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Sensors, Volume 18, Issue 5 (May 2018)

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Cover Story (view full-size image) The miniature sensor integrates a printed slot antenna, a low-noise amplifier and an active mixer [...] Read more.
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Open AccessArticle Development of PZT Actuated Valveless Micropump
Sensors 2018, 18(5), 1302; https://doi.org/10.3390/s18051302
Received: 26 February 2018 / Revised: 20 March 2018 / Accepted: 5 April 2018 / Published: 24 April 2018
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Abstract
A piezoelectrically actuated valveless micropump has been designed and developed. The principle components of this system are piezoelectrically actuated (PZT) metal diaphragms and a complete fluid flow system. The design of this pump mainly focuses on a cross junction, which is generated by
[...] Read more.
A piezoelectrically actuated valveless micropump has been designed and developed. The principle components of this system are piezoelectrically actuated (PZT) metal diaphragms and a complete fluid flow system. The design of this pump mainly focuses on a cross junction, which is generated by a nozzle jet attached to a pump chamber and the intersection of two inlet channels and an outlet channel respectively. During each PZT diaphragm vibration cycle, the junction connecting the inlet and outlet channels with the nozzle jet permits consistencies in fluidic momentum and resistances in order to facilitate complete fluidic path throughout the system, in the absence of any physical valves. The entire micropump structure is fabricated as a plate-by-plate element of polymethyl methacrylate (PMMA) sheets and sandwiched to get required fluidic network as well as the overall device. In order to identify the flow characteristics, and to validate the test results with numerical simulation data, FEM analysis using ANSYS was carried out and an eigenfrequency analysis was performed to the PZT diaphragm using COMSOL Multiphysics. In addition, the control system of the pump was designed and developed to change the applied frequency to the piezoelectric diaphragms. The experimental data revealed that the maximum flow rate is 31.15 mL/min at a frequency of 100 Hz. Our proposed design is not only for a specific application but also useful in a wide range of biomedical applications. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Improved Fast Self-Calibration Method for Hybrid Inertial Navigation System under Stationary Condition
Sensors 2018, 18(5), 1303; https://doi.org/10.3390/s18051303
Received: 9 March 2018 / Revised: 20 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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Abstract
The navigation accuracy of the inertial navigation system (INS) can be greatly improved when the inertial measurement unit (IMU) is effectively calibrated and compensated, such as gyro drifts and accelerometer biases. To reduce the requirement for turntable precision in the classical calibration method,
[...] Read more.
The navigation accuracy of the inertial navigation system (INS) can be greatly improved when the inertial measurement unit (IMU) is effectively calibrated and compensated, such as gyro drifts and accelerometer biases. To reduce the requirement for turntable precision in the classical calibration method, a continuous dynamic self-calibration method based on a three-axis rotating frame for the hybrid inertial navigation system is presented. First, by selecting a suitable IMU frame, the error models of accelerometers and gyros are established. Then, by taking the navigation errors during rolling as the observations, the overall twenty-one error parameters of hybrid inertial navigation system (HINS) are identified based on the calculation of the intermediate parameter. The actual experiment verifies that the method can identify all error parameters of HINS and this method has equivalent accuracy to the classical calibration on a high-precision turntable. In addition, this method is rapid, simple and feasible. Full article
(This article belongs to the Special Issue Inertial Sensors and Systems 2018)
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Open AccessArticle Displacement and Strain Measurement up to 1000 °C Using a Hollow Coaxial Cable Fabry-Perot Resonator
Sensors 2018, 18(5), 1304; https://doi.org/10.3390/s18051304
Received: 28 March 2018 / Revised: 19 April 2018 / Accepted: 19 April 2018 / Published: 24 April 2018
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Abstract
We present a hollow coaxial cable Fabry-Perot resonator for displacement and strain measurement up to 1000 °C. By employing a novel homemade hollow coaxial cable made of stainless steel as a sensing platform, the high-temperature tolerance of the sensor is dramatically improved. A
[...] Read more.
We present a hollow coaxial cable Fabry-Perot resonator for displacement and strain measurement up to 1000 °C. By employing a novel homemade hollow coaxial cable made of stainless steel as a sensing platform, the high-temperature tolerance of the sensor is dramatically improved. A Fabry-Perot resonator is implemented on this hollow coaxial cable by introducing two highly-reflective reflectors along the cable. Based on a nested structure design, the external displacement and strain can be directly correlated to the cavity length of the resonator. By tracking the shift of the amplitude reflection spectrum of the microwave resonator, the applied displacement and strain can be determined. The displacement measurement experiment showed that the sensor could function properly up to 1000 °C. The sensor was also employed to measure the thermal strain of a steel plate during the heating process. The stability of the novel sensor was also investigated. The developed sensing platform and sensing configurations are robust, cost-effective, easy to manufacture, and can be flexibly designed for many other measurement applications in harsh high-temperature environments. Full article
(This article belongs to the Special Issue Resonator Sensors 2018)
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Open AccessArticle Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
Sensors 2018, 18(5), 1305; https://doi.org/10.3390/s18051305
Received: 18 March 2018 / Revised: 17 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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Abstract
Location information is one of the most vital information required to achieve intelligent and context-aware capability for various applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on using Global Navigation Satellite Systems (GNSS)
[...] Read more.
Location information is one of the most vital information required to achieve intelligent and context-aware capability for various applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on using Global Navigation Satellite Systems (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions, yet signal spoofing for illegal or covert transportation and misleading receiver timing is increasing and now frequent. Hence, detection and mitigation of spoofing attacks has become an important topic. Several contributions on spoofing detection have been made, focusing on different layers of a GNSS receiver. This paper focuses on spoofing detection utilizing self-contained sensors, namely inertial measurement units (IMUs) and vehicle odometer outputs. A spoofing detection approach based on a consistency check between GNSS and IMU/odometer mechanization is proposed. To detect a spoofing attack, the method analyses GNSS and IMU/odometer measurements independently during a pre-selected observation window and cross checks the solutions provided by GNSS and inertial navigation solution (INS)/odometer mechanization. The performance of the proposed method is verified in real vehicular environments. Mean spoofing detection time and detection performance in terms of receiver operation characteristics (ROC) in sub-urban and dense urban environments are evaluated. Full article
(This article belongs to the Special Issue GNSS and Fusion with Other Sensors)
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Open AccessArticle Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography
Sensors 2018, 18(5), 1306; https://doi.org/10.3390/s18051306
Received: 25 March 2018 / Revised: 13 April 2018 / Accepted: 18 April 2018 / Published: 24 April 2018
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Abstract
Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether
[...] Read more.
Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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Open AccessArticle Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound
Sensors 2018, 18(5), 1308; https://doi.org/10.3390/s18051308
Received: 2 March 2018 / Revised: 17 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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Abstract
Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too
[...] Read more.
Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Towards an Electrochemical Immunosensor System with Temperature Control for Cytokine Detection
Sensors 2018, 18(5), 1309; https://doi.org/10.3390/s18051309
Received: 12 March 2018 / Revised: 12 April 2018 / Accepted: 16 April 2018 / Published: 24 April 2018
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Abstract
The cytokine interleukin-13 (IL-13) plays a major role in airway inflammation and is a target of new anti-asthmatic drugs. Hence, IL-13 determination could be interesting in assessing therapy success. Thus, in this work an electrochemical immunosensor for IL-13 was developed and integrated into
[...] Read more.
The cytokine interleukin-13 (IL-13) plays a major role in airway inflammation and is a target of new anti-asthmatic drugs. Hence, IL-13 determination could be interesting in assessing therapy success. Thus, in this work an electrochemical immunosensor for IL-13 was developed and integrated into a fluidic system with temperature control for read-out. Therefore, two sets of results are presented. First, the sensor was set up in sandwich format on single-walled carbon nanotube electrodes and was read out by applying the hydrogen peroxide–hydroquinone–horseradish peroxidase (HRP) system. Second, a fluidic system was built up with an integrated heating function realized by Peltier elements that allowed a temperature-controlled read-out of the immunosensor in order to study the influence of temperature on the amperometric read-out. The sensor was characterized at the temperature optimum of HRP at 30 °C and at 12 °C as a reference for lower performance. These results were compared to a measurement without temperature control. At the optimum operation temperature of 30 °C, the highest sensitivity (slope) was obtained compared to lower temperatures and a limit of detection of 5.4 ng/mL of IL-13 was calculated. Taken together, this approach is a first step towards an automated electrochemical immunosensor platform and shows the potential of a temperature-controlled read-out. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Stand-Off Detection of Alcohol Vapors Exhaled by Humans
Sensors 2018, 18(5), 1310; https://doi.org/10.3390/s18051310
Received: 6 March 2018 / Revised: 11 April 2018 / Accepted: 19 April 2018 / Published: 24 April 2018
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Abstract
Early detection of humans under the influence of alcohol in public places (workplace, public gathering) is particularly important for safety reasons. In this article, the theoretical analysis of stand-off detection of alcohol in the air exhaled by humans as well as experimental results
[...] Read more.
