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Sensors, Volume 18, Issue 8 (August 2018)

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Open AccessArticle Highly Sensitive and Selective Colorimetric Detection of Methylmercury Based on DNA Functionalized Gold Nanoparticles
Sensors 2018, 18(8), 2679; https://doi.org/10.3390/s18082679 (registering DOI)
Received: 11 June 2018 / Revised: 26 July 2018 / Accepted: 30 July 2018 / Published: 15 August 2018
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Abstract
A new colorimetric detection of methylmercury (CH3Hg+) was developed, which was based on the surface deposition of Hg enhancing the catalytic activity of gold nanoparticles (AuNPs). The AuNPs were functionalized with a specific DNA strand (HT7) recognizing
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A new colorimetric detection of methylmercury (CH3Hg+) was developed, which was based on the surface deposition of Hg enhancing the catalytic activity of gold nanoparticles (AuNPs). The AuNPs were functionalized with a specific DNA strand (HT7) recognizing CH3Hg+, which was used to capture and separate CH3Hg+ by centrifugation. It was found that the CH3Hg+ reduction resulted in the deposition of Hg onto the surface of AuNPs. As a result, the catalytic activity of the AuNPs toward the chromogenic reaction of 3,3,5,5-tetramethylbenzidine (TMB)-H2O2 was remarkably enhanced. Under optimal conditions, a limit of detection of 5.0 nM was obtained for CH3Hg+ with a linear range of 10–200 nM. We demonstrated that the colorimetric method was fairly simple with a low cost and can be conveniently applied to CH3Hg+ detection in environmental samples. Full article
(This article belongs to the Special Issue Colorimetric and Fluorescent Sensors 2018)
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Open AccessArticle A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data
Sensors 2018, 18(8), 2678; https://doi.org/10.3390/s18082678 (registering DOI)
Received: 10 July 2018 / Revised: 12 August 2018 / Accepted: 13 August 2018 / Published: 15 August 2018
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Abstract
Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper
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Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper presents a feature-based method for 6D pose estimation of rigid objects based on the Point Pair Features voting approach. The presented solution combines a novel preprocessing step, which takes into consideration the discriminative value of surface information, with an improved matching method for Point Pair Features. In addition, an improved clustering step and a novel view-dependent re-scoring process are proposed alongside two scene consistency verification steps. The proposed method performance is evaluated against 15 state-of-the-art solutions on a set of extensive and variate publicly available datasets with real-world scenarios under clutter and occlusion. The presented results show that the proposed method outperforms all tested state-of-the-art methods for all datasets with an overall 6.6% relative improvement compared to the second best method. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Model and Simulation Studies of the Method for Optimization of Dynamic Properties of Tachometric Anemometers
Sensors 2018, 18(8), 2677; https://doi.org/10.3390/s18082677 (registering DOI)
Received: 16 July 2018 / Revised: 8 August 2018 / Accepted: 13 August 2018 / Published: 14 August 2018
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Abstract
Mechanical tachometric anemometers, based on the phenomenon of the exchange of momentum between the flow and rotating measuring element, represent an important class of instruments used in flow metrology. In particular, they are used in meteorological and ventilation measurements. Mechanical anemometers with rotating
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Mechanical tachometric anemometers, based on the phenomenon of the exchange of momentum between the flow and rotating measuring element, represent an important class of instruments used in flow metrology. In particular, they are used in meteorological and ventilation measurements. Mechanical anemometers with rotating measuring element are, however, known for their drawback related to their poor dynamic properties resulting from relatively large dimensions and mechanical inertia of the measuring element. In these instruments, the phenomenon of overestimating the measured average velocity caused by the inertia of the rotor takes place. Optimization of the dynamics of the measurement process, as well as the estimation and minimization of the measurement uncertainty, can be performed based on mathematical model of anemometer. In this study, a new, original concept of optimization of dynamic properties of tachometric anemometers is proposed, and the results of model and simulation studies are presented. The new concept of measuring instrument is based on the use of feedback and active control of the rotor. The new method was tested using model research, where two types of flow velocity excitations were applied: sinusoidal and rectangular. The tests carried out showed that the developed method allows for minimization of the dynamic uncertainty of the measurement and minimizes the phenomenon of average flow velocity overestimation occurring in time-varying flows. It has been shown that the use of optimization system allows for approximately tenfold reduction of the error of average velocity measurement in the case of pulsating flows. In addition, the optimization systems allow for anemometer’s transmission bandwidth to be extended about a hundred times. This creates new application possibilities for these instruments and allows for a large reduction of measurement uncertainty. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Ceramic Diffusion Bonding Method for Passive LC High-Temperature Pressure Sensor
