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Sensors (ISSN 1424-8220; CODEN: SENSC9) is the leading international, peer-reviewed, open access journal on the science and technology of sensors and biosensors. Sensors is published monthly online by MDPI.
- Open Access - free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed by the Science Citation Index Expanded (Web of Science), MEDLINE (PubMed), Ei Compendex, Inspec (IET) and other databases.
- Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 24 days after submission; acceptance to publication is undertaken in 5.6 days (median values for papers published in this journal in 2017).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Sections: published in 8 topical sections.
Impact Factor: 2.677 (2016) ; 5-Year Impact Factor: 2.964 (2016)
Latest Articles
Label-Free Impedance Sensing of Aflatoxin B1 with Polyaniline Nanofibers/Au Nanoparticle Electrode Array
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Sensors 2018, 18(5), 1320; doi:10.3390/s18051320 (registering DOI) - 24 April 2018
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
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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|1Hz 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 (3) 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.
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Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model
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Sensors 2018, 18(5), 1318; doi:10.3390/s18051318 (registering DOI) - 24 April 2018
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
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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.
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Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator
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Sensors 2018, 18(5), 1317; doi:10.3390/s18051317 (registering DOI) - 24 April 2018
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)
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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.
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Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study
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Sensors 2018, 18(5), 1316; doi:10.3390/s18051316 (registering DOI) - 24 April 2018
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,
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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.
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Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor
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Sensors 2018, 18(5), 1315; doi:10.3390/s18051315 (registering DOI) - 24 April 2018
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
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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.
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High Sensitive pH Sensor Based on AlInN/GaN Heterostructure Transistor
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Sensors 2018, 18(5), 1314; doi:10.3390/s18051314 (registering DOI) - 24 April 2018
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
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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.
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Design and Performance of a 1 ms High-Speed Vision Chip with 3D-Stacked 140 GOPS Column-Parallel PEs †
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Sensors 2018, 18(5), 1313; doi:10.3390/s18051313 (registering DOI) - 24 April 2018
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
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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.
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Infrastructure for Integration of Legacy Electrical Equipment into a Smart-Grid Using Wireless Sensor Networks
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Sensors 2018, 18(5), 1312; doi:10.3390/s18051312 (registering DOI) - 24 April 2018
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
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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.
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Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data
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Sensors 2018, 18(5), 1311; doi:10.3390/s18051311 (registering DOI) - 24 April 2018
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
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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.
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Stand-Off Detection of Alcohol Vapors Exhaled by Humans
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Sensors 2018, 18(5), 1310; doi:10.3390/s18051310 (registering DOI) - 24 April 2018
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
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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.
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Towards an Electrochemical Immunosensor System with Temperature Control for Cytokine Detection
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Sensors 2018, 18(5), 1309; doi:10.3390/s18051309 (registering DOI) - 24 April 2018
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
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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.
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Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound
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Sensors 2018, 18(5), 1308; doi:10.3390/s18051308 (registering DOI) - 24 April 2018
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
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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.
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A Review of the Piezoelectric Electromechanical Impedance Based Structural Health Monitoring Technique for Engineering Structures
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Sensors 2018, 18(5), 1307; doi:10.3390/s18051307 - 24 April 2018
Abstract
The birth of smart materials such as piezoelectric (PZT) transducers has aided in revolutionizing the field of structural health monitoring (SHM) based on non-destructive testing (NDT) methods. While a relatively new NDT method known as the electromechanical (EMI) technique has been investigated for
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The birth of smart materials such as piezoelectric (PZT) transducers has aided in revolutionizing the field of structural health monitoring (SHM) based on non-destructive testing (NDT) methods. While a relatively new NDT method known as the electromechanical (EMI) technique has been investigated for more than two decades, there are still various problems that must be solved before it is applied to real structures. The technique, which has a significant potential to contribute to the creation of one of the most effective SHM systems, involves the use of a single PZT for exciting and sensing of the host structure. In this paper, studies applied for the past decade related to the EMI technique have been reviewed to understand its trend. In addition, new concepts and ideas proposed by various authors are also surveyed, and the paper concludes with a discussion of the potential directions for future works.
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Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography
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Sensors 2018, 18(5), 1306; doi:10.3390/s18051306 - 24 April 2018
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
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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.
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Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
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Sensors 2018, 18(5), 1305; doi:10.3390/s18051305 - 24 April 2018
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)
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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.
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Displacement and Strain Measurement up to 1000 °C Using a Hollow Coaxial Cable Fabry-Perot Resonator
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Sensors 2018, 18(5), 1304; doi:10.3390/s18051304 - 24 April 2018
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
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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.
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An Improved Fast Self-Calibration Method for Hybrid Inertial Navigation System under Stationary Condition
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Sensors 2018, 18(5), 1303; doi:10.3390/s18051303 - 24 April 2018
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,
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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.
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Development of PZT Actuated Valveless Micropump
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Sensors 2018, 18(5), 1302; doi:10.3390/s18051302 - 24 April 2018
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
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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.
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Development and Hybrid Position/Force Control of a Dual-Drive Macro-Fiber-Composite Microgripper
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Sensors 2018, 18(4), 1301; doi:10.3390/s18041301 - 23 April 2018
Abstract
This paper reports on the development, implementation and hybrid control of a new micro-fiber-composite microgripper with synchronous position and force control capabilities. In particular, the micro-fiber-composite actuator was composed of rectangular piezoelectric fibers covered by interdigitated electrodes and embedded in structural epoxy. Thus,
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This paper reports on the development, implementation and hybrid control of a new micro-fiber-composite microgripper with synchronous position and force control capabilities. In particular, the micro-fiber-composite actuator was composed of rectangular piezoelectric fibers covered by interdigitated electrodes and embedded in structural epoxy. Thus, the micro-fiber-composite microgripper had a larger displacement-volume ratio (i.e., the ratio of the output displacement to the volume of the microgripper) than that of a traditional piezoelectric one. Moreover, to regulate both the gripper position and the gripping force simultaneously, a hybrid position/force control scheme using fuzzy sliding mode control and the proportional-integral controller was developed. In particular, the fuzzy sliding mode control was used to achieve the precision position control under the influence of the system disturbances and uncertainties, and the proportional-integral controller was used to guarantee the force control accuracy of the microgripper. A series of experimental investigations was performed to verify the feasibility of the developed microgripper and the control scheme. The experimental results validated the effectiveness of the designed microgripper and hybrid control scheme. The developed microgripper was capable of precision and multiscale micromanipulation tasks.
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