Early detection of humans under the influence of alcohol in public places (workplace, public gathering) is particularly important for safety reasons. In this article, the theoretical analysis of stand-off detection of alcohol in the air exhaled by humans as well as experimental results of the developed experimental setup is presented. The concept of differential absorption of two laser beams at different wavelengths was used. The idea of using standard deviation of the relative difference of the amplitudes of two signals to detect the alcohol was applied for the first time. The idea was verified by the experiments and it was shown that a reliable device can be developed that can efficiently detect alcohol concentration in the exhaled air at the level of 0.3 mg/L (0.63‰). Moreover, the concept of such device examining humans entering a specific area was proposed. The results of this article may be useful to scientists or engineers working on alcohol detection in human blood. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data
Sensors 2018, 18(5), 1311; https://doi.org/10.3390/s18051311
Received: 28 January 2018 / Revised: 3 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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Abstract
This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a
[...] Read more.
This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients. Full article
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Open AccessArticle Infrastructure for Integration of Legacy Electrical Equipment into a Smart-Grid Using Wireless Sensor Networks
Sensors 2018, 18(5), 1312; https://doi.org/10.3390/s18051312
Received: 25 February 2018 / Revised: 8 April 2018 / Accepted: 17 April 2018 / Published: 24 April 2018
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Abstract
At present, the standardisation of electrical equipment communications is on the rise. In particular, manufacturers are releasing equipment for the smart grid endowed with communication protocols such as DNP3, IEC 61850, and MODBUS. However, there are legacy equipment operating in the electricity distribution
[...] Read more.
At present, the standardisation of electrical equipment communications is on the rise. In particular, manufacturers are releasing equipment for the smart grid endowed with communication protocols such as DNP3, IEC 61850, and MODBUS. However, there are legacy equipment operating in the electricity distribution network that cannot communicate using any of these protocols. Thus, we propose an infrastructure to allow the integration of legacy electrical equipment to smart grids by using wireless sensor networks (WSNs). In this infrastructure, each legacy electrical device is connected to a sensor node, and the sink node runs a middleware that enables the integration of this device into a smart grid based on suitable communication protocols. This middleware performs tasks such as the translation of messages between the power substation control centre (PSCC) and electrical equipment in the smart grid. Moreover, the infrastructure satisfies certain requirements for communication between the electrical equipment and the PSCC, such as enhanced security, short response time, and automatic configuration. The paper’s contributions include a solution that enables electrical companies to integrate their legacy equipment into smart-grid networks relying on any of the above mentioned communication protocols. This integration will reduce the costs related to the modernisation of power substations. Full article
(This article belongs to the Special Issue Smart Communication Protocols and Algorithms for Sensor Networks)
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Open AccessArticle Design and Performance of a 1 ms High-Speed Vision Chip with 3D-Stacked 140 GOPS Column-Parallel PEs
Sensors 2018, 18(5), 1313; https://doi.org/10.3390/s18051313
Received: 19 December 2017 / Revised: 15 March 2018 / Accepted: 9 April 2018 / Published: 24 April 2018
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Abstract
We have developed a high-speed vision chip using 3D stacking technology to address the increasing demand for high-speed vision chips in diverse applications. The chip comprises a 1/3.2-inch, 1.27 Mpixel, 500 fps (0.31 Mpixel, 1000 fps, 2 × 2 binning) vision chip with
[...] Read more.
We have developed a high-speed vision chip using 3D stacking technology to address the increasing demand for high-speed vision chips in diverse applications. The chip comprises a 1/3.2-inch, 1.27 Mpixel, 500 fps (0.31 Mpixel, 1000 fps, 2 × 2 binning) vision chip with 3D-stacked column-parallel Analog-to-Digital Converters (ADCs) and 140 Giga Operation per Second (GOPS) programmable Single Instruction Multiple Data (SIMD) column-parallel PEs for new sensing applications. The 3D-stacked structure and column parallel processing architecture achieve high sensitivity, high resolution, and high-accuracy object positioning. Full article
(This article belongs to the Special Issue Special Issue on the 2017 International Image Sensor Workshop (IISW))
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Open AccessArticle High Sensitive pH Sensor Based on AlInN/GaN Heterostructure Transistor
Sensors 2018, 18(5), 1314; https://doi.org/10.3390/s18051314
Received: 4 March 2018 / Revised: 12 April 2018 / Accepted: 21 April 2018 / Published: 24 April 2018
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Abstract
The AlInN/GaN high-electron-mobility-transistor (HEMT) indicates better performances compared with the traditional AlGaN/GaN HEMTs. The present work investigated the pH sensor functionality of an analogous HEMT AlInN/GaN device with an open gate. It was shown that the Al0.83In0.17N/GaN device demonstrates
[...] Read more.
The AlInN/GaN high-electron-mobility-transistor (HEMT) indicates better performances compared with the traditional AlGaN/GaN HEMTs. The present work investigated the pH sensor functionality of an analogous HEMT AlInN/GaN device with an open gate. It was shown that the Al0.83In0.17N/GaN device demonstrates excellent pH sense functionality in aqueous solutions, exhibiting higher sensitivity (−30.83 μA/pH for AlInN/GaN and −4.6 μA/pH for AlGaN/GaN) and a faster response time, lower degradation and good stability with respect to the AlGaN/GaN device, which is attributed to higher two-dimensional electron gas (2DEG) density and a thinner barrier layer in Al0.83In0.17N/GaN owning to lattice matching. On the other hand, the open gate geometry was found to affect the pH sensitivity obviously. Properly increasing the width and shortening the length of the open gate area could enhance the sensitivity. However, when the open gate width is too larger or too small, the pH sensitivity would be suppressed conversely. Designing an optimal ratio of the width to the length is important for achieving high sensitivity. This work suggests that the AlInN/GaN-based 2DEG carrier modulated devices would be good candidates for high-performance pH sensors and other related applications. Full article
(This article belongs to the Special Issue Potentiometric Chemical Sensors)
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Open AccessArticle Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor
Sensors 2018, 18(5), 1315; https://doi.org/10.3390/s18051315
Received: 28 March 2018 / Revised: 20 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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Abstract
Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by
[...] Read more.
Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. Full article
(This article belongs to the Special Issue Visual Sensors)
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Open AccessArticle Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study
Sensors 2018, 18(5), 1316; https://doi.org/10.3390/s18051316
Received: 15 March 2018 / Revised: 11 April 2018 / Accepted: 16 April 2018 / Published: 24 April 2018
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Abstract
Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors,
[...] Read more.
Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy. Full article
(This article belongs to the Special Issue Smart Vehicular Mobile Sensing)
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Open AccessArticle Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator
Sensors 2018, 18(5), 1317; https://doi.org/10.3390/s18051317
Received: 30 January 2018 / Revised: 19 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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Abstract
Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG)
[...] Read more.
Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG) systems aim to provide clean and reliable electrical power. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN) is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT) generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances. Full article
(This article belongs to the Special Issue New Trends in Ambient Intelligence Applications)
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Open AccessArticle Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model
Sensors 2018, 18(5), 1318; https://doi.org/10.3390/s18051318
Received: 30 March 2018 / Revised: 19 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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Abstract
This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a
[...] Read more.
This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Label-Free Impedance Sensing of Aflatoxin B1 with Polyaniline Nanofibers/Au Nanoparticle Electrode Array
Sensors 2018, 18(5), 1320; https://doi.org/10.3390/s18051320
Received: 5 April 2018 / Revised: 21 April 2018 / Accepted: 21 April 2018 / Published: 24 April 2018
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Abstract
Aflatoxin B1 (AFB1) is produced by the Aspergillus flavus and Aspergillus parasiticus group of fungi which is most hepatotoxic and hepatocarcinogenic and occurs as a contaminant in a variety of foods. AFB1 is mutagenic, teratogenic, and causes immunosuppression in
[...] Read more.