Sensors 2018, 18(8), 2676; https://doi.org/10.3390/s18082676 (registering DOI)
Received: 17 July 2018 / Revised: 9 August 2018 / Accepted: 13 August 2018 / Published: 14 August 2018
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Abstract
Alumina ceramic is a highly promising material for fabricating high-temperature pressure sensors. In this paper, a direct bonding method for fabricating a sensitive cavity with alumina ceramic is presented. Alumina ceramic substrates were bonded together to form a sensitive cavity for high-temperature pressure
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Alumina ceramic is a highly promising material for fabricating high-temperature pressure sensors. In this paper, a direct bonding method for fabricating a sensitive cavity with alumina ceramic is presented. Alumina ceramic substrates were bonded together to form a sensitive cavity for high-temperature pressure environments. The device can sense pressure parameters at high temperatures. To verify the sensitivity performance of the fabrication method in high-temperature environments, an inductor and capacitor were integrated on the ceramic substrate with the fabricated sensitive cavity to form a wireless passive LC pressure sensor with thick-film integrated technology. Finally, the fabricated sensor was tested using a system test platform. The experimental results show that the sensor can realize pressure measurements above 900 °C, confirming that the fabricated sensitive cavity has excellent sealing properties. Therefore, the direct bonding method can potentially be used for developing all-ceramic high-temperature pressure sensors for application in harsh environments. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields
Sensors 2018, 18(8), 2675; https://doi.org/10.3390/s18082675 (registering DOI)
Received: 1 August 2018 / Revised: 11 August 2018 / Accepted: 13 August 2018 / Published: 14 August 2018
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Abstract
Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm
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Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient ( σ s o i l ° ). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient ( σ s o i l ° ) and the AIEM-simulated backscattering coefficient ( σ soil-simu ° ). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessReview Machine Learning in Agriculture: A Review
Sensors 2018, 18(8), 2674; https://doi.org/10.3390/s18082674 (registering DOI)
Received: 27 June 2018 / Revised: 31 July 2018 / Accepted: 7 August 2018 / Published: 14 August 2018
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Abstract
Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production
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Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action. Full article
(This article belongs to the Special Issue Sensors in Agriculture 2018)
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Open AccessArticle One-Step Laser Encapsulation of Nano-Cracking Strain Sensors
Sensors 2018, 18(8), 2673; https://doi.org/10.3390/s18082673 (registering DOI)
Received: 29 June 2018 / Revised: 4 August 2018 / Accepted: 8 August 2018 / Published: 14 August 2018
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Abstract
Development of flexible strain sensors that can be attached directly onto the skin, such as skin-mountable or wearable electronic devices, has recently attracted attention. However, such flexible sensors are generally exposed to various harsh environments, such as sweat, humidity, or dust, which cause
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Development of flexible strain sensors that can be attached directly onto the skin, such as skin-mountable or wearable electronic devices, has recently attracted attention. However, such flexible sensors are generally exposed to various harsh environments, such as sweat, humidity, or dust, which cause noise and shorten the sensor lifetimes. This study reports the development of a nano-crack-based flexible sensor with mechanically, thermally, and chemically stable electrical characteristics in external environments using a novel one-step laser encapsulation (OLE) method optimized for thin films. The OLE process allows simultaneous patterning, cutting, and encapsulating of a device using laser cutting and thermoplastic polymers. The processes are simplified for economical and rapid production (one sensor in 8 s). Unlike other encapsulation methods, OLE does not degrade the performance of the sensor because the sensing layers remain unaffected. Sensors protected with OLE exhibit mechanical, thermal, and chemical stability under water-, heat-, dust-, and detergent-exposed conditions. Finally, a waterproof, flexible strain sensor is developed to detect motions around the eye, where oil and sweat are generated. OLE-based sensors can be used in several applications that are exposed to a large amount of foreign matter, such as humid or sweaty environments. Full article
