrated with a readout circuit on-a-chip using the complementary metal oxide semiconductor (CMOS)-microelectro -mechanical system (MEMS) technique are investigated. The ethanol sensor is made up of a heater, a sensitive film and interdigitated electrodes. The sensitive film is tin dioxide that is prepared by the sol-gel method. The heater is located under the interdigitated electrodes, and the sensitive film is coated on the interdigitated electrodes. The sensitive film needs a working temperature of 220 °C. The heater is employed to provide the working temperature of sensitive film. The sensor generates a change in capacitance when the sensitive film senses ethanol gas. A readout circuit is used to convert the capacitance variation of the sensor into the output frequency. Experiments show that the sensitivity of the ethanol sensor is 0.9 MHz/ppm. Full article
(This article belongs to the Section Physical Sensors)
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<p>Structure of the integrated ethanol sensor.</p>
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<p>Readout circuit for the ethanol sensor.</p>
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<p>Simulated results of the output frequency for the circuit.</p>
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<p>SEM image of the tin dioxide film.</p>
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<p>Elements of the tin dioxide film measured by EDS.</p>
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<p>Fabrication process of the ethanol sensor: (<b>a</b>) after the CMOS process; (<b>b</b>) etching the sacrificial layer; (<b>c</b>) coating the tin dioxide film.</p>
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<p>SEM image of the interdigitated electrodes after the wet etching.</p>
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<p>Optical image of the ethanol sensor after the post-process.</p>
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<p>Response of the ethanol sensor at 3 ppm ethanol.</p>
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