Aflatoxin B1 (AFB1) is produced by the Aspergillus flavus and Aspergillus parasiticus group of fungi which is most hepatotoxic and hepatocarcinogenic and occurs as a contaminant in a variety of foods. AFB1 is mutagenic, teratogenic, and causes immunosuppression in animals and is mostly found in peanuts, corn, and food grains. Therefore, novel methodologies of sensitive and expedient strategy are often required to detect mycotoxins at the lowest level. Herein, we report an electrochemical impedance sensor that selectively detects AFB1 at the lowest level by utilizing polyaniline nanofibers (PANI) coated with gold (Au) nanoparticles composite based indium tin oxide (ITO) disk electrodes. The Au-PANI nanocomposites were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) spectroscopy, and electrochemical impedance spectroscopy (EIS). The composite electrode exhibited a 14-fold decrement in |Z|1 Hz in comparison with the bare electrode. The Au-PANI acted as an effective sensing platform having high surface area, electrochemical conductivity, and biocompatibility which enabled greater loading deposits of capture antibodies. As a result, the presence of AFB1 was screened with high sensitivity and stability by monitoring the changes in impedance magnitude (|Z|) in the presence of a standard iron probe which was target specific and proportional to logarithmic AFB1 concentrations (CAFB1). The sensor exhibits a linear range 0.1 to 100 ng/mL with a detection limit (3σ) of 0.05 ng/mL and possesses good reproducibility and high selectivity against another fungal mycotoxin, Ochratoxin A (OTA). With regard to the practicability, the proposed sensor was successfully applied to spiked corn samples and proved excellent potential for AFB1 detection and development of point-of-care (POC) disease sensing applications. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Prediction of Fatigue Crack Growth in Gas Turbine Engine Blades Using Acoustic Emission
Sensors 2018, 18(5), 1321; https://doi.org/10.3390/s18051321
Received: 1 March 2018 / Revised: 29 March 2018 / Accepted: 19 April 2018 / Published: 25 April 2018
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Abstract
Fatigue failure is the main type of failure that occurs in gas turbine engine blades and an online monitoring method for detecting fatigue cracks in blades is urgently needed. Therefore, in this present study, we propose the use of acoustic emission (AE) monitoring
[...] Read more.
Fatigue failure is the main type of failure that occurs in gas turbine engine blades and an online monitoring method for detecting fatigue cracks in blades is urgently needed. Therefore, in this present study, we propose the use of acoustic emission (AE) monitoring for the online identification of the blade status. Experiments on fatigue crack propagation based on the AE monitoring of gas turbine engine blades and TC11 titanium alloy plates were conducted. The relationship between the cumulative AE hits and the fatigue crack length was established, before a method of using the AE parameters to determine the crack propagation stage was proposed. A method for predicting the degree of crack propagation and residual fatigue life based on the AE energy was obtained. The results provide a new method for the online monitoring of cracks in the gas turbine engine blade. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Novel Approach to the Identification of Compromised Pulmonary Systems in Smokers by Exploiting Tidal Breathing Patterns
Sensors 2018, 18(5), 1322; https://doi.org/10.3390/s18051322
Received: 28 February 2018 / Revised: 23 March 2018 / Accepted: 26 March 2018 / Published: 25 April 2018
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Abstract
Smoking causes unalterable physiological abnormalities in the pulmonary system. This is emerging as a serious threat worldwide. Unlike spirometry, tidal breathing does not require subjects to undergo forceful breathing maneuvers and is progressing as a new direction towards pulmonary health assessment. The aim
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Smoking causes unalterable physiological abnormalities in the pulmonary system. This is emerging as a serious threat worldwide. Unlike spirometry, tidal breathing does not require subjects to undergo forceful breathing maneuvers and is progressing as a new direction towards pulmonary health assessment. The aim of the paper is to evaluate whether tidal breathing signatures can indicate deteriorating adult lung condition in an otherwise healthy person. If successful, such a system can be used as a pre-screening tool for all people before some of them need to undergo a thorough clinical checkup. This work presents a novel systematic approach to identify compromised pulmonary systems in smokers from acquired tidal breathing patterns. Tidal breathing patterns are acquired during restful breathing of adult participants. Thereafter, physiological attributes are extracted from the acquired tidal breathing signals. Finally, a unique classification approach of locally weighted learning with ridge regression (LWL-ridge) is implemented, which handles the subjective variations in tidal breathing data without performing feature normalization. The LWL-ridge classifier recognized compromised pulmonary systems in smokers with an average classification accuracy of 86.17% along with a sensitivity of 80% and a specificity of 92%. The implemented approach outperformed other variants of LWL as well as other standard classifiers and generated comparable results when applied on an external cohort. This end-to-end automated system is suitable for pre-screening people routinely for early detection of lung ailments as a preventive measure in an infrastructure-agnostic way. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Optical and Acoustic Sensor-Based 3D Ball Motion Estimation for Ball Sport Simulators †
Sensors 2018, 18(5), 1323; https://doi.org/10.3390/s18051323
Received: 16 March 2018 / Revised: 18 April 2018 / Accepted: 24 April 2018 / Published: 25 April 2018
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Abstract
Estimation of the motion of ball-shaped objects is essential for the operation of ball sport simulators. In this paper, we propose an estimation system for 3D ball motion, including speed and angle of projection, by using acoustic vector and infrared (IR) scanning sensors.
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Estimation of the motion of ball-shaped objects is essential for the operation of ball sport simulators. In this paper, we propose an estimation system for 3D ball motion, including speed and angle of projection, by using acoustic vector and infrared (IR) scanning sensors. Our system is comprised of three steps to estimate a ball motion: sound-based ball firing detection, sound source localization, and IR scanning for motion analysis. First, an impulsive sound classification based on the mel-frequency cepstrum and feed-forward neural network is introduced to detect the ball launch sound. An impulsive sound source localization using a 2D microelectromechanical system (MEMS) microphones and delay-and-sum beamforming is presented to estimate the firing position. The time and position of a ball in 3D space is determined from a high-speed infrared scanning method. Our experimental results demonstrate that the estimation of ball motion based on sound allows a wider activity area than similar camera-based methods. Thus, it can be practically applied to various simulations in sports such as soccer and baseball. Full article
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Open AccessArticle Theoretical Design of a Two-Photon Fluorescent Probe for Nitric Oxide with Enhanced Emission Induced by Photoninduced Electron Transfer
Sensors 2018, 18(5), 1324; https://doi.org/10.3390/s18051324
Received: 3 March 2018 / Revised: 6 April 2018 / Accepted: 11 April 2018 / Published: 25 April 2018
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Abstract
In the present work, we systematically investigate the sensing abilities of two recently literature-reported two-photon fluorescent NO probes, i.e., the o-phenylenediamine derivative of Nile Red and the p-phenylenediamine derivative of coumarin. The recognition mechanisms of these probes are studied by using the molecular
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In the present work, we systematically investigate the sensing abilities of two recently literature-reported two-photon fluorescent NO probes, i.e., the o-phenylenediamine derivative of Nile Red and the p-phenylenediamine derivative of coumarin. The recognition mechanisms of these probes are studied by using the molecular orbital classifying method, which demonstrates the photoinduced electron transfer process. In addition, we have designed two new probes by swapping receptor units present on fluorophores, i.e., the p-phenylenediamine derivative of Nile Red and the o-phenylenediamine derivative of coumarin. However, it illustrates that only the latter has ability to function as off-on typed fluorescent probe for NO. More importantly, calculations on the two-photon absorption properties of the probes demonstrate that both receptor derivatives of coumarin possess larger TPA cross-sections than Nile Red derivatives, which makes a better two photon fluorescent probe. Our theoretical investigations reveal that the underlying mechanism satisfactorily explain the experimental results, providing a theoretical basis on the structure-property relationships which is beneficial to developing new two-photon fluorescent probes for NO. Full article
(This article belongs to the Special Issue Novel Sensors for Bioimaging)
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Open AccessArticle Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis
Sensors 2018, 18(5), 1325; https://doi.org/10.3390/s18051325
Received: 24 March 2018 / Revised: 18 April 2018 / Accepted: 21 April 2018 / Published: 25 April 2018
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Abstract
Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency
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Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Open AccessArticle A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
Sensors 2018, 18(5), 1326; https://doi.org/10.3390/s18051326
Received: 16 February 2018 / Revised: 20 April 2018 / Accepted: 23 April 2018 / Published: 25 April 2018
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Abstract
Signal modulation is an essential design factor of a positioning system, which directly impacts the system’s potential performance. Chirp compressions have been widely applied in the fields of communication, radar, and indoor positioning owing to their high compression gain and good resistance to