(This article belongs to the Special Issue Smart Sensing System for Real-Time Monitoring)
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Open AccessArticle A Forward Collision Warning System for Smartphones Using Image Processing and V2V Communication
Sensors 2018, 18(8), 2672; https://doi.org/10.3390/s18082672 (registering DOI)
Received: 9 July 2018 / Revised: 3 August 2018 / Accepted: 3 August 2018 / Published: 14 August 2018
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Abstract
In this paper, we present a forward collision warning application for smartphones that uses license plate recognition and vehicular communication to generate warnings for notifying the drivers of the vehicle behind and the one ahead, of a probable collision when the vehicle behind
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In this paper, we present a forward collision warning application for smartphones that uses license plate recognition and vehicular communication to generate warnings for notifying the drivers of the vehicle behind and the one ahead, of a probable collision when the vehicle behind does not maintain an established safe distance between itself and the vehicle ahead. The application was tested in both static and mobile scenarios, from which we confirmed the working of our application, even though its performance is affected by the hardware limitations of the smartphones. Full article
(This article belongs to the Special Issue Advances on Vehicular Networks: From Sensing to Autonomous Driving)
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Open AccessArticle Centimeter-Level Orbit Determination for TG02 Spacelab Using Onboard GNSS Data
Sensors 2018, 18(8), 2671; https://doi.org/10.3390/s18082671 (registering DOI)
Received: 3 July 2018 / Revised: 31 July 2018 / Accepted: 3 August 2018 / Published: 14 August 2018
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Abstract
Tiangong-2, the second Chinese manned spacecraft, was launched into low Earth orbit on 15 September 2016. The dual-frequency geodetic GNSS receiver equipped on it is supporting a number of scientific experiments in orbit. This paper uses the onboard GNSS data from 3–31 December
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Tiangong-2, the second Chinese manned spacecraft, was launched into low Earth orbit on 15 September 2016. The dual-frequency geodetic GNSS receiver equipped on it is supporting a number of scientific experiments in orbit. This paper uses the onboard GNSS data from 3–31 December 2016 (in the attitude mode of three-axis Earth-pointing stabilization) to analyze the data quantity, as well as the code multipath error. Then, the dynamic and reduced-dynamic methods are adopted to perform the post Precise Orbit Determination (POD) based on the carrier phase measurements, respectively. After that, the orbit accuracy is evaluated using a number of tests, which include the analysis of observation residuals, Overlapping Orbit Differences (OODs), orbit comparison between dynamic and reduced-dynamic and Satellite Laser Ranging (SLR) validation. The results show that: (1) the average Root Mean Square (RMS) of the on-board GNSS phase fitting residuals is 8.8 mm; (2) regarding the OODs determined by the reduced-dynamic method, the average RMS in radial (R), along-track (T) and cross-track (N) directions is 0.43 cm, 1.34 cm and 0.39 cm, respectively, and there are no obvious system errors; (3) the orbit accuracy of TG02 determined by the reduced-dynamic method is comparable to that of the dynamic method, and the average RMS of their differences in R, T, N and 3D directions is 3.05 cm, 3.60 cm, 2.52 cm and 5.40 cm, respectively; (4) SLR data are used to validate the reduced-dynamic orbits, and the average RMS along the station-satellite direction is 1.94 cm. It can be seen that both of these two methods can meet the demands of 3D centimeter-level orbit determination for TG02. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Tracking Personal Health-Environment Interaction with Novel Mobile Sensing Devices
Sensors 2018, 18(8), 2670; https://doi.org/10.3390/s18082670 (registering DOI)
Received: 26 June 2018 / Revised: 8 August 2018 / Accepted: 9 August 2018 / Published: 14 August 2018
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Abstract
The development of connected health devices has allowed for a more accurate assessment of a person’s state under free-living conditions. In this work, we use two mobile sensing devices and investigate the correlation between individual’s resting metabolic rate (RMR) and volatile organic compounds
