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Sensors, Volume 19, Issue 14 (July-2 2019)

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Open AccessArticle
Orthogonal Demodulation Pound–Drever–Hall Technique for Ultra-Low Detection Limit Pressure Sensing
Sensors 2019, 19(14), 3223; https://doi.org/10.3390/s19143223 (registering DOI)
Received: 10 June 2019 / Revised: 15 July 2019 / Accepted: 20 July 2019 / Published: 22 July 2019
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Abstract
We report on a novel optical microcavity sensing scheme by using the orthogonal demodulation Pound–Drever–Hall (PDH) technique. We found that larger sensitivity in a broad range of cavity quality factor (Q) could be obtained. Taking microbubble resonator (MBR) pressure sensing as an example, [...] Read more.
We report on a novel optical microcavity sensing scheme by using the orthogonal demodulation Pound–Drever–Hall (PDH) technique. We found that larger sensitivity in a broad range of cavity quality factor (Q) could be obtained. Taking microbubble resonator (MBR) pressure sensing as an example, a lower detection limit than the conventional wavelength shift detection method was achieved. When the MBR cavity Q is about 105–106, the technique can decrease the detection limit by one or two orders of magnitude. The pressure-frequency sensitivity is 11.6 GHz/bar at wavelength of 850 nm, and its detection limit can approach 0.0515 mbar. This technique can also be applied to other kinds of microcavity sensors to improve sensing performance. Full article
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Open AccessArticle
Wireless, Portable Fiber Bragg Grating Interrogation System Employing Optical Edge Filter
Sensors 2019, 19(14), 3222; https://doi.org/10.3390/s19143222 (registering DOI)
Received: 10 June 2019 / Revised: 11 July 2019 / Accepted: 18 July 2019 / Published: 22 July 2019
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Abstract
A small-size, high-precision fiber Bragg grating interrogator was developed for continuous plethysmograph monitoring. The interrogator employs optical edge filters, which were integrated with a broad-band light source and photodetector to demodulate the Bragg wavelength shift. An amplifier circuit was designed to effectively amplify [...] Read more.
A small-size, high-precision fiber Bragg grating interrogator was developed for continuous plethysmograph monitoring. The interrogator employs optical edge filters, which were integrated with a broad-band light source and photodetector to demodulate the Bragg wavelength shift. An amplifier circuit was designed to effectively amplify the plethysmograph signal, obtained as a small vibration of optical power on the large offset. The standard deviation of the measured Bragg wavelength was about 0.1 pm. The developed edge filter module and amplifier circuit were encased with a single-board computer and communicated with a laptop computer via Wi-Fi. As a result, the plethysmograph was clearly obtained remotely, indicating the possibility of continuous vital sign measurement. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Experimental Validation of Gaussian Process-Based Air-to-Ground Communication Quality Prediction in Urban Environments
Sensors 2019, 19(14), 3221; https://doi.org/10.3390/s19143221 (registering DOI)
Received: 12 July 2019 / Revised: 12 July 2019 / Accepted: 17 July 2019 / Published: 22 July 2019
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Abstract
This paper presents a detailed experimental assessment of Gaussian Process (GP) regression for air-to-ground communication channel prediction for relay missions in urban environment. Considering restrictions from outdoor urban flight experiments, a way to simulate complex urban environments at an indoor room scale is [...] Read more.
This paper presents a detailed experimental assessment of Gaussian Process (GP) regression for air-to-ground communication channel prediction for relay missions in urban environment. Considering restrictions from outdoor urban flight experiments, a way to simulate complex urban environments at an indoor room scale is introduced. Since water significantly absorbs wireless communication signal, water containers are utilized to replace buildings in a real-world city. To evaluate the performance of the GP-based channel prediction approach, several indoor experiments in an artificial urban environment were conducted. The performance of the GP-based and empirical model-based prediction methods for a relay mission was evaluated by measuring and comparing the communication signal strength at the optimal relay position obtained from each method. The GP-based prediction approach shows an advantage over the model-based one as it provides a reasonable performance without a need for a priori information of the environment (e.g., 3D map of the city and communication model parameters) in dynamic urban environments. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Monocular Robust Depth Estimation Vision System for Robotic Tasks Interventions in Metallic Targets
Sensors 2019, 19(14), 3220; https://doi.org/10.3390/s19143220 (registering DOI)
Received: 12 June 2019 / Revised: 6 July 2019 / Accepted: 17 July 2019 / Published: 22 July 2019
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Abstract
Robotic interventions in hazardous scenarios need to pay special attention to safety, as in most cases it is necessary to have an expert operator in the loop. Moreover, the use of a multi-modal Human-Robot Interface allows the user to interact with the robot [...] Read more.