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Signal modulation is an essential design factor of a positioning system, which directly impacts the system’s potential performance. Chirp compressions have been widely applied in the fields of communication, radar, and indoor positioning owing to their high compression gain and good resistance to narrowband interferences and multipath fading. Based on linear chirp, we present a modulation method named chirped pseudo-noise (ChPN). The mathematical model of the ChPN signal is provided with its auto-correlation function (ACF) and the power spectrum density (PSD) derived. The ChPN with orthogonal chirps is also discussed, which has better resistance to near-far effect. Then the generation and detection methods as well as the performances of ChPN are discussed by theoretical analysis and simulation. The results show that, for ChPN signals with the same main-lobe bandwidth (MLB), generally, the signal with a larger sweep bandwidth has better tracking precision and multipath resistance. ChPN yields slighter ACF peaks ambiguity due to its lower ACF side-peaks, although its tracking precision is a little worse than that of a binary offset carrier (BOC) with the same MLB. Moreover, ChPN provides better overall anti-multipath performance than BOC. For the ChPN signals with the same code rate, a signal with a larger sweep bandwidth has better performance in most aspects. In engineering practice, a ChPN receiver can be implemented by minor modifications of a BOC receiver. Thus, ChPN modulation shows promise for future positioning applications. Full article
(This article belongs to the Special Issue GNSS and Fusion with Other Sensors)
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Open AccessArticle Ultrasound Pulse-Echo Coupled with a Tracking Technique for Simultaneous Measurement of Multiple Bubbles
Sensors 2018, 18(5), 1327; https://doi.org/10.3390/s18051327
Received: 20 March 2018 / Revised: 12 April 2018 / Accepted: 21 April 2018 / Published: 25 April 2018
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Abstract
Bubbly flows are commonly used in various applications and their measurement is an important research topic. The ultrasound pulse-echo technique allows for the detection of each bubble and the measurement of the position of its surface. However, so far it has been used
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Bubbly flows are commonly used in various applications and their measurement is an important research topic. The ultrasound pulse-echo technique allows for the detection of each bubble and the measurement of the position of its surface. However, so far it has been used only to measure single bubbles. This paper investigates whether the pulse-echo technique can be applied for measuring multiple bubbles concurrently. The ultrasonic transducer wavelength and diameter were selected based on expected bubble diameters so that each bubble produced a strong reflection. The pulse-echo was implemented to obtain good accuracy without sacrificing the signal processing speed. A tracking technique was developed for the purpose of connecting detected reflections to trajectories. The technique was tested experimentally by measuring the horizontal position of rising air bubbles in a water tank. The results show that the pulse-echo technique can detect multiple bubbles concurrently. The pulse-echo technique detected almost the same number of bubbles as a high-speed video. For average void fractions up to around 1 % (and instantaneous void fraction reaching 5.3 % ), the rate of bubbles missed by the pulse-echo and the rate of noise trajectories both stayed less than 5%. The error rate increased with the void fraction, limiting the technique’s application range. Full article
(This article belongs to the Special Issue Ultrasonic Sensors 2018)
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Open AccessArticle On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery
Sensors 2018, 18(5), 1328; https://doi.org/10.3390/s18051328
Received: 28 March 2018 / Revised: 17 April 2018 / Accepted: 18 April 2018 / Published: 25 April 2018
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Abstract
With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data
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With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments
Sensors 2018, 18(5), 1329; https://doi.org/10.3390/s18051329
Received: 21 March 2018 / Revised: 18 April 2018 / Accepted: 21 April 2018 / Published: 25 April 2018
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Abstract
Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching
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Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments. Full article
(This article belongs to the Special Issue Visual Sensors)
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Open AccessArticle Rapid, High Affinity Binding by a Fluorescein Templated Copolymer Combining Covalent, Hydrophobic, and Acid–Base Noncovalent Crosslinks
Sensors 2018, 18(5), 1330; https://doi.org/10.3390/s18051330
Received: 8 February 2018 / Revised: 19 April 2018 / Accepted: 19 April 2018 / Published: 25 April 2018
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Abstract
A new type of biomimetic templated copolymer has been prepared by reverse addition fragmentation chain transfer polymerization (RAFT) in dioxane. The initial formulation includes the template fluorescein, N-isopropylacrylamide (NIPAM, 84 mol %), methacrylic acid (MAA, 5-mol %), 4-vinylpyridine (4-VP, 9 mmol %),
[...] Read more.
A new type of biomimetic templated copolymer has been prepared by reverse addition fragmentation chain transfer polymerization (RAFT) in dioxane. The initial formulation includes the template fluorescein, N-isopropylacrylamide (NIPAM, 84 mol %), methacrylic acid (MAA, 5-mol %), 4-vinylpyridine (4-VP, 9 mmol %), and N,N′-methylenebis(acrylamide) (MBA, 2 mol %). PolyNIPAM is a thermosensitive polymer that comes out of aqueous solution above its lower critical solution temperature forming hydrophobic ‘crosslinks’. MAA and 4-VP interact in dioxane forming acid–base crosslinks. The excess 4-VP serves as a recognition monomer organizing around the template fluorescein to form a binding site that is held in place by the noncovalent and covalent crosslinks. The MBA is a covalent crosslinker. The RAFT agent in the resulting copolylmer was reduced to a thiol and attached to gold nanoparticles. The gold nanoparticle bound copolymer binds fluorescein completely in less than two seconds with an affinity constant greater than 108 M−1. A reference copolymer prepared with the same monomers by the same procedure binds fluorescein much more weakly. Full article
(This article belongs to the Special Issue Polymer-Based Sensors for Bioanalytes)
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Open AccessArticle Feasibility of Optical Coherence Tomography (OCT) for Intra-Operative Detection of Blood Flow during Gastric Tube Reconstruction
Sensors 2018, 18(5), 1331; https://doi.org/10.3390/s18051331
Received: 15 March 2018 / Revised: 15 April 2018 / Accepted: 21 April 2018 / Published: 25 April 2018
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Abstract
In this study; an OCT-based intra-operative imaging method for blood flow detection during esophagectomy with gastric tube reconstruction is investigated. Change in perfusion of the gastric tube tissue can lead to ischemia; with a high morbidity and mortality as a result. Anastomotic leakage
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In this study; an OCT-based intra-operative imaging method for blood flow detection during esophagectomy with gastric tube reconstruction is investigated. Change in perfusion of the gastric tube tissue can lead to ischemia; with a high morbidity and mortality as a result. Anastomotic leakage (incidence 5–20%) is one of the most severe complications after esophagectomy with gastric tube reconstruction. Optical imaging techniques provide for minimal-invasive and real-time visualization tools that can be used in intraoperative settings. By implementing an optical technique for blood flow detection during surgery; perfusion can be imaged and quantified and; if needed; perfusion can be improved by either a surgical intervention or the administration of medication. The feasibility of imaging gastric microcirculation in vivo using optical coherence tomography (OCT) during surgery of patients with esophageal cancer by visualizing blood flow based on the speckle contrast from M-mode OCT images is studied. The percentage of pixels exhibiting a speckle contrast value indicative of flow was quantified to serve as an objective parameter to assess blood flow at 4 locations on the reconstructed gastric tube. Here; it was shown that OCT can be used for direct blood flow imaging during surgery and may therefore aid in improving surgical outcomes for patients. Full article
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Open AccessArticle The Application of an Ultrasound Tomography Algorithm in a Novel Ring 3D Ultrasound Imaging System
Sensors 2018, 18(5), 1332; https://doi.org/10.3390/s18051332
Received: 16 March 2018 / Revised: 15 April 2018 / Accepted: 19 April 2018 / Published: 25 April 2018
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Abstract
Currently, breast cancer is one of the most common cancers in women all over the world. A novel 3D breast ultrasound imaging ring system using the linear array transducer is proposed to decrease costs, reduce processing difficulties, and improve patient comfort as compared
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Currently, breast cancer is one of the most common cancers in women all over the world. A novel 3D breast ultrasound imaging ring system using the linear array transducer is proposed to decrease costs, reduce processing difficulties, and improve patient comfort as compared to modern day breast screening systems. The 1 × 128 Piezoelectric Micromachined Ultrasonic Transducer (PMUT) linear array is placed 90 degrees cross-vertically. The transducer surrounds the mammary gland, which allows for non-contact detection. Once the experimental platform is built, the breast model is placed through the electric rotary table opening and into a water tank that is at a constant temperature of 32 °C. The electric rotary table performs a 360° scan either automatically or mechanically. Pulse echo signals are captured through a circular scanning method at discrete angles. Subsequently, an ultrasonic tomography algorithm is designed, and a horizontal slice imaging is realized. The experimental results indicate that the preliminary detection of mass is realized by using this ring system. Circular scanning imaging is obtained by using a rotatable linear array instead of a cylindrical array, which allows the size and location of the mass to be recognized. The resolution of breast imaging is improved through the adjustment of the angle interval (>0.05°) and multiple slices are gained through different transducer array elements (1 × 128). These results validate the feasibility of the system design as well as the algorithm, and encourage us to implement our concept with a clinical study in the future. Full article
(This article belongs to the Special Issue Ultrasonic Sensors 2018)
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Open AccessArticle Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming
Sensors 2018, 18(5), 1333; https://doi.org/10.3390/s18051333
Received: 22 January 2018 / Revised: 16 April 2018 / Accepted: 19 April 2018 / Published: 25 April 2018
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Abstract
Improving the sustainability in agriculture is nowadays an important challenge. The automation of irrigation processes via low-cost sensors can to spread technological advances in a sector very influenced by economical costs. This article presents an auto-calibrated pH sensor able to detect and adjust
