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Sensors, Volume 19, Issue 1 (January-1 2019)

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Cover Story (view full-size image) An electrical penetration assembly (EPA), which provides a pressure barrier for the containment [...] Read more.
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Open AccessArticle Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses
Sensors 2019, 19(1), 217; https://doi.org/10.3390/s19010217
Received: 19 November 2018 / Revised: 24 December 2018 / Accepted: 4 January 2019 / Published: 8 January 2019
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Abstract
A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a [...] Read more.
A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH4 and their mixtures were acquired by an automated gas distribution and test system. By adjusting the parameters of the CNN structure, the gas LeNet-5 was improved to recognize the three categories of CO, CH4 and their mixtures omitting the concentration influences. The final gas identification accuracy rate reached 98.67% with the unused data as test set by the improved gas LeNet-5. Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle Implicit Calibration Using Probable Fixation Targets
Sensors 2019, 19(1), 216; https://doi.org/10.3390/s19010216
Received: 8 November 2018 / Revised: 13 December 2018 / Accepted: 25 December 2018 / Published: 8 January 2019
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Abstract
Proper calibration of eye movement signal registered by an eye tracker seems to be one of the main challenges in popularizing eye trackers as yet another user-input device. Classic calibration methods taking time and imposing unnatural behavior on eyes must be replaced by [...] Read more.
Proper calibration of eye movement signal registered by an eye tracker seems to be one of the main challenges in popularizing eye trackers as yet another user-input device. Classic calibration methods taking time and imposing unnatural behavior on eyes must be replaced by intelligent methods that are able to calibrate the signal without conscious cooperation by the user. Such an implicit calibration requires some knowledge about the stimulus a user is looking at and takes into account this information to predict probable gaze targets. This paper describes a possible method to perform implicit calibration: it starts with finding probable fixation targets (PFTs), then it uses these targets to build a mapping-probable gaze path. Various algorithms that may be used for finding PFTs and mappings are presented in the paper and errors are calculated using two datasets registered with two different types of eye trackers. The results show that although for now the implicit calibration provides results worse than the classic one, it may be comparable with it and sufficient for some applications. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments
Sensors 2019, 19(1), 215; https://doi.org/10.3390/s19010215
Received: 27 October 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 8 January 2019
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Abstract
Robots, or in general, intelligent vehicles, require large amounts of data to adapt their behavior to the environment and achieve their goals. When their missions take place in large areas, using additional information to that gathered by the onboard sensors frequently offers a [...] Read more.
Robots, or in general, intelligent vehicles, require large amounts of data to adapt their behavior to the environment and achieve their goals. When their missions take place in large areas, using additional information to that gathered by the onboard sensors frequently offers a more efficient solution of the problem. The emergence of Cyber-Physical Systems and Cloud computing allows this approach, but integration of sensory information, and its effective availability for the robots or vehicles is challenging. This paper addresses the development and implementation of a modular mobile node of a Wireless Sensor Network (WSN), designed to be mounted onboard vehicles, and capable of using different sensors according to mission needs. The mobile node is integrated with an existing static network, transforming it into a Hybrid Wireless Sensor Network (H-WSN), and adding flexibility and range to it. The integration is achieved without the need for multi-hop routing. A database holds the data acquired by both mobile and static nodes, allowing access in real-time to the gathered information. A Human–Machine Interface (HMI) presents this information to users. Finally, the system is tested in real urban scenarios in a use-case of measurement of gas levels. Full article
(This article belongs to the Special Issue Design and Implementation of Future CPS)
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Open AccessArticle Decoupling of Airborne Dynamic Bending Deformation Angle and Its Application in the High-Accuracy Transfer Alignment Process
Sensors 2019, 19(1), 214; https://doi.org/10.3390/s19010214
Received: 21 November 2018 / Revised: 3 January 2019 / Accepted: 4 January 2019 / Published: 8 January 2019
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Abstract
In the traditional airborne distributed position and orientation system (DPOS) transfer alignment process, the coupling angle between the dynamic deformation and body angular motion is not estimated or compensated, which causes the process to have low precision and long convergence time. To achieve [...] Read more.