Robotic interventions in hazardous scenarios need to pay special attention to safety, as in most cases it is necessary to have an expert operator in the loop. Moreover, the use of a multi-modal Human-Robot Interface allows the user to interact with the robot using manual control in critical steps, as well as semi-autonomous behaviours in more secure scenarios, by using, for example, object tracking and recognition techniques. This paper describes a novel vision system to track and estimate the depth of metallic targets for robotic interventions. The system has been designed for on-hand monocular cameras, focusing on solving lack of visibility and partial occlusions. This solution has been validated during real interventions at the Centre for Nuclear Research (CERN) accelerator facilities, achieving 95% success in autonomous mode and 100% in a supervised manner. The system increases the safety and efficiency of the robotic operations, reducing the cognitive fatigue of the operator during non-critical mission phases. The integration of such an assistance system is especially important when facing complex (or repetitive) tasks, in order to reduce the work load and accumulated stress of the operator, enhancing the performance and safety of the mission. Full article
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Open AccessArticle
Two Degree-of-Freedom Fiber-Coupled Heterodyne Grating Interferometer with Milli-Radian Operating Range of Rotation
Sensors 2019, 19(14), 3219; https://doi.org/10.3390/s19143219 (registering DOI)
Received: 9 May 2019 / Revised: 18 July 2019 / Accepted: 20 July 2019 / Published: 22 July 2019
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Abstract
In the displacement measurement of the wafer stage in lithography machines, signal quality is affected by the relative angular position between the encoder head and the grating. In this study, a two-degree-of-freedom fiber-coupled heterodyne grating interferometer with large operating range of rotation is [...] Read more.
In the displacement measurement of the wafer stage in lithography machines, signal quality is affected by the relative angular position between the encoder head and the grating. In this study, a two-degree-of-freedom fiber-coupled heterodyne grating interferometer with large operating range of rotation is presented. Fibers without fiber couplers are utilized to receive the interference beams for high-contrast signals under the circumstances of large angular displacement and ZEMAX ray tracing software simulation and experimental validation have been carried out. Meanwhile, a reference beam generated inside the encoder head is adopted to suppress the thermal drift of the interferometer. Experimental results prove that the proposed grating interferometer could realize sub-nanometer displacement measurement stability in both in-plane and out-of-plane directions, which is 0.246 nm and 0.465 nm of 3σ value respectively within 30 s. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
Electrochemical Sensing of α-Fetoprotein Based on Molecularly Imprinted Polymerized Ionic Liquid Film on a Gold Nanoparticle Modified Electrode Surface
Sensors 2019, 19(14), 3218; https://doi.org/10.3390/s19143218 (registering DOI)
Received: 28 May 2019 / Revised: 24 June 2019 / Accepted: 16 July 2019 / Published: 22 July 2019
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Abstract
A molecularly imprinted sensor was fabricated for alpha-fetoprotein (AFP) using an ionic liquid as a functional monomer. Ionic liquid possesses many excellent characteristics which can improve the sensing performances of the imprinted electrochemical sensor. To demonstrate this purpose, 1-[3-(N-cystamine)propyl]-3-vinylimidazolium tetrafluoroborate ionic liquid [(Cys)VIMBF [...] Read more.
A molecularly imprinted sensor was fabricated for alpha-fetoprotein (AFP) using an ionic liquid as a functional monomer. Ionic liquid possesses many excellent characteristics which can improve the sensing performances of the imprinted electrochemical sensor. To demonstrate this purpose, 1-[3-(N-cystamine)propyl]-3-vinylimidazolium tetrafluoroborate ionic liquid [(Cys)VIMBF4] was synthesized and used as a functional monomer to fabricate an AFP imprinted polymerized ionic liquid film on a gold nanoparticle modified glassy carbon electrode (GCE) surface at room temperature. After removing the AFP template, a molecularly imprinted electrochemical sensor was successfully prepared. The molecularly imprinted sensor exhibits excellent selectivity towards AFP, and can be used for sensitive determination of AFP. Under the optimized conditions, the imprinted sensor shows a good linear response to AFP in the concentration range of 0.03 ng mL−1~5 ng mL−1. The detection limit is estimated to be 2 pg mL−1. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems
Sensors 2019, 19(14), 3217; https://doi.org/10.3390/s19143217 (registering DOI)
Received: 18 June 2019 / Revised: 18 July 2019 / Accepted: 19 July 2019 / Published: 22 July 2019
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Abstract
Most approaches for moving object detection (MOD) based on computer vision are limited to stationary camera environments. In advanced driver assistance systems (ADAS), however, ego-motion is added to image frames owing to the use of a moving camera. This results in mixed motion [...] Read more.