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Improving the sustainability in agriculture is nowadays an important challenge. The automation of irrigation processes via low-cost sensors can to spread technological advances in a sector very influenced by economical costs. This article presents an auto-calibrated pH sensor able to detect and adjust the imbalances in the pH levels of the nutrient solution used in hydroponic agriculture. The sensor is composed by a pH probe and a set of micropumps that sequentially pour the different liquid solutions to maintain the sensor calibration and the water samples from the channels that contain the nutrient solution. To implement our architecture, we use an auto-calibrated pH sensor connected to a wireless node. Several nodes compose our wireless sensor networks (WSN) to control our greenhouse. The sensors periodically measure the pH level of each hydroponic support and send the information to a data base (DB) which stores and analyzes the data to warn farmers about the measures. The data can then be accessed through a user-friendly, web-based interface that can be accessed through the Internet by using desktop or mobile devices. This paper also shows the design and test bench for both the auto-calibrated pH sensor and the wireless network to check their correct operation. Full article
(This article belongs to the Special Issue Smart Communication Protocols and Algorithms for Sensor Networks)
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Open AccessArticle Gait Shear and Plantar Pressure Monitoring: A Non-Invasive OFS Based Solution for e-Health Architectures
Sensors 2018, 18(5), 1334; https://doi.org/10.3390/s18051334
Received: 26 March 2018 / Revised: 12 April 2018 / Accepted: 20 April 2018 / Published: 25 April 2018
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Abstract
In an era of unprecedented progress in sensing technology and communication, health services are now able to closely monitor patients and elderly citizens without jeopardizing their daily routines through health applications on their mobile devices in what is known as e-Health. Within this
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In an era of unprecedented progress in sensing technology and communication, health services are now able to closely monitor patients and elderly citizens without jeopardizing their daily routines through health applications on their mobile devices in what is known as e-Health. Within this field, we propose an optical fiber sensor (OFS) based system for the simultaneous monitoring of shear and plantar pressure during gait movement. These parameters are considered to be two key factors in gait analysis that can help in the early diagnosis of multiple anomalies, such as diabetic foot ulcerations or in physical rehabilitation scenarios. The proposed solution is a biaxial OFS based on two in-line fiber Bragg gratings (FBGs), which were inscribed in the same optical fiber and placed individually in two adjacent cavities, forming a small sensing cell. Such design presents a more compact and resilient solution with higher accuracy when compared to the existing electronic systems. The implementation of the proposed elements into an insole is also described, showcasing the compactness of the sensing cells, which can easily be integrated into a non-invasive mobile e-Health solution for continuous remote gait monitoring of patients and elder citizens. The reported results show that the proposed system outperforms existing solutions, in the sense that it is able to dynamically discriminate shear and plantar pressure during gait. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle Exploring on the Sensitivity Changes of the LC Resonance Magnetic Sensors Affected by Superposed Ringing Signals
Sensors 2018, 18(5), 1335; https://doi.org/10.3390/s18051335
Received: 12 March 2018 / Revised: 13 April 2018 / Accepted: 20 April 2018 / Published: 25 April 2018
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Abstract
LC resonance magnetic sensors are widely used in low-field nuclear magnetic resonance (LF-NMR) and surface nuclear magnetic resonance (SNMR) due to their high sensitivity, low cost and simple design. In magnetically shielded rooms, LC resonance magnetic sensors can exhibit sensitivities at the fT/√Hz
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LC resonance magnetic sensors are widely used in low-field nuclear magnetic resonance (LF-NMR) and surface nuclear magnetic resonance (SNMR) due to their high sensitivity, low cost and simple design. In magnetically shielded rooms, LC resonance magnetic sensors can exhibit sensitivities at the fT/√Hz level in the kHz range. However, since the equivalent magnetic field noise of this type of sensor is greatly affected by the environment, weak signals are often submerged in practical applications, resulting in relatively low signal-to-noise ratios (SNRs). To determine why noise increases in unshielded environments, we analysed the noise levels of an LC resonance magnetic sensor (L ≠ 0) and a Hall sensor (L ≈ 0) in different environments. The experiments and simulations indicated that the superposed ringing of the LC resonance magnetic sensors led to the observed increase in white noise level caused by environmental interference. Nevertheless, ringing is an inherent characteristic of LC resonance magnetic sensors. It cannot be eliminated when environmental interference exists. In response to this problem, we proposed a method that uses matching resistors with various values to adjust the quality factor Q of the LC resonance magnetic sensor in different measurement environments to obtain the best sensitivity. The LF-NMR experiment in the laboratory showed that the SNR is improved significantly when the LC resonance magnetic sensor with the best sensitivity is selected for signal acquisition in the light of the test environment. (When the matching resistance is 10 kΩ, the SNR is 3.46 times that of 510 Ω). This study improves LC resonance magnetic sensors for nuclear magnetic resonance (NMR) detection in a variety of environments. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study
Sensors 2018, 18(5), 1336; https://doi.org/10.3390/s18051336
Received: 12 March 2018 / Revised: 18 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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Abstract
Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty
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Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty components are of key importance to enable targeted interventions and remediation. We hypothesize that sensor-derived parameters, measured by a pendant accelerometer device in the home setting, are sensitive to identifying pre-frailty. Methods: Using the Fried frailty phenotype criteria, 153 community-dwelling, ambulatory older adults were classified as pre-frail (51%), frail (22%), or non-frail (27%). A pendant sensor was used to monitor the at home physical activity, using a chest acceleration over 48 h. An algorithm was developed to quantify physical activity pattern (PAP), physical activity behavior (PAB), and sleep quality parameters. Statistically significant parameters were selected to discriminate the pre-frail from frail and non-frail adults. Results: The stepping parameters, walking parameters, PAB parameters (sedentary and moderate-to-vigorous activity), and the combined parameters reached and area under the curve of 0.87, 0.85, 0.85, and 0.88, respectively, for identifying pre-frail adults. No sleep parameters discriminated the pre-frail from the rest of the adults. Conclusions: This study demonstrates that a pendant sensor can identify pre-frailty via daily home monitoring. These findings may open new opportunities in order to remotely measure and track frailty via telehealth technologies. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle A Distance Bounding Protocol for Location-Cloaked Applications
Sensors 2018, 18(5), 1337; https://doi.org/10.3390/s18051337
Received: 14 March 2018 / Revised: 4 April 2018 / Accepted: 5 April 2018 / Published: 26 April 2018
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Abstract
Location-based services (LBSs) assume that users are willing to release trustworthy and useful details about their whereabouts. However, many location privacy concerns have arisen. For location privacy protection, several algorithms build a cloaking region to hide a user’s location. However, many applications may
[...] Read more.
Location-based services (LBSs) assume that users are willing to release trustworthy and useful details about their whereabouts. However, many location privacy concerns have arisen. For location privacy protection, several algorithms build a cloaking region to hide a user’s location. However, many applications may not operate adequately on cloaked locations. For example, a traditional distance bounding protocol (DBP)—which is run by two nodes called the prover and the verifier—may conclude an untight and useless distance between these two entities. An LBS (verifier) may use this distance as a metric of usefulness and trustworthiness of the location claimed by the user (prover). However, we show that if a tight distance is desired, traditional DBP can refine a user’s cloaked location and compromise its location privacy. To find a proper balance, we propose a location-privacy-aware DBP protocol. Our solution consists of adding some small delays before submitting any user’s response. We show that several issues arise when a certain delay is chosen, and we propose some solutions. The effectiveness of our techniques in balancing location refinement and utility is demonstrated through simulation. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Highly Stable and Flexible Pressure Sensors with Modified Multi-Walled Carbon Nanotube/Polymer Composites for Human Monitoring
Sensors 2018, 18(5), 1338; https://doi.org/10.3390/s18051338
Received: 3 March 2018 / Revised: 18 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
A facile method for preparing an easy processing, repeatable and flexible pressure sensor was presented via the synthesis of modified multi-walled carbon nanotubes (m-MWNTs) and polyurethane (PU) films. The surface modification of multi-walled carbon nanotubes (MWNTs) simultaneously used a silane coupling agent (KH550)
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A facile method for preparing an easy processing, repeatable and flexible pressure sensor was presented via the synthesis of modified multi-walled carbon nanotubes (m-MWNTs) and polyurethane (PU) films. The surface modification of multi-walled carbon nanotubes (MWNTs) simultaneously used a silane coupling agent (KH550) and sodium dodecyl benzene sulfonate (SDBS) to improve the dispersibility and compatibility of the MWNTs in a polymer matrix. The electrical property and piezoresistive behavior of the m-MWNT/PU composites were compared with raw multi-walled carbon nanotube (raw MWNT)/PU composites. Under linear uniaxial pressure, the m-MWNT/PU composite exhibited 4.282%kPa−1 sensitivity within the pressure of 1 kPa. The nonlinear error, hysteresis error and repeatability error of the piezoresistivity of m-MWNT/PU decreased 9%, 16.72% and 54.95% relative to raw MWNT/PU respectively. Therefore, the piezoresistive response of m-MWNT/PU had better stability than that of raw MWNT/PU composites. The m-MWNT/PU sensors could be utilized in wearable devices for body movement detection, monitoring of respiration and pressure detection in garments. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks
Sensors 2018, 18(5), 1339; https://doi.org/10.3390/s18051339