In the traditional airborne distributed position and orientation system (DPOS) transfer alignment process, the coupling angle between the dynamic deformation and body angular motion is not estimated or compensated, which causes the process to have low precision and long convergence time. To achieve high-precision transfer alignment, a decoupling method for the airborne dynamic deformation angle is proposed in this paper. The model of the coupling angle is established through mathematical derivation. Then, taking the coupling angle into consideration, angular velocity error and velocity error between the master INS and slave IMU are corrected. Based on this, a novel 27-state Kalman filter model is established. Simulation results demonstrate that, compared with the traditional transfer alignment model, the model proposed in this paper has faster convergence time and higher accuracy. Full article
(This article belongs to the Special Issue Aerospace Sensors and Multisensor Systems)
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Open AccessArticle Extended Multiple Aperture Mapdrift-Based Doppler Parameter Estimation and Compensation for Very-High-Squint Airborne SAR Imaging
Sensors 2019, 19(1), 213; https://doi.org/10.3390/s19010213
Received: 31 October 2018 / Revised: 3 January 2019 / Accepted: 3 January 2019 / Published: 8 January 2019
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Abstract
Doppler parameter estimation and compensation (DPEC) is an important technique for airborne SAR imaging due to the unpredictable disturbance of real aircraft trajectory. Traditional DPEC methods can be only applied for broadside, small- or medium-squint geometries, as they at most consider the spatial [...] Read more.
Doppler parameter estimation and compensation (DPEC) is an important technique for airborne SAR imaging due to the unpredictable disturbance of real aircraft trajectory. Traditional DPEC methods can be only applied for broadside, small- or medium-squint geometries, as they at most consider the spatial variance of the second-order Doppler phase. To implement the DPEC in very-high-squint geometries, we propose an extended multiple aperture mapdrift (EMAM) method in this paper for better accuracy. This advantage is achieved by further estimating and compensating the spatial variation of the third-order Doppler phase, i.e., the derivative of the Doppler rate. The main procedures of the EMAM, including the steps of sub-view image generation, sliding-window-based cross-correlation, and image-offset-based Doppler parameter estimation, are derived in detail, followed by the analyses for the EMAM performance. The presented approach is evaluated by both computer simulations and real airborne data. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Techniques and Applications)
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Open AccessEditorial Acknowledgement to Reviewers of Sensors in 2018
Sensors 2019, 19(1), 212; https://doi.org/10.3390/s19010212
Published: 8 January 2019
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Abstract
Rigorous peer-review is the corner-stone of high-quality academic publishing[...] Full article
Open AccessArticle Synthesis of Cu2O/CuO Nanocrystals and Their Application to H2S Sensing
Sensors 2019, 19(1), 211; https://doi.org/10.3390/s19010211
Received: 13 December 2018 / Revised: 28 December 2018 / Accepted: 7 January 2019 / Published: 8 January 2019
Cited by 2 | Viewed by 498 | PDF Full-text (4648 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Semiconducting metal oxide nanocrystals are an important class of materials that have versatile applications because of their useful properties and high stability. Here, we developed a simple route to synthesize nanocrystals (NCs) of copper oxides such as Cu2O and CuO using [...] Read more.
Semiconducting metal oxide nanocrystals are an important class of materials that have versatile applications because of their useful properties and high stability. Here, we developed a simple route to synthesize nanocrystals (NCs) of copper oxides such as Cu2O and CuO using a hot-soap method, and applied them to H2S sensing. Cu2O NCs were synthesized by simply heating a copper precursor in oleylamine in the presence of diol at 160 °C under an Ar flow. X-ray diffractometry (XRD), dynamic light scattering (DLS), and transmission electron microscopy (TEM) results indicated the formation of monodispersed Cu2O NCs having approximately 5 nm in crystallite size and 12 nm in colloidal size. The conversion of the Cu2O NCs to CuO NCs was undertaken by straightforward air oxidation at room temperature, as confirmed by XRD and UV-vis analyses. A thin film Cu2O NC sensor fabricated by spin coating showed responses to H2S in dilute concentrations (1–8 ppm) at 50–150 °C, but the stability was poor because of the formation of metallic Cu2S in a H2S atmosphere. We found that Pd loading improved the stability of the sensor response. The Pd-loaded Cu2O NC sensor exhibited reproducible responses to H2S at 200 °C. Based on the gas sensing mechanism, it is suggested that Pd loading facilitates the reaction of adsorbed oxygen with H2S and suppresses the irreversible formation of Cu2S. Full article
(This article belongs to the Special Issue Functional Materials for the Applications of Advanced Gas Sensors)
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Open AccessArticle Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals
Sensors 2019, 19(1), 210; https://doi.org/10.3390/s19010210
Received: 25 September 2018 / Revised: 18 December 2018 / Accepted: 26 December 2018 / Published: 8 January 2019
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Abstract
Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an [...] Read more.
Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI. Full article
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Open AccessArticle City Scale Particulate Matter Monitoring Using LoRaWAN Based Air Quality IoT Devices
Sensors 2019, 19(1), 209; https://doi.org/10.3390/s19010209
Received: 30 November 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 8 January 2019
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Abstract
Air Quality (AQ) is a very topical issue for many cities and has a direct impact on citizen health. The AQ of a large UK city is being investigated using low-cost Particulate Matter (PM) sensors, and the results obtained by these sensors have [...] Read more.
Air Quality (AQ) is a very topical issue for many cities and has a direct impact on citizen health. The AQ of a large UK city is being investigated using low-cost Particulate Matter (PM) sensors, and the results obtained by these sensors have been compared with government operated AQ stations. In the first pilot deployment, six AQ Internet of Things (IoT) devices have been designed and built, each with four different low-cost PM sensors, and they have been deployed at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide Area Network (LPWAN) coverage. The study concludes that (i) the physical device developed can operate at a city scale; (ii) some low-cost PM sensors are viable for monitoring AQ and for detecting PM trends; (iii) LoRaWAN is suitable for city scale sensor coverage where connectivity is an issue. Based on the findings from this first pilot project, a larger LoRaWAN enabled AQ sensor network is being deployed across the city of Southampton in the UK. Full article
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Open AccessArticle A Tangible Solution for Hand Motion Tracking in Clinical Applications
Sensors 2019, 19(1), 208; https://doi.org/10.3390/s19010208
Received: 21 November 2018 / Revised: 22 December 2018 / Accepted: 23 December 2018 / Published: 8 January 2019
Cited by 1 | Viewed by 483 | PDF Full-text (6327 KB) | HTML Full-text | XML Full-text
Abstract
Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. [...] Read more.
Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. The proposed system addresses the specific requirements of clinical applications in two ways: (1) In contrast to glove-based approaches, the proposed solution maintains the sense of touch. (2) In contrast to previous work, the proposed methods avoid the use of complex calibration procedures, which means that they are suitable for patients with severe motor impairment of the hand. To overcome the limited significance of validation in lab environments with homogeneous magnetic fields, we validate the proposed system using functional hand motions in the presence of severe magnetic disturbances as they appear in realistic clinical settings. We show that standard sensor fusion methods that rely on magnetometer readings may perform well in perfect laboratory environments but can lead to more than 15 cm root-mean-square error for the fingertip distances in realistic environments, while our advanced method yields root-mean-square errors below 2 cm for all performed motions. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests
Sensors 2019, 19(1), 207; https://doi.org/10.3390/s19010207
Received: 5 December 2018 / Revised: 5 January 2019 / Accepted: 6 January 2019 / Published: 8 January 2019
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Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping [...] Read more.
Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption. Full article
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
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Open AccessArticle A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors
Sensors 2019, 19(1), 206; https://doi.org/10.3390/s19010206
Received: 14 November 2018 / Revised: 13 December 2018 / Accepted: 18 December 2018 / Published: 8 January 2019
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Abstract
Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of [...] Read more.
Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory and hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson–Gaussian denoising. VST transforms signal-dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subsequently, multiscale transforms are employed on the noisy image to segregate signal and noise into separate coefficients. That facilitates the application of local binary hypothesis testing on multiple scales using empirical distribution function (EDF) for the purpose of detection and removal of noise. We demonstrate the effectiveness of the proposed framework with different multiscale transforms and on a wide variety of input datasets. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle In-Fiber Mach-Zehnder Interferometer Based on Three-Core Fiber for Measurement of Directional Bending
Sensors 2019, 19(1), 205; https://doi.org/10.3390/s19010205
Received: 19 November 2018 / Revised: 20 December 2018 / Accepted: 27 December 2018 / Published: 8 January 2019
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A highly sensitive directional bending sensor based on a three-core fiber (TCF) Mach-Zehnder interferometer (MZI) is presented in this study. This MZI-based bending sensor was fabricated by fusion-splicing a section of TCF between two single-mode fibers (SMF) with core-offset. Due to the location [...] Read more.