Most approaches for moving object detection (MOD) based on computer vision are limited to stationary camera environments. In advanced driver assistance systems (ADAS), however, ego-motion is added to image frames owing to the use of a moving camera. This results in mixed motion in the image frames and makes it difficult to classify target objects and background. In this paper, we propose an efficient MOD algorithm that can cope with moving camera environments. In addition, we present a hardware design and implementation results for the real-time processing of the proposed algorithm. The proposed moving object detector was designed using hardware description language (HDL) and its real-time performance was evaluated using an FPGA based test system. Experimental results demonstrate that our design achieves better detection performance than existing MOD systems. The proposed moving object detector was implemented with 13.2K logic slices, 104 DSP48s, and 163 BRAM and can support real-time processing of 30 fps at an operating frequency of 200 MHz. Full article
(This article belongs to the Special Issue Perception Sensors for Road Applications)
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Open AccessArticle
Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders
Sensors 2019, 19(14), 3216; https://doi.org/10.3390/s19143216
Received: 1 June 2019 / Revised: 18 July 2019 / Accepted: 19 July 2019 / Published: 22 July 2019
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Abstract
Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data. This paper takes the satellite power subsystem as an example and presents a reliable anomaly detection method. Due to the lack of abnormal data, [...] Read more.
Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data. This paper takes the satellite power subsystem as an example and presents a reliable anomaly detection method. Due to the lack of abnormal data, the autoencoder is a powerful method for unsupervised anomaly detection. This study proposes a novel stage-training denoising autoencoder (ST-DAE) that trains the features, in stages. This novel method has better reconstruction capabilities in comparison to common autoencoders, sparse autoencoders, and denoising autoencoders. Meanwhile, a cluster-based anomaly threshold determination method is proposed. In this study, specific methods were designed to evaluate the autoencoder performance in three perspectives. Experiments were carried out on real satellite telemetry data, and the results showed that the proposed ST-DAE generally outperformed the autoencoders, in comparison. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Open AccessArticle
The Effect of the Color Filter Array Layout Choice on State-of-the-Art Demosaicing
Sensors 2019, 19(14), 3215; https://doi.org/10.3390/s19143215
Received: 20 June 2019 / Revised: 18 July 2019 / Accepted: 18 July 2019 / Published: 21 July 2019
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Abstract
Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development [...] Read more.
Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development in the past 20 years ranges from simple linear interpolation to modern neural-network-based (NN) approaches that encode the prior knowledge of millions of training images to fill in missing data in an inconspicious way. CFA design, on the other hand, is less well studied, although still recognized to strongly impact demosaicing performance. This is because demosaicing algorithms are typically limited to one particular CFA pattern, impeding straightforward CFA comparison. This is starting to change with newer classes of demosaicing that may be considered generic or CFA-agnostic. In this study, by comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing. We test the hypothesis that, with the increasing power of NN-based demosaicing, the influence of optimal CFA design on system performance decreases. This hypothesis is supported with the experimental results. Such a finding would herald the possibility of relaxing CFA requirements, providing more freedom in the CFA design choice and producing high-quality cameras. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing II)
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Open AccessArticle
A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet
Sensors 2019, 19(14), 3214; https://doi.org/10.3390/s19143214
Received: 22 June 2019 / Revised: 13 July 2019 / Accepted: 19 July 2019 / Published: 21 July 2019
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Abstract
Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning algorithm-based CVD diagnosis methods are [...] Read more.
Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning algorithm-based CVD diagnosis methods are extensively employed, however, most such algorithms can only utilize one-lead ECGs. Hence, the potential information in other-lead ECGs was not utilized. To address this issue, we have developed novel methods for diagnosing arrhythmia. In this work, DL-CCANet and TL-CCANet are proposed to extract abstract discriminating features from dual-lead and three-lead ECGs, respectively. Then, the linear support vector machine specializing in high-dimensional features is used as the classifier model. On the MIT-BIH database, a 95.2% overall accuracy is obtained by detecting 15 types of heartbeats using DL-CCANet. On the INCART database, overall accuracies of 94.01% (II and V1 leads), 93.90% (V1 and V5 leads) and 94.07% (II and V5 leads) are achieved by detecting seven types of heartbeat using DL-CCANet, while TL-CCANet yields a higher overall accuracy of 95.52% using the above three leads. In addition, all of the above experiments are implemented using noisy ECG data. The proposed methods have potential to be applied in the clinic and mobile devices. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for Disease Diagnosis)
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Open AccessArticle
Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview
Sensors 2019, 19(14), 3213; https://doi.org/10.3390/s19143213
Received: 27 April 2019 / Revised: 11 July 2019 / Accepted: 17 July 2019 / Published: 21 July 2019
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Abstract
The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial [...] Read more.
The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones. Full article
(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)
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Open AccessArticle
Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment
Sensors 2019, 19(14), 3212; https://doi.org/10.3390/s19143212
Received: 29 May 2019 / Revised: 8 July 2019 / Accepted: 18 July 2019 / Published: 21 July 2019
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Abstract
Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated [...] Read more.
Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated by using the Velodyne VLP-16 LiDAR system, so as to extract the diameter at breast height (DBH) of individual trees. The Velodyne VLP-16 LiDAR system and inertial measurement units (IMU) were used to construct a mobile measurement platform for generating 3D point cloud maps for forest areas. The 3D point cloud map in the forest area was processed offline, and the ground point cloud was removed by the random sample consensus (RANSAC) algorithm. The trees in the experimental area were segmented by the European clustering algorithm, and the DBH component of the tree point cloud was extracted and projected onto a 2D plane, fitting the DBH of the trees using the RANSAC algorithm in the plane. A three-dimensional point cloud map of 71 trees was generated in the experimental area, and estimated the DBH. The mean and variance of the absolute error were 0.43 cm and 0.50, respectively. The relative error of the whole was 2.27%, the corresponding variance was 15.09, and the root mean square error (RMSE) was 0.70 cm. The experimental results were good and met the requirements of forestry mapping, and the application value and significance were presented. Full article
(This article belongs to the Special Issue Mobile Laser Scanning Systems)
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Open AccessArticle
Application of Sapphire-Fiber-Bragg-Grating-Based Multi-Point Temperature Sensor in Boilers at a Commercial Power Plant
Sensors 2019, 19(14), 3211; https://doi.org/10.3390/s19143211
Received: 29 June 2019 / Revised: 17 July 2019 / Accepted: 19 July 2019 / Published: 21 July 2019
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Abstract
Readily available temperature sensing in boilers is necessary to improve efficiencies, minimize downtime, and reduce toxic emissions for a power plant. The current techniques are typically deployed as a single-point measurement and are primarily used for detection and prevention of catastrophic events due [...] Read more.
Readily available temperature sensing in boilers is necessary to improve efficiencies, minimize downtime, and reduce toxic emissions for a power plant. The current techniques are typically deployed as a single-point measurement and are primarily used for detection and prevention of catastrophic events due to the harsh environment. In this work, a multi-point temperature sensor based on wavelength-multiplexed sapphire fiber Bragg gratings (SFBGs) were fabricated via the point-by-point method with a femtosecond laser. The sensor was packaged and calibrated in the lab, including thermally equilibrating at 1200 °C, followed by a 110-h, 1000 °C stability test. After laboratory testing, the sensor system was deployed in both a commercial coal-fired and a gas-fired boiler for 42 days and 48 days, respectively. The performance of the sensor was consistent during the entire test duration, over the course of which it measured temperatures up to 950 °C (with some excursions over 1000 °C), showing the survivability of the sensor in a field environment. The sensor has a demonstrated measurement range from room temperature to 1200 °C, but the maximum temperature limit is expected to be up to 1900 °C, based on previous work with other sapphire based temperature sensors. Full article
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Open AccessArticle
Application of ECIS to Assess FCCP-Induced Changes of MSC Micromotion and Wound Healing Migration
Sensors 2019, 19(14), 3210; https://doi.org/10.3390/s19143210
Received: 17 June 2019 / Revised: 19 July 2019 / Accepted: 19 July 2019 / Published: 21 July 2019
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Abstract
Electric cell-substrate impedance sensing (ECIS) is an emerging technique for sensitively monitoring morphological changes of adherent cells in tissue culture. In this study, human mesenchymal stem cells (hMSCs) were exposed to different concentrations of carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) for 20 h and their [...] Read more.
Electric cell-substrate impedance sensing (ECIS) is an emerging technique for sensitively monitoring morphological changes of adherent cells in tissue culture. In this study, human mesenchymal stem cells (hMSCs) were exposed to different concentrations of carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) for 20 h and their subsequent concentration-dependent responses in micromotion and wound healing migration were measured by ECIS. FCCP disrupts ATP synthesis and results in a decrease in cell migration rates. To detect the change of cell micromotion in response to FCCP challenge, time-series resistances of cell-covered electrodes were monitored and the values of variance were calculated to verify the difference. While Seahorse XF-24 extracellular flux analyzer can detect the effect of FCCP at 3 μM concentration, the variance calculation of the time-series resistances measured at 4 kHz can detect the effect of FCCP at concentrations as low as 1 μM. For wound healing migration, the recovery resistance curves were fitted by sigmoid curve and the hill slope showed a concentration-dependent decline from 0.3 μM to 3 μM, indicating a decrease in cell migration rate. Moreover, dose dependent incline of the inflection points from 0.3 μM to 3 μM FCCP implied the increase of the half time for wound recovery migration. Together, our results demonstrate that partial uncoupling of mitochondrial oxidative phosphorylation reduces micromotion and wound healing migration of hMSCs. The ECIS method used in this study offers a simple and sensitive approach to investigate stem cell migration and its regulation by mitochondrial dynamics. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle
Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks
Sensors 2019, 19(14), 3209; https://doi.org/10.3390/s19143209
Received: 7 June 2019 / Revised: 15 July 2019 / Accepted: 18 July 2019 / Published: 21 July 2019
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Abstract
The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the [...] Read more.