Received: 22 March 2018 / Revised: 18 April 2018 / Accepted: 19 April 2018 / Published: 26 April 2018
PDF Full-text (1276 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked
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Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked task environment, where a trained model is used to make workload estimates on a new participant who is not represented in the training set. Using experimental data from the Multi-Attribute Task Battery (MATB) environment, a variety of deep neural network models are evaluated in the trade-space of computational efficiency, model accuracy, variance and temporal specificity yielding three important contributions: (1) The performance of ensembles of individually-trained models is statistically indistinguishable from group-trained methods at most sequence lengths. These ensembles can be trained for a fraction of the computational cost compared to group-trained methods and enable simpler model updates. (2) While increasing temporal sequence length improves mean accuracy, it is not sufficient to overcome distributional dissimilarities between individuals’ EEG data, as it results in statistically significant increases in cross-participant variance. (3) Compared to all other networks evaluated, a novel convolutional-recurrent model using multi-path subnetworks and bi-directional, residual recurrent layers resulted in statistically significant increases in predictive accuracy and decreases in cross-participant variance. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Open AccessArticle Adaptive Monostatic System for Measuring Microwave Reflections from the Breast
Sensors 2018, 18(5), 1340; https://doi.org/10.3390/s18051340
Received: 30 March 2018 / Revised: 18 April 2018 / Accepted: 22 April 2018 / Published: 26 April 2018
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Abstract
A second-generation monostatic radar system to measure microwave reflections from the human breast is presented and analyzed. The present system can measure the outline of the breast with an accuracy of ±1 mm and precisely place the microwave sensor in an adaptive matter
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A second-generation monostatic radar system to measure microwave reflections from the human breast is presented and analyzed. The present system can measure the outline of the breast with an accuracy of ±1 mm and precisely place the microwave sensor in an adaptive matter such that microwaves are normally incident on the skin. Microwave reflections are measured between 10 MHz to 12 GHz with sensitivity of 65 to 75 dB below the input power and a total scan time of 30 min for 140 locations. The time domain reflections measured from a volunteer show fidelity above 0.98 for signals in a single scan. Finally, multiple scans of a breast phantoms demonstrate the consistency of the system in terms of recorded reflection, outline measurement, and image reconstruction. Full article
(This article belongs to the Special Issue Sensors for Microwave Imaging and Detection)
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Open AccessArticle Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City
Sensors 2018, 18(5), 1341; https://doi.org/10.3390/s18051341
Received: 27 March 2018 / Revised: 17 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
Smart city (SC) technologies can provide appropriate services according to citizens’ demands. One of the key enablers in a SC is the Internet of Things (IoT) technology, which enables a massive number of devices to connect with each other. However, these devices usually
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Smart city (SC) technologies can provide appropriate services according to citizens’ demands. One of the key enablers in a SC is the Internet of Things (IoT) technology, which enables a massive number of devices to connect with each other. However, these devices usually come from different manufacturers with different product standards, which confront interactive control problems. Moreover, these devices will produce large amounts of data, and efficiently analyzing these data for intelligent services. In this paper, we propose a novel artificial intelligence-based semantic IoT (AI-SIoT) hybrid service architecture to integrate heterogeneous IoT devices to support intelligent services. In particular, the proposed architecture is empowered by semantic and AI technologies, which enable flexible connections among heterogeneous devices. The AI technology can support very implement efficient data analysis and make accurate decisions on service provisions in various kinds. Furthermore, we also present several practical use cases of the proposed AI-SIoT architecture and the opportunities and challenges to implement the proposed AI-SIoT for future SCs are also discussed. Full article
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Open AccessArticle Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network
Sensors 2018, 18(5), 1342; https://doi.org/10.3390/s18051342
Received: 3 April 2018 / Revised: 22 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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Abstract
As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing
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As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing the 3D imaging area creates a heavy computational burden, thus leading to unsatisfactory efficiency. Secondly, it is difficult to resolve the target under low signal-to-noise ratio (SNR). In this paper, we propose a 3D imaging method based on matched filtering (MF) and convolutional neural network (CNN), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. In terms of the frequency-hopping (FH) signal, the original echo is processed with MF. By extracting the processed echo in different spike pulses separately, targets in different imaging planes are reconstructed simultaneously to decompose the global computational complexity, and then are synthesized together to reconstruct the 3D target. Based on the conventional TCAI model, we deduce and build a new TCAI model based on MF. Furthermore, the convolutional neural network (CNN) is designed to teach the MF-TCAI how to reconstruct the low SNR target better. The experimental results demonstrate that the MF-TCAI achieves impressive performance on imaging ability and efficiency under low SNR. Moreover, the MF-TCAI has learned to better resolve the low-SNR 3D target with the help of CNN. In summary, the proposed 3D TCAI can achieve: (1) low-SNR high-resolution imaging by using MF; (2) efficient 3D imaging by downsizing the large-scale reference-signal matrix; and (3) intelligent imaging with CNN. Therefore, the TCAI based on MF and CNN has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Electrochemical Biosensor for Nitrite Based on Polyacrylic-Graphene Composite Film with Covalently Immobilized Hemoglobin
Sensors 2018, 18(5), 1343; https://doi.org/10.3390/s18051343
Received: 11 March 2018 / Revised: 13 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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Abstract
A new biosensor for the analysis of nitrite in food was developed based on hemoglobin (Hb) covalently immobilized on the succinimide functionalized poly(n-butyl acrylate)-graphene [poly(nBA)-rGO] composite film deposited on a carbon-paste screen-printed electrode (SPE). The immobilized Hb on the poly(nBA)-rGO conducting matrix exhibited
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A new biosensor for the analysis of nitrite in food was developed based on hemoglobin (Hb) covalently immobilized on the succinimide functionalized poly(n-butyl acrylate)-graphene [poly(nBA)-rGO] composite film deposited on a carbon-paste screen-printed electrode (SPE). The immobilized Hb on the poly(nBA)-rGO conducting matrix exhibited electrocatalytic ability for the reduction of nitrite with significant enhancement in the reduction peak at −0.6 V versus Ag/AgCl reference electrode. Thus, direct determination of nitrite can be achieved by monitoring the cathodic peak current signal of the proposed polyacrylic-graphene hybrid film-based voltammetric nitrite biosensor. The nitrite biosensor exhibited a reproducible dynamic linear response range from 0.05–5 mg L−1 nitrite and a detection limit of 0.03 mg L−1. No significant interference was observed by potential interfering ions such as Ca2+, Na+, K+, NH4+, Mg2+, and NO3 ions. Analysis of nitrite in both raw and processed edible bird’s nest (EBN) samples demonstrated recovery of close to 100%. The covalent immobilization of Hb on poly(nBA)-rGO composite film has improved the performance of the electrochemical nitrite biosensor in terms of broader detection range, lower detection limit, and prolonged biosensor stability. Full article
(This article belongs to the Special Issue Polymer-Based Sensors for Bioanalytes)
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Open AccessArticle Smart Web-Based Platform to Support Physical Rehabilitation
Sensors 2018, 18(5), 1344; https://doi.org/10.3390/s18051344
Received: 29 March 2018 / Revised: 21 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
The enhancement of ubiquitous and pervasive computing brings new perspectives in medical rehabilitation. In that sense, the present study proposes a smart, web-based platform to promote the reeducation of patients after hip replacement surgery. This project focuses on two fundamental aspects in the
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The enhancement of ubiquitous and pervasive computing brings new perspectives in medical rehabilitation. In that sense, the present study proposes a smart, web-based platform to promote the reeducation of patients after hip replacement surgery. This project focuses on two fundamental aspects in the development of a suitable tele-rehabilitation application, which are: (i) being based on an affordable technology, and (ii) providing the patients with a real-time assessment of the correctness of their movements. A probabilistic approach based on the development and training of ten Hidden Markov Models (HMMs) is used to discriminate in real time the main faults in the execution of the therapeutic exercises. Two experiments are designed to evaluate the precision of the algorithm for classifying movements performed in the laboratory and clinical settings, respectively. A comparative analysis of the data shows that the models are as reliable as the physiotherapists to discriminate and identify the motion errors. The results are discussed in terms of the required setup for a successful application in the field and further implementations to improve the accuracy and usability of the system. Full article
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Open AccessArticle Appdaptivity: An Internet of Things Device-Decoupled System for Portable Applications in Changing Contexts
Sensors 2018, 18(5), 1345; https://doi.org/10.3390/s18051345
Received: 1 March 2018 / Revised: 20 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
Currently, applications in the Internet of Things (IoT) are tightly coupled to the underlying physical devices. As a consequence, upon adding a device, device replacement or user’s relocation to a different physical space, application developers have to re-perform installation and configuration processes to