A highly sensitive directional bending sensor based on a three-core fiber (TCF) Mach-Zehnder interferometer (MZI) is presented in this study. This MZI-based bending sensor was fabricated by fusion-splicing a section of TCF between two single-mode fibers (SMF) with core-offset. Due to the location of the core in the TCF, a bend applied to the TCF-based MZI led to an elongation or shortening of the core, which makes the sensor suitable for directional bending measurement. To analyze the bending characteristics, two types of TCF-based sensors, with the fusion-spliced core located at different positions between the SMFs, were investigated. A swept source was employed in the measurement technique. The experimental results showed that, for the two types of sensors in this setup, the bending sensitivities of the two sensors were 15.36 nm/m−1 and 3.11 nm/m−1 at the bending direction of 0°, and −20.48 nm/m−1 and −5.29 nm/m−1 at the bending direction of 180°. The temperature sensitivities of the two sensors were 0.043 nm/°C and 0.041 nm/°C, respectively. The proposed sensors are compact, versatile, inexpensive to fabricate, and are expected to have potential applications in biomedical sensing. Full article
(This article belongs to the Special Issue Optical Sensing Based on Microscale Devices)
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Open AccessArticle Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
Sensors 2019, 19(1), 204; https://doi.org/10.3390/s19010204
Received: 30 November 2018 / Revised: 29 December 2018 / Accepted: 3 January 2019 / Published: 8 January 2019
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Abstract
With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel [...] Read more.
With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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Open AccessArticle Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm
Sensors 2019, 19(1), 203; https://doi.org/10.3390/s19010203
Received: 13 December 2018 / Revised: 27 December 2018 / Accepted: 4 January 2019 / Published: 8 January 2019
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With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion [...] Read more.
With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection. Full article
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Open AccessReview “Statistics 103” for Multitarget Tracking
Sensors 2019, 19(1), 202; https://doi.org/10.3390/s19010202
Received: 3 December 2018 / Revised: 24 December 2018 / Accepted: 24 December 2018 / Published: 8 January 2019
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The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and [...] Read more.
The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and measure-theoretic point process (p.p.) theory. Recently, however, an allegedly more general theoretical foundation for multitarget tracking has been proposed. In it, the constituent components of FISST have been systematically replaced by mathematically more complicated concepts—and, especially, by the very measure theory and measure-theoretic p.p.’s that FISST eschews. It is shown that this proposed alternative is actually a mathematical paraphrase of part of FISST that does not correctly address the technical idiosyncrasies of the multitarget tracking application. Full article
Open AccessArticle Wireless Charging Deployment in Sensor Networks
Sensors 2019, 19(1), 201; https://doi.org/10.3390/s19010201
Received: 11 December 2018 / Revised: 31 December 2018 / Accepted: 2 January 2019 / Published: 8 January 2019
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Charging schemes utilizing mobile wireless chargers can be applied to prolong the lifespan of a wireless sensor network. In considering charging schemes with mobile chargers, most current studies focus on charging each sensor from a single position, then optimizing the moving paths of [...] Read more.
Charging schemes utilizing mobile wireless chargers can be applied to prolong the lifespan of a wireless sensor network. In considering charging schemes with mobile chargers, most current studies focus on charging each sensor from a single position, then optimizing the moving paths of the chargers. However, in reality, a wireless charger may charge the same sensor from several positions in its path. In this paper we consider this fact and seek to minimize both the number of charging locations and the total required charging time. Two charging plans are developed. The first plan considers the charging time required by each sensor and greedily selects the charging service positions. The second one is a two-phase plan, where the number of charging positions is first minimized, then minimum charging times are assigned to every position according to the charging requirements of the nearby sensors. This paper also corrects a problem neglected by some studies in minimizing the number of charging service positions and further provides a corresponding solution. Empirical studies show that compared with other minimal clique partition (MCP)-based methods, the proposed charging plan may save up to 60% in terms of both the number of charging positions and the total required charging time. Full article
(This article belongs to the Special Issue Algorithm and Distributed Computing for the Internet of Things)
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Open AccessArticle Reinforcement Strains in Reinforced Concrete Tensile Members Recorded by Strain Gauges and FBG Sensors: Experimental and Numerical Analysis
Sensors 2019, 19(1), 200; https://doi.org/10.3390/s19010200
Received: 5 December 2018 / Revised: 21 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
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Abstract
Experimental and numerical studies have been carried out on reinforced concrete (RC) short tensile specimens. Double pull-out tests employed rectangular RC elements of a length determined not to yield any additional primary cracks. Tests were carried out with tensor strain gauges installed within [...] Read more.