The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the models is evaluated based on the field measurement and the mentioned backscattering models, CIEM and MDB performed with root mean square error (RMSE) of 0.78 dB and 1.45 dB, respectively. In the second step, based on the neural networks (NNS), soil surface moisture is estimated using the two backscattering models, based on neural networks (NNs), from single polarization Sentinel-1 images over bare soils. The inversion results show the efficiency of the single polarized data for retrieving soil surface moisture, especially for VV polarization. Full article
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Open AccessArticle
Physical Unclonable Functions in the Internet of Things: State of the Art and Open Challenges
Sensors 2019, 19(14), 3208; https://doi.org/10.3390/s19143208
Received: 29 April 2019 / Revised: 17 June 2019 / Accepted: 21 June 2019 / Published: 21 July 2019
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Abstract
Attacks on Internet of Things (IoT) devices are on the rise. Physical Unclonable Functions (PUFs) are proposed as a robust and lightweight solution to secure IoT devices. The main advantage of a PUF compared to the current classical cryptographic solutions is its compatibility [...] Read more.
Attacks on Internet of Things (IoT) devices are on the rise. Physical Unclonable Functions (PUFs) are proposed as a robust and lightweight solution to secure IoT devices. The main advantage of a PUF compared to the current classical cryptographic solutions is its compatibility with IoT devices with limited computational resources. In this paper, we investigate the maturity of this technology and the challenges toward PUF utilization in IoT that still need to be addressed. Full article
(This article belongs to the Special Issue Context-Awareness in the Internet of Things)
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Open AccessArticle
Optical Temperature Control Unit and Convolutional Neural Network for Colorimetric Detection of Loop-Mediated Isothermal Amplification on a Lab-On-A-Disc Platform
Sensors 2019, 19(14), 3207; https://doi.org/10.3390/s19143207
Received: 20 May 2019 / Revised: 5 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
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Abstract
Lab-on-a-disc (LOD) has emerged as a promising candidate for a point-of-care testing (POCT) device because it can effectively integrate complex fluid manipulation steps using multiple layers of polymeric substrates. However, it is still highly challenging to design and fabricate temperature measurement and heating [...] Read more.
Lab-on-a-disc (LOD) has emerged as a promising candidate for a point-of-care testing (POCT) device because it can effectively integrate complex fluid manipulation steps using multiple layers of polymeric substrates. However, it is still highly challenging to design and fabricate temperature measurement and heating system in non-contact with the surface of LOD, which is a prerequisite to successful realization of DNA amplification especially with a rotatable disc. This study presents a Lab-on-a-disc (LOD)-based automatic loop-mediated isothermal amplification (LAMP) system, where a thermochromic coating (<~420 µm) was used to distantly measure the chamber’s temperature and a micro graphite film was integrated into the chamber to remotely absorb laser beam with super high efficiency. We used a deep learning network to more consistently analyze the product of LAMP than we could with the naked eye. Consequently, both temperature heating and measurement were carried out without a physical contact with the surface of LOD. The experimental results show that the proposed approach, which no previous work has attempted, was highly effective in realizing LAMP in LOD. Full article
(This article belongs to the Special Issue Sensors and Lab-on-a-Chip)
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Open AccessArticle
Adaptive Noise Reduction for Sound Event Detection Using Subband-Weighted NMF
Sensors 2019, 19(14), 3206; https://doi.org/10.3390/s19143206
Received: 26 May 2019 / Revised: 16 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
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Abstract
Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). First, a scheme for noise dictionary learning from the input [...] Read more.
Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). First, a scheme for noise dictionary learning from the input noisy signal is employed by the technique of robust NMF, which supports adaptation to noise variations. The estimated noise dictionary is used to develop a supervised source separation framework in combination with a pre-trained event dictionary. Second, to improve the separation quality, we extend the basic NMF model to a weighted form, with the aim of varying the relative importance of the different components when separating a target sound event from noise. With properly designed weights, the separation process is forced to rely more on those dominant event components, whereas the noise gets greatly suppressed. The proposed method is evaluated on a dataset of the rare sound event detection task of the DCASE 2017 challenge, and achieves comparable results to the top-ranking system based on convolutional recurrent neural networks (CRNNs). The proposed weighted NMF method shows an excellent noise reduction ability, and achieves an improvement of an F-score by 5%, compared to the unweighted approach. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations
Sensors 2019, 19(14), 3205; https://doi.org/10.3390/s19143205
Received: 15 June 2019 / Revised: 16 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
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Abstract
Geospatial products, such as digital elevation models (DEMs), are important topographic tools for tackling local flood studies. This study investigates the contribution of LiDAR elevation data in DEM generation based on fixed-wing unmanned aerial vehicle (UAV) imaging for flood applications. More specifically, it [...] Read more.