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Currently, applications in the Internet of Things (IoT) are tightly coupled to the underlying physical devices. As a consequence, upon adding a device, device replacement or user’s relocation to a different physical space, application developers have to re-perform installation and configuration processes to reconfigure applications, which bears costs in time and knowledge of low-level details. In the emerging IoT field, this issue is even more challenging due to its current unpredictable growth in term of applications and connected devices. In addition, IoT applications can be personalised to each end user and can be present in different environments. As a result, IoT scenarios are very changeable, presenting a challenge for IoT applications. In this paper we present Appdaptivity, a system that enables the development of portable device-decoupled applications that can be adapted to changing contexts. Through Appdaptivity, application developers can intuitively create portable and personalised applications, disengaging from the underlying physical infrastructure. Results confirms a good scalability of the system in terms of connected users and components involved. Full article
(This article belongs to the Special Issue Internet of Things and Ubiquitous Sensing)
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Open AccessArticle Using the Transient Response of WO3 Nanoneedles under Pulsed UV Light in the Detection of NH3 and NO2
Sensors 2018, 18(5), 1346; https://doi.org/10.3390/s18051346
Received: 3 April 2018 / Revised: 23 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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Abstract
Here we report on the use of pulsed UV light for activating the gas sensing response of metal oxides. Under pulsed UV light, the resistance of metal oxides presents a ripple due to light-induced transient adsorption and desorption phenomena. This methodology has been
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Here we report on the use of pulsed UV light for activating the gas sensing response of metal oxides. Under pulsed UV light, the resistance of metal oxides presents a ripple due to light-induced transient adsorption and desorption phenomena. This methodology has been applied to tungsten oxide nanoneedle gas sensors operated either at room temperature or under mild heating (50 °C or 100 °C). It has been found that by analyzing the rate of resistance change caused by pulsed UV light, a fast determination of gas concentration is achieved (ten-fold improvement in response time). The technique is useful for detecting both oxidizing (NO2) and reducing (NH3) gases, even in the presence of different levels of ambient humidity. Room temperature operated sensors under pulsed UV light show good response towards ammonia and nitrogen dioxide at low power consumption levels. Increasing their operating temperature to 50 °C or 100 °C has the effect of further increasing sensitivity. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle Real-Time Vision-Based Stiffness Mapping
Sensors 2018, 18(5), 1347; https://doi.org/10.3390/s18051347
Received: 29 March 2018 / Revised: 20 April 2018 / Accepted: 21 April 2018 / Published: 26 April 2018
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Abstract
This paper presents new findings concerning a hand-held stiffness probe for the medical diagnosis of abnormalities during palpation of soft-tissue. Palpation is recognized by the medical community as an essential and low-cost method to detect and diagnose disease in soft-tissue. However, differences are
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This paper presents new findings concerning a hand-held stiffness probe for the medical diagnosis of abnormalities during palpation of soft-tissue. Palpation is recognized by the medical community as an essential and low-cost method to detect and diagnose disease in soft-tissue. However, differences are often subtle and clinicians need to train for many years before they can conduct a reliable diagnosis. The probe presented here fills this gap providing a means to easily obtain stiffness values of soft tissue during a palpation procedure. Our stiffness sensor is equipped with a multi degree of freedom (DoF) Aurora magnetic tracker, allowing us to track and record the 3D position of the probe whilst examining a tissue area, and generate a 3D stiffness map in real-time. The stiffness probe was integrated in a robotic arm and tested in an artificial environment representing a good model of soft tissue organs; the results show that the sensor can accurately measure and map the stiffness of a silicon phantom embedded with areas of varying stiffness. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Plasmonic Multichannel Refractive Index Sensor Based on Subwavelength Tangent-Ring Metal–Insulator–Metal Waveguide
Sensors 2018, 18(5), 1348; https://doi.org/10.3390/s18051348
Received: 26 March 2018 / Revised: 20 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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Abstract
In this paper, a multichannel refractive index sensor based on a subwavelength metal–insulator–metal (MIM) waveguide coupled with tangent-ring resonators is proposed. When two tangent-ring resonators were placed above the MIM waveguide, Fano resonance with asymmetrical line shape appeared in the transmission spectrum due
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In this paper, a multichannel refractive index sensor based on a subwavelength metal–insulator–metal (MIM) waveguide coupled with tangent-ring resonators is proposed. When two tangent-ring resonators were placed above the MIM waveguide, Fano resonance with asymmetrical line shape appeared in the transmission spectrum due to the interference between the light–dark resonant modes. The sensitivity and figure of merit were as high as 880 nm/RIU and 964, respectively. Through adding more tangent-ring resonators, multiple Fano resonances with ultrasharp peaks/dips were achieved in the wavelength range of 800–2000 nm. Besides, negative group delays were also observed in the Fano resonant dips. Two-dimensional finite-difference time-domain (FDTD) method was used to simulate and analyze the performances of the proposed structures. These kinds of multiring structures can find important applications in the on-chip optical sensing and optical communication areas. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Comparing Building and Neighborhood-Scale Variability of CO2 and O3 to Inform Deployment Considerations for Low-Cost Sensor System Use
Sensors 2018, 18(5), 1349; https://doi.org/10.3390/s18051349
Received: 28 March 2018 / Revised: 23 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
The increased use of low-cost air quality sensor systems, particularly by communities, calls for the further development of best-practices to ensure these systems collect usable data. One area identified as requiring more attention is that of deployment logistics, that is, how to select
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The increased use of low-cost air quality sensor systems, particularly by communities, calls for the further development of best-practices to ensure these systems collect usable data. One area identified as requiring more attention is that of deployment logistics, that is, how to select deployment sites and how to strategically place sensors at these sites. Given that sensors are often placed at homes and businesses, ideal placement is not always possible. Considerations such as convenience, access, aesthetics, and safety are also important. To explore this issue, we placed multiple sensor systems at an existing field site allowing us to examine both neighborhood-level and building-level variability during a concurrent period for CO2 (a primary pollutant) and O3 (a secondary pollutant). In line with previous studies, we found that local and transported emissions as well as thermal differences in sensor systems drive variability, particularly for high-time resolution data. While this level of variability is unlikely to affect data on larger averaging scales, this variability could impact analysis if the user is interested in high-time resolution or examining local sources. However, with thoughtful placement and thorough documentation, high-time resolution data at the neighborhood level has the potential to provide us with entirely new information on local air quality trends and emissions. Full article
(This article belongs to the Special Issue Sensor Networks for Environmental Observations)
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Open AccessArticle Improving Fall Detection Using an On-Wrist Wearable Accelerometer
Sensors 2018, 18(5), 1350; https://doi.org/10.3390/s18051350
Received: 12 March 2018 / Revised: 18 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are
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Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle Cooperative Monocular-Based SLAM for Multi-UAV Systems in GPS-Denied Environments
Sensors 2018, 18(5), 1351; https://doi.org/10.3390/s18051351
Received: 21 January 2018 / Revised: 3 April 2018 / Accepted: 19 April 2018 / Published: 26 April 2018
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This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability
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This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation. Full article
(This article belongs to the Special Issue I3S 2017 Selected Papers)
Open AccessArticle Fused Microknot Optical Resonators in Folded Photonic Tapers for in-Liquid Durable Sensing
Sensors 2018, 18(5), 1352; https://doi.org/10.3390/s18051352
Received: 4 March 2018 / Revised: 2 April 2018 / Accepted: 19 April 2018 / Published: 26 April 2018
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Abstract
Optical microknot fibers (OMFs) serve as localized devices, where photonic resonances (PRs) enable self-interfering elements sensitive to their environment. However, typical fragility and drifting of the knot severely limit the performance and durability of microknots as sensors in aqueous settings. Herein we present
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Optical microknot fibers (OMFs) serve as localized devices, where photonic resonances (PRs) enable self-interfering elements sensitive to their environment. However, typical fragility and drifting of the knot severely limit the performance and durability of microknots as sensors in aqueous settings. Herein we present the fabrication, electrical fusing, preparation, and persistent detection of volatile liquids in multiple wetting–dewetting cycles of volatile compounds and quantify the persistent phase shifts with a simple model relating to the ambient liquid, enabling durable in-liquid sensing employing OMF PRs. Full article
(This article belongs to the Special Issue Optical Fiber Sensors 2017)
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Open AccessArticle All-Optical Photoacoustic Sensors for Steel Rebar Corrosion Monitoring
Sensors 2018, 18(5), 1353; https://doi.org/10.3390/s18051353
Received: 3 April 2018 / Revised: 21 April 2018 / Accepted: 25 April 2018 / Published: 27 April 2018
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Abstract
This article presents an application of an active all-optical photoacoustic sensing system with four elements for steel rebar corrosion monitoring. The sensor utilized a photoacoustic mechanism of gold nanocomposites to generate 8 MHz broadband ultrasound pulses in 0.4 mm compact space. A nanosecond