Experimental and numerical studies have been carried out on reinforced concrete (RC) short tensile specimens. Double pull-out tests employed rectangular RC elements of a length determined not to yield any additional primary cracks. Tests were carried out with tensor strain gauges installed within a specially modified reinforcement bar and, alternatively, with fibre Bragg grating based optical sensors. The aim of this paper is to analyse the different experimental setups regarding obtaining more accurate and reliable reinforcement strain distribution data. Furthermore, reinforcement strain profiles obtained numerically using the stress transfer approach and the Model Code 2010 provided bond-slip model were compared against the experimental results. Accurate knowledge of the relation between the concrete and the embedded reinforcement is necessary and lacking to this day for less scattered and reliable prediction of cracking behaviour of RC elements. The presented experimental strain values enable future research on bond interaction. In addition, few double pull-out test results are published when compared to ordinary bond tests of single pull-out tests with embedded reinforcement. The authors summarize the comparison with observations on experimental setups and discuss the findings. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessReview A Survey of Energy-Efficient Communication Protocols with QoS Guarantees in Wireless Multimedia Sensor Networks
Sensors 2019, 19(1), 199; https://doi.org/10.3390/s19010199
Received: 3 December 2018 / Revised: 29 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
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In recent years, wireless multimedia sensor networks (WMSNs) have emerged as a prominent technique for delivering multimedia information such as still images and videos. Being under the great spotlight of research communities, however, multimedia delivery over resource- constraint WMSNs poses great challenges, especially [...] Read more.
In recent years, wireless multimedia sensor networks (WMSNs) have emerged as a prominent technique for delivering multimedia information such as still images and videos. Being under the great spotlight of research communities, however, multimedia delivery over resource- constraint WMSNs poses great challenges, especially in terms of energy efficiency and quality-of-service (QoS) guarantees. In this paper, recent developments in techniques for designing highly energy-efficient and QoS-capable WMSNs are surveyed. We first study the unique characteristics and the relevantly imposed requirements of WMSNs. For each requirement we also summarize their existing solutions. Then we review recent research efforts on energy-efficient and QoS-aware communication protocols, including MAC protocols, with a focus on their prioritization and service differentiation mechanisms and disjoint multipath routing protocols. Full article
(This article belongs to the Special Issue Visual Sensor Networks and Related Applications)
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Open AccessArticle BeiDou Augmented Navigation from Low Earth Orbit Satellites
Sensors 2019, 19(1), 198; https://doi.org/10.3390/s19010198
Received: 16 November 2018 / Revised: 20 December 2018 / Accepted: 26 December 2018 / Published: 7 January 2019
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Abstract
Currently, the Global Navigation Satellite System (GNSS) mainly uses the satellites in Medium Earth Orbit (MEO) to provide position, navigation, and timing (PNT) service. The weak navigation signals limit its usage in deep attenuation environments, and make it easy to interference and counterfeit [...] Read more.