Geospatial products, such as digital elevation models (DEMs), are important topographic tools for tackling local flood studies. This study investigates the contribution of LiDAR elevation data in DEM generation based on fixed-wing unmanned aerial vehicle (UAV) imaging for flood applications. More specifically, it assesses the accuracy of UAV-derived DEMs using the proposed LiDAR-derived control point (LCP) method in a Structure-from-Motion photogrammetry processing. Also, the flood estimates (volume and area) of the UAV terrain products are compared with a LiDAR-based reference. The applied LCP-georeferencing method achieves an accuracy comparable with other studies. In addition, it has the advantage of using semi-automatic terrain data classification and is readily applicable in flood studies. Lastly, it proves the complementarity between LiDAR and UAV photogrammetry at the local level. Full article
(This article belongs to the Special Issue Urban Remote Sensing and Sustainable Development)
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Open AccessArticle
Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?
Sensors 2019, 19(14), 3204; https://doi.org/10.3390/s19143204
Received: 30 May 2019 / Revised: 8 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
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Abstract
Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface [...] Read more.
Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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Open AccessArticle
Design and Analysis of a Magnetically Coupled Multi-Frequency Hybrid Energy Harvester
Sensors 2019, 19(14), 3203; https://doi.org/10.3390/s19143203
Received: 10 June 2019 / Revised: 11 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
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Abstract
The approach to improve the output power of piezoelectric energy harvester is one of the current research hotspots. In the case where some sources have two or more discrete vibration frequencies, this paper proposed three types of magnetically coupled multi-frequency hybrid energy harvesters [...] Read more.
The approach to improve the output power of piezoelectric energy harvester is one of the current research hotspots. In the case where some sources have two or more discrete vibration frequencies, this paper proposed three types of magnetically coupled multi-frequency hybrid energy harvesters (MHEHs) to capture vibration energy composed of two discrete frequencies. Electromechanical coupling models were established to analyze the magnetic forces, and to evaluate the power generation characteristics, which were verified by the experimental test. The optimal structure was selected through the comparison. With 2 m/s2 excitation acceleration, the optimal peak output power was 2.96 mW at 23.6 Hz and 4.76 mW at 32.8 Hz, respectively. The superiority of hybrid energy harvesting mechanism was demonstrated. The influences of initial center-to-center distances between two magnets and length of cantilever beam on output power were also studied. At last, the frequency sweep test was conducted. Both theoretical and experimental analyses indicated that the proposed MHEH produced more electric power over a larger operating bandwidth. Full article
(This article belongs to the Special Issue Piezoelectric Transducers)
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Open AccessArticle
A Sensitive Carbon Monoxide Sensor Based on Photoacoustic Spectroscopy with a 2.3 μm Mid-Infrared High-Power Laser and Enhanced Gas Absorption
Sensors 2019, 19(14), 3202; https://doi.org/10.3390/s19143202
Received: 2 July 2019 / Revised: 17 July 2019 / Accepted: 19 July 2019 / Published: 20 July 2019
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Abstract
A photoacoustic spectroscopy (PAS)-based carbon monoxide (CO) gas sensor with a high-power laser and an enhanced gas absorption was demonstrated. The light source was a distributed feedback (DFB), continuous wave (CW) diode laser with a high output power of ~8 mW to give [...] Read more.
A photoacoustic spectroscopy (PAS)-based carbon monoxide (CO) gas sensor with a high-power laser and an enhanced gas absorption was demonstrated. The light source was a distributed feedback (DFB), continuous wave (CW) diode laser with a high output power of ~8 mW to give a strong excitation. The target gas received optical absorption enhanced two times by using a right-angle prism reflecting the laser beam. In order to reduce the noise from the background, wavelength modulation spectroscopy (WMS) and second-harmonic detection techniques were used. The modulation frequency and modulation depth were optimized theoretically and experimentally. Water vapor was added in the PAS sensor system to increase the vibrational–translational (V–T) relaxation rate of the CO molecule, which resulted in an ~8 times signal enhancement compared with the using of a dry CO/N2 gas mixture. The amplitude of the 2f signal had a 1.52-fold improvement compared to the one with only one time absorption. The experimental results showed that such a sensor had an excellent linear response to the optical power and gas concentration. At 1 s integration time, a minimum detection limit (MDL) for CO detection of 9.8 ppm was achieved. The long-term stability of the sensor system was evaluated with an Allan deviation analysis. When the integration time was 1100 s, the MDL improved to be 530 ppb. The detection performance of such a PAS-based CO sensor can be further improved when a laser with a higher output power and increasing optical absorption times is used. Full article
(This article belongs to the Special Issue Recent Advances in Gas Nanosensors)
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Open AccessArticle
A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming
Sensors 2019, 19(14), 3201; https://doi.org/10.3390/s19143201
Received: 30 June 2019 / Revised: 16 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
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Abstract
Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to “concept drift”, which occurs when systems [...] Read more.
Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to “concept drift”, which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions. Full article
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
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Open AccessArticle
Lightweight Driver Monitoring System Based on Multi-Task Mobilenets
Sensors 2019, 19(14), 3200; https://doi.org/10.3390/s19143200
Received: 12 June 2019 / Revised: 11 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
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Abstract
Research on driver status recognition has been actively conducted to reduce fatal crashes caused by the driver’s distraction and drowsiness. As in many other research areas, deep-learning-based algorithms are showing excellent performance for driver status recognition. However, despite decades of research in the [...] Read more.
Research on driver status recognition has been actively conducted to reduce fatal crashes caused by the driver’s distraction and drowsiness. As in many other research areas, deep-learning-based algorithms are showing excellent performance for driver status recognition. However, despite decades of research in the driver status recognition area, the visual image-based driver monitoring system has not been widely used in the automobile industry. This is because the system requires high-performance processors, as well as has a hierarchical structure in which each procedure is affected by an inaccuracy from the previous procedure. To avoid using a hierarchical structure, we propose a method using Mobilenets without the functions of face detection and tracking and show this method is enabled to recognize facial behaviors that indicate the driver’s distraction. However, frames per second processed by Mobilenets with a Raspberry pi, one of the single-board computers, is not enough to recognize the driver status. To alleviate this problem, we propose a lightweight driver monitoring system using a resource sharing device in a vehicle (e.g., a driver’s mobile phone). The proposed system is based on Multi-Task Mobilenets (MT-Mobilenets), which consists of the Mobilenets’ base and multi-task classifier. The three Softmax regressions of the multi-task classifier help one Mobilenets base recognize facial behaviors related to the driver status, such as distraction, fatigue, and drowsiness. The proposed system based on MT-Mobilenets improved the accuracy of the driver status recognition with Raspberry Pi by using one additional device. Full article
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
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Open AccessCommunication
Converting a Common Low-Cost Document Scanner into a Multispectral Scanner
Sensors 2019, 19(14), 3199; https://doi.org/10.3390/s19143199
Received: 20 May 2019 / Revised: 18 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
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Abstract
Forged documents and counterfeit currency can be better detected with multispectral imaging in multiple color channels instead of the usual red, green and blue. However, multispectral cameras/scanners are expensive. We propose the construction of a low cost scanner designed to capture multispectral images [...] Read more.
Forged documents and counterfeit currency can be better detected with multispectral imaging in multiple color channels instead of the usual red, green and blue. However, multispectral cameras/scanners are expensive. We propose the construction of a low cost scanner designed to capture multispectral images of documents. A standard sheet-feed scanner was modified by disconnecting its internal light source and connecting an external multispectral light source comprising of narrow band light emitting diodes (LED). A document was scanned by illuminating the scanner light guide successively with different LEDs and capturing a scan of the document. The system costs less than a hundred dollars and is portable. It can potentially be used for applications in verification of questioned documents, checks, receipts and bank notes. Full article
(This article belongs to the Special Issue Document-Image Related Visual Sensors and Machine Learning Techniques)
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Open AccessArticle
Research on the Weld Position Detection Method for Sandwich Structures from Face-Panel Side Based on Backscattered X-ray
Sensors 2019, 19(14), 3198; https://doi.org/10.3390/s19143198
Received: 5 June 2019 / Revised: 7 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
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Abstract
Web-core sandwich panels are a typical lightweight structure utilized in a variety of fields, such as naval, aviation, aerospace, etc. Welding is considered as an effective process to join the face panel to the core panel from the face panel side. However, it [...] Read more.
Web-core sandwich panels are a typical lightweight structure utilized in a variety of fields, such as naval, aviation, aerospace, etc. Welding is considered as an effective process to join the face panel to the core panel from the face panel side. However, it is difficult to locate the joint position (i.e., the position of core panel) due to the shielding of the face panel. This paper studies a weld position detection method based on X-ray from the face panel side for aluminum web-core sandwich panels used in aviation and naval structures. First, an experimental system was designed for weld position detection, able to quickly acquire the X-ray intensity signal backscattered by the specimen. An effective signal processing method was developed to accurately extract the characteristic value of X-ray intensity signals representing the center of the joint. Secondly, an analytical model was established to calculate and optimize the detection parameters required for detection of the weld position of a given specimen by analyzing the relationship between the backscattered X-ray intensity signal detected by the detector and the parameters of the detection system and specimen during the detection process. Finally, several experiments were carried out on a 6061 aluminum alloy specimen with a thickness of 3 mm. The experimental results demonstrate that the maximum absolute error of the detection was 0.340 mm, which is sufficiently accurate for locating the position of the joint. This paper aims to provide the technical basis for the automatic tracking of weld joints from the face panel side, required for the high-reliability manufacturing of curved sandwich structures. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation
Sensors 2019, 19(14), 3197; https://doi.org/10.3390/s19143197
Received: 31 May 2019 / Revised: 12 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
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Abstract
Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect [...] Read more.
Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect measurement accuracy. This paper developed a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal contextual aspects. To validate the feasibility, stability, and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination changes and fog interference were simulated experimentally in the laboratory. The results of the proposed method were compared to conventional displacement sensor data and current vision-based method results. It was demonstrated that the proposed method gave better measurement results than the current ones under illumination change and fog interference. Full article
(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)
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Open AccessArticle
Design of a Purely Mechanical Sensor-Controller Integrated System for Walking Assistance on an Ankle-Foot Exoskeleton
Sensors 2019, 19(14), 3196; https://doi.org/10.3390/s19143196
Received: 5 June 2019 / Revised: 14 July 2019 / Accepted: 16 July 2019 / Published: 19 July 2019
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Abstract
Propulsion during push-off (PO) is a key factor to realize human locomotion. Through the detection of real-time gait stage, assistance could be provided to the human body at the proper time. In most cases, ankle-foot exoskeletons consist of electronic sensors, microprocessors, and actuators. [...] Read more.
Propulsion during push-off (PO) is a key factor to realize human locomotion. Through the detection of real-time gait stage, assistance could be provided to the human body at the proper time. In most cases, ankle-foot exoskeletons consist of electronic sensors, microprocessors, and actuators. Although these three essential elements contribute to fulfilling the function of the detection, control, and energy injection, they result in a huge system that reduces the wearing comfort. To simplify the sensor-controller system and reduce the mass of the exoskeleton, we designed a smart clutch in this paper, which is a sensor-controller integrated system that comprises a sensing part and an executing part. With a spring functioning as an actuator, the whole exoskeleton system is completely made up of mechanical parts and has no external power source. By controlling the engagement of the actuator based on the signal acquired from the sensing part, the proposed clutch enables the ankle-foot exoskeleton (AFE) to provide additional ankle torque during PO, and allows free rotation of the ankle joint during swing phase, thus reducing the metabolic cost of the human body. There are two striking advantages of the designed clutch. On the one hand, the clutch is lightweight and reliable—it resists the possible shock during walking since there is no circuit connection or power in the system. On the other hand, the detection of gait relies on the contact states between human feet and the ground, so the clutch is universal and does not need to be customized for individuals. Full article
(This article belongs to the Special Issue Smart Sensors and Devices in Artificial Intelligence)
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Open AccessArticle
Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network
Sensors 2019, 19(14), 3195; https://doi.org/10.3390/s19143195
Received: 18 June 2019 / Revised: 16 July 2019 / Accepted: 16 July 2019 / Published: 19 July 2019
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Abstract
Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional [...] Read more.
Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices. Full article
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Open AccessArticle
A Resource Allocation Mechanism Based on Weighted Efficiency Interference-Aware for D2D Underlaid Communication
Sensors 2019, 19(14), 3194; https://doi.org/10.3390/s19143194
Received: 21 May 2019 / Revised: 10 July 2019 / Accepted: 16 July 2019 / Published: 19 July 2019
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Abstract
Device-to-device (D2D) communication is a promising technique for direct communication to enhance the performance of cellular networks. In order to improve the system throughput and utilization of spectrum resource, a resource allocation mechanism for D2D underlaid communication is proposed in this paper where [...] Read more.
Device-to-device (D2D) communication is a promising technique for direct communication to enhance the performance of cellular networks. In order to improve the system throughput and utilization of spectrum resource, a resource allocation mechanism for D2D underlaid communication is proposed in this paper where D2D pairs reuse the resource blocks (RBs) of cellular uplink users, adopting a matching matrix to disclose the results of resource allocation. Details of the proposed resource allocation mechanism focused are listed as: the transmit power of D2D pairs are determined by themselves with the distributed power control method, and D2D pairs are assigned to different clusters that are the intended user sets of RBs, according to the threshold of the signal-to-interference-plus-noise ratio (SINR). The weighted efficiency interference-aware (WE-I-A) algorithm is proposed and applied subsequently to promote the system throughput by optimizing the matching of D2D pairs and RBs, where each D2D pair is weighted based on the SINR to compete for the priority of RBs fairly. Simulation results demonstrate that the proposed algorithm contributes to a good performance on the system throughput even if the uplink state is limited. Full article
(This article belongs to the Section Sensor Networks)
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