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This article presents an application of an active all-optical photoacoustic sensing system with four elements for steel rebar corrosion monitoring. The sensor utilized a photoacoustic mechanism of gold nanocomposites to generate 8 MHz broadband ultrasound pulses in 0.4 mm compact space. A nanosecond 532 nm pulsed laser and 400 μm multimode fiber were employed to incite an ultrasound reaction. The fiber Bragg gratings were used as distributed ultrasound detectors. Accelerated corrosion testing was applied to four sections of a single steel rebar with four different corrosion degrees. Our results demonstrated that the mass loss of steel rebar displayed an exponential growth with ultrasound frequency shifts. The sensitivity of the sensing system was such that 0.175 MHz central frequency reduction corresponded to 0.02 g mass loss of steel rebar corrosion. It was proved that the all-optical photoacoustic sensing system can actively evaluate the corrosion of steel rebar via ultrasound spectrum. This multipoint all-optical photoacoustic method is promising for embedment into a concrete structure for distributed corrosion monitoring. Full article
(This article belongs to the Special Issue Optics Sensors Using Microstructured and Photonics Crystal Fibers)
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Open AccessArticle An Improved BeiDou-2 Satellite-Induced Code Bias Estimation Method
Sensors 2018, 18(5), 1354; https://doi.org/10.3390/s18051354
Received: 23 March 2018 / Revised: 26 April 2018 / Accepted: 26 April 2018 / Published: 27 April 2018
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Abstract
Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB), which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS) IGSO and MEO satellites. In
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Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB), which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS) IGSO and MEO satellites. In order to mitigate their adverse effects on absolute precise applications which use the code measurements, we propose in this paper an improved correction model to estimate the CMB. Different from the traditional model which considering the correction values are orbit-type dependent (estimating two sets of values for IGSO and MEO, respectively) and modeling the CMB as a piecewise linear function with a elevation node separation of 10°, we estimate the corrections for each BDS IGSO + MEO satellite on one hand, and a denser elevation node separation of 5° is used to model the CMB variations on the other hand. Currently, the institutions such as IGS-MGEX operate over 120 stations which providing the daily BDS observations. These large amounts of data provide adequate support to refine the CMB estimation satellite by satellite in our improved model. One month BDS observations from MGEX are used for assessing the performance of the improved CMB model by means of precise point positioning (PPP). Experimental results show that for the satellites on the same orbit type, obvious differences can be found in the CMB at the same node and frequency. Results show that the new correction model can improve the wide-lane (WL) ambiguity usage rate for WL fractional cycle bias estimation, shorten the WL and narrow-lane (NL) time to first fix (TTFF) in PPP ambiguity resolution (AR) as well as improve the PPP positioning accuracy. With our improved correction model, the usage of WL ambiguity is increased from 94.1% to 96.0%, the WL and NL TTFF of PPP AR is shorten from 10.6 to 9.3 min, 67.9 to 63.3 min, respectively, compared with the traditional correction model. In addition, both the traditional and improved CMB model have a better performance in these aspects compared with the model which does not account for the CMB correction. Full article
(This article belongs to the collection Positioning and Navigation)
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Open AccessArticle Feasibility Study on S-Band Microwave Radiation and 3D-Thermal Infrared Imaging Sensor-Aided Recognition of Polymer Materials from End-of-Life Vehicles
Sensors 2018, 18(5), 1355; https://doi.org/10.3390/s18051355
Received: 14 February 2018 / Revised: 24 April 2018 / Accepted: 25 April 2018 / Published: 27 April 2018
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Abstract
With the increase the worldwide consumption of vehicles, end-of-life vehicles (ELVs) have kept rapidly increasing in the last two decades. Metallic parts and materials of ELVs can be easily reused and recycled, but the automobile shredder residues (ASRs), of which elastomer and plastic
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With the increase the worldwide consumption of vehicles, end-of-life vehicles (ELVs) have kept rapidly increasing in the last two decades. Metallic parts and materials of ELVs can be easily reused and recycled, but the automobile shredder residues (ASRs), of which elastomer and plastic materials make up the vast majority, are difficult to recycle. ASRs are classified as hazardous materials in the main industrial countries, and are required to be materially recycled up to 85–95% by mass until 2020. However, there is neither sufficient theoretical nor practical experience for sorting ASR polymers. In this research, we provide a novel method by using S-Band microwave irradiation together with 3D scanning as well as infrared thermal imaging sensors for the recognition and sorting of typical plastics and elastomers from the ASR mixture. In this study, an industrial magnetron array with 2.45 GHz irradiation was utilized as the microwave source. Seven kinds of ELV polymer (PVC, ABS, PP, EPDM, NBR, CR, and SBR) crushed scrap residues were tested. After specific power microwave irradiation for a certain time, the tested polymer materials were heated up to different extents corresponding to their respective sensitivities to microwave irradiation. Due to the variations in polymer chemical structure and additive agents, polymers have different sensitivities to microwave radiation, which leads to differences in temperature rises. The differences of temperature increase were obtained by a thermal infrared sensor, and the position and geometrical features of the tested scraps were acquired by a 3D imaging sensor. With this information, the scrap material could be recognized and then sorted. The results showed that this method was effective when the tested polymer materials were heated up to more than 30 °C. For full recognition of the tested polymer scraps, the minimum temperature variations of 5 °C and 10.5 °C for plastics and elastomers were needed, respectively. The sorting efficiency was independent of particle sizes but depended on the power and time of the microwave irradiation. Generally, more than 75% (mass) of the tested polymer materials could be successfully recognized and sorted under an irradiation power of 3 kW. Plastics were much more insensitive to microwave irradiation than elastomers. With this method, the tested mixture of the plastic group (PVC, ABS, PP) and the mixture of elastomer group (EPDM, NBR, CR, and SBR) could be fully separated with an efficiency of 100%. Full article
(This article belongs to the Special Issue I3S 2017 Selected Papers)
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Open AccessArticle On the Nature of Energy-Feasible Wireless Nanosensor Networks
Sensors 2018, 18(5), 1356; https://doi.org/10.3390/s18051356
Received: 15 March 2018 / Revised: 23 April 2018 / Accepted: 24 April 2018 / Published: 27 April 2018
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Abstract
Electromagnetic nanocommunications, understood as the communication between electronic nanoscale devices through electromagnetic waves in the terahertz band, has attracted increasing attention in recent years. In this regard, several solutions have already been proposed. However, many of them do not sufficiently capture the significance
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Electromagnetic nanocommunications, understood as the communication between electronic nanoscale devices through electromagnetic waves in the terahertz band, has attracted increasing attention in recent years. In this regard, several solutions have already been proposed. However, many of them do not sufficiently capture the significance of the limitations in nanodevice energy-gathering and storing capacity. In this paper, we address key factors affecting the energy consumption of nanodevices, highlighting the effect of the communication scheme employed. Then, we also examine how nanodevices are powered, focusing on the main parameters governing the powering nanosystem. Different mathematical expressions are derived to analyze the impact of these parameters on its performance. Based on these expressions, the functionality of a nanogenerator is evaluated to gain insight into the conditions under which a wireless nanosensor network (WNSN) is viable from the energetic point of view. The results reveal that a micrometer-sized piezoelectric system in high-lossy environments (exceeding 100 dB/mm) becomes inoperative for transmission distances over 1.5 mm by its inability to harvest and store the amount of energy required to overcome the path loss. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2018)
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Open AccessArticle Colorimetric Humidity Sensor Using Inverse Opal Photonic Gel in Hydrophilic Ionic Liquid
Sensors 2018, 18(5), 1357; https://doi.org/10.3390/s18051357
Received: 30 March 2018 / Revised: 25 April 2018 / Accepted: 25 April 2018 / Published: 27 April 2018
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Abstract
We demonstrate a fast response colorimetric humidity sensor using a crosslinked poly(2-hydroxyethyl methacrylate) (PHEMA) in the form of inverse opal photonic gel (IOPG) soaked in 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM+][BF4]), a non-volatile hydrophilic room temperature ionic liquid (IL). An evaporative
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We demonstrate a fast response colorimetric humidity sensor using a crosslinked poly(2-hydroxyethyl methacrylate) (PHEMA) in the form of inverse opal photonic gel (IOPG) soaked in 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM+][BF4]), a non-volatile hydrophilic room temperature ionic liquid (IL). An evaporative colloidal assembly enabled the fabrication of highly crystalline opal template, and a subsequent photopolymerization of PHEMA followed by solvent-etching and final soaking in IL produced a humidity-responsive IOPG showing highly reflective structural color by Bragg diffraction. Three IOPG sensors with different crosslinking density were fabricated on a single chip, where a lightly crosslinked IOPG exhibited the color change response over entire visible spectrum with respect to the humidity changes from 0 to 80% RH. As the water content increased in IL, thermodynamic interactions between PHEMA and [BMIM+][BF4] became more favorable, to show a red-shifted structural color owing to a longitudinal swelling of IOPG. Highly porous IO structure enabled fast humidity-sensing kinetics with the response times of ~1 min for both swelling and deswelling. Temperature-dependent swelling of PHEMA in [BMIM+][BF4] revealed that the current system follows an upper critical solution temperature (UCST) behavior with the diffraction wavelength change as small as 1% at the temperature changes from