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The development of connected health devices has allowed for a more accurate assessment of a person’s state under free-living conditions. In this work, we use two mobile sensing devices and investigate the correlation between individual’s resting metabolic rate (RMR) and volatile organic compounds (VOCs) exposure levels. A total of 17 healthy, young, and sedentary office workers were recruited, measured for RMR with a mobile indirect calorimetry (IC) device, and compared with their corresponding predicted RMR values from the Academy of Nutrition and Dietetics’ recommended epidemiological equation, the Mifflin–St Jeor equation (MSJE). Individual differences in the RMR values from the IC device and the epidemiological equation were found, and the subjects’ RMRs were classified as normal, high, or low based on a cut-off of ±200 kcal/day difference with respect to the predicted value. To study the cause of the difference, VOCs exposure levels of each participant’s daytime working environment and nighttime resting environment were assessed using a second mobile sensing device for VOCs exposure detection. The results showed that all sedentary office workers had a low VOCs exposure level (<2 ppmC), and there was no obvious correlation between VOCs exposure and the RMR difference. However, an additional participant who was a worker in an auto repair shop, showed high VOCs exposure with respect to the sedentary office worker population and a significant difference between measured and predicted RMR, with a low RMR of 500 kcal/day difference. The mobile sensing devices have been demonstrated to be suitable for the assessment of direct information of human health–environment interactions at free-living conditions. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle A Fiber Bragg Grating Based Torsional Vibration Sensor for Rotating Machinery
Sensors 2018, 18(8), 2669; https://doi.org/10.3390/s18082669 (registering DOI)
Received: 8 June 2018 / Revised: 3 August 2018 / Accepted: 8 August 2018 / Published: 14 August 2018
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Abstract
This paper proposes a new type of torsional vibration sensor based on fiber Bragg grating (FBG). The sensor has two mass ball optical fiber systems. The optical fiber is directly treated as an elastomer and a mass ball is fixed in the middle
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This paper proposes a new type of torsional vibration sensor based on fiber Bragg grating (FBG). The sensor has two mass ball optical fiber systems. The optical fiber is directly treated as an elastomer and a mass ball is fixed in the middle of the fiber in each mass ball fiber system, which is advantageously small, lightweight, and has anti-electromagnetic interference properties. The torsional vibration signal can be calculated by the four FBGs’ wavelength shifts, which are caused by mass balls. The difference in the two sets of mass ball optical fiber systems achieves anti-horizontal vibration and anti-temperature interference. The principle and model of the sensor, as well as numerical analysis and structural parameter design, are introduced. The experimental conclusions show that the minimum torsional natural frequency of the sensor is 27.35 Hz and the torsional vibration measurement sensitivity is 0.3603 pm/(rad/s2). Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)
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Open AccessArticle Biosensors for the Detection of Interaction between Legionella pneumophila Collagen-Like Protein and Glycosaminoglycans
Sensors 2018, 18(8), 2668; https://doi.org/10.3390/s18082668 (registering DOI)
Received: 10 July 2018 / Revised: 8 August 2018 / Accepted: 10 August 2018 / Published: 14 August 2018
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Abstract
The adhesin Legionella collagen-like (Lcl) protein can bind to extracellular matrix components and mediate the binding of Legionella pneumophila to host cells. In this study, electrochemical impedance spectroscopy (EIS) and surface plasmon resonance (SPR)-based biosensors were employed to characterize these interactions between glycosaminoglycans
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The adhesin Legionella collagen-like (Lcl) protein can bind to extracellular matrix components and mediate the binding of Legionella pneumophila to host cells. In this study, electrochemical impedance spectroscopy (EIS) and surface plasmon resonance (SPR)-based biosensors were employed to characterize these interactions between glycosaminoglycans (GAGs) and the adhesin Lcl protein. Fucoidan displayed a high affinity (KD 18 nM) for Lcl protein. Chondroitin sulfate A and dermatan sulfate differ in the position of a carboxyl group replacing D-glucuronate with D-iduronate. Our results indicated that the presence of D-iduronate in dermatan sulfate strongly hindered its interaction with Lcl. These biophysical studies provided valuable information in our understanding of adhesin-ligand interactions related to Legionella pneumophila infections. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Canada 2018)
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Open AccessArticle Research on Construction Workers’ Activity Recognition Based on Smartphone
Sensors 2018, 18(8), 2667; https://doi.org/10.3390/s18082667 (registering DOI)
Received: 11 July 2018 / Revised: 9 August 2018 / Accepted: 9 August 2018 / Published: 14 August 2018
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Abstract
This research on identification and classification of construction workers’ activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic flexibility and stability, this paper
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This research on identification and classification of construction workers’ activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic flexibility and stability, this paper proposes an approach to construction-activity recognition based on smartphones. The accelerometers and gyroscopes embedded in smartphones were utilized to collect three-axis acceleration and angle data of eight main activities with relatively high frequency in simulated floor-reinforcing steel work. Data acquisition from multiple body parts enhanced the dimensionality of activity features to better distinguish between different activities. The CART algorithm of a decision tree was adopted to build a classification training model whose effectiveness was evaluated and verified through cross-validation. The results showed that the accuracy of classification for overall samples was up to 89.85% and the accuracy of prediction was 94.91%. The feasibility of using smartphones as data-acquisition tools in construction management was verified. Moreover, it was proved that the combination of a decision-tree algorithm with smartphones could achieve complex activity classification and identification. Full article
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Open AccessArticle Fine-Grained Face Annotation Using Deep Multi-Task CNN
Sensors 2018, 18(8), 2666; https://doi.org/10.3390/s18082666 (registering DOI)
Received: 3 July 2018 / Revised: 6 August 2018 / Accepted: 13 August 2018 / Published: 14 August 2018
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Abstract
We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct recognition tasks: age group, gender and visual
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We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct recognition tasks: age group, gender and visual attributes (such as hair color, face shape and the presence of makeup). The proposed model shares all the CNN’s parameters among tasks and deals with task-specific estimation through the introduction of two components: (i) a gating mechanism to control activations’ sharing and to adaptively route them across different face attributes; (ii) a module to post-process the predictions in order to take into account the correlation among face attributes. The model is trained by fusing multiple databases for increasing the number of face attributes that can be estimated and using a center loss for disentangling representations among face attributes in the embedding space. Extensive experiments validate the effectiveness of the proposed approach. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle Unified Channel Management for Cognitive Radio Sensor Networks Aided Internet of Things
Sensors 2018, 18(8), 2665; https://doi.org/10.3390/s18082665 (registering DOI)
Received: 14 July 2018 / Revised: 7 August 2018 / Accepted: 11 August 2018 / Published: 14 August 2018
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Abstract
Cognitive capabilities are indispensable for the Internet of Things (IoT) not only to equip them with learning, thinking, and decision-making capabilities but also to cater to their unprecedented huge spectrum requirements due to their gigantic numbers and heterogeneity. Therefore, in this paper, a
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Cognitive capabilities are indispensable for the Internet of Things (IoT) not only to equip them with learning, thinking, and decision-making capabilities but also to cater to their unprecedented huge spectrum requirements due to their gigantic numbers and heterogeneity. Therefore, in this paper, a novel unified channel management framework (CMF) is introduced for cognitive radio sensor networks (CRSNs), which comprises an (1) opportunity detector (ODR), (2) opportunity scheduler (OSR), and (3) opportunity ranker (ORR) to specifically address the immense and diverse spectrum requirements of CRSN-aided IoT. The unified CMF is unique for its type as it covers all three angles of spectrum management. The ODR is a double threshold based multichannel spectrum sensor that allows an IoT device to concurrently sense multiple channels to maximize spectrum opportunities. OSR is an integer linear programming (ILP) based channel allocation mechanism that assigns channels to heterogeneous IoT devices based on their minimal quality of service (QoS) requirements. ORR collects feedback from IoT devices about their transmission experience and generates special channel-sensing order (CSO) for each IoT device based on the data rate and idle-time probabilities. The simulation results demonstrate that the proposed CMF outperforms the existing ones in terms of collision probability, detection probability, blocking probability, idle-time probability, and data rate. Full article
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