Currently, the Global Navigation Satellite System (GNSS) mainly uses the satellites in Medium Earth Orbit (MEO) to provide position, navigation, and timing (PNT) service. The weak navigation signals limit its usage in deep attenuation environments, and make it easy to interference and counterfeit by jammers or spoofers. Moreover, being far away to the Earth results in relatively slow motion of the satellites in the sky and geometric change, making long time needed for achieved centimeter positioning accuracy. By using the satellites in Lower Earth Orbit (LEO) as the navigation satellites, these disadvantages can be addressed. In this contribution, the advantages of navigation from LEO constellation has been investigated and analyzed theoretically. The space segment of global Chinese BeiDou Navigation Satellite System consisting of three GEO, three IGSO, and 24 MEO satellites has been simulated with a LEO constellation with 120 satellites in 10 orbit planes with inclination of 55 degrees in a nearly circular orbit (eccentricity about 0.000001) at an approximate altitude of 975 km. With simulated data, the performance of LEO constellation to augment the global Chinese BeiDou Navigation Satellite System (BeiDou-3) has been assessed, as one of the example to show the promising of using LEO as navigation system. The results demonstrate that the satellite visibility and position dilution of precision have been significantly improved, particularly in mid-latitude region of Asia-Pacific region, once the LEO data were combined with BeiDou-3 for navigation. Most importantly, the convergence time for Precise Point Positioning (PPP) can be shorted from about 30 min to 1 min, which is essential and promising for real-time PPP application. Considering there are a plenty of commercial LEO communication constellation with hundreds or thousands of satellites, navigation from LEO will be an economic and promising way to change the heavily relay on GNSS systems. Full article
(This article belongs to the Special Issue High-Precision GNSS in Remote Sensing Applications)
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Open AccessFeature PaperArticle Faster R-CNN and Geometric Transformation-Based Detection of Driver’s Eyes Using Multiple Near-Infrared Camera Sensors
Sensors 2019, 19(1), 197; https://doi.org/10.3390/s19010197
Received: 3 December 2018 / Revised: 31 December 2018 / Accepted: 3 January 2019 / Published: 7 January 2019
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Abstract
Studies are being actively conducted on camera-based driver gaze tracking in a vehicle environment for vehicle interfaces and analyzing forward attention for judging driver inattention. In existing studies on the single-camera-based method, there are frequent situations in which the eye information necessary for [...] Read more.
Studies are being actively conducted on camera-based driver gaze tracking in a vehicle environment for vehicle interfaces and analyzing forward attention for judging driver inattention. In existing studies on the single-camera-based method, there are frequent situations in which the eye information necessary for gaze tracking cannot be observed well in the camera input image owing to the turning of the driver’s head during driving. To solve this problem, existing studies have used multiple-camera-based methods to obtain images to track the driver’s gaze. However, this method has the drawback of an excessive computation process and processing time, as it involves detecting the eyes and extracting the features of all images obtained from multiple cameras. This makes it difficult to implement it in an actual vehicle environment. To solve these limitations of existing studies, this study proposes a method that uses a shallow convolutional neural network (CNN) for the images of the driver’s face acquired from two cameras to adaptively select camera images more suitable for detecting eye position; faster R-CNN is applied to the selected driver images, and after the driver’s eyes are detected, the eye positions of the camera image of the other side are mapped through a geometric transformation matrix. Experiments were conducted using the self-built Dongguk Dual Camera-based Driver Database (DDCD-DB1) including the images of 26 participants acquired from inside a vehicle and the Columbia Gaze Data Set (CAVE-DB) open database. The results confirmed that the performance of the proposed method is superior to those of the existing methods. Full article
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
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Open AccessArticle Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
Sensors 2019, 19(1), 196; https://doi.org/10.3390/s19010196
Received: 6 December 2018 / Revised: 28 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
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Abstract
The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination [...] Read more.
The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network. Full article
(This article belongs to the Special Issue Signal and Information Processing in Wireless Sensor Networks)
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Open AccessArticle Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion
Sensors 2019, 19(1), 195; https://doi.org/10.3390/s19010195
Received: 29 November 2018 / Revised: 22 December 2018 / Accepted: 4 January 2019 / Published: 7 January 2019
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Abstract
Background: Motion capture and analyzing systems are essential for understanding locomotion. However, the existing devices are too cumbersome and can be used indoors only. A newly-developed wearable motion capture and measurement system with multiple sensors and ultrasound imaging was introduced in this study. [...] Read more.
Background: Motion capture and analyzing systems are essential for understanding locomotion. However, the existing devices are too cumbersome and can be used indoors only. A newly-developed wearable motion capture and measurement system with multiple sensors and ultrasound imaging was introduced in this study. Methods: In ten healthy participants, the changes in muscle area and activity of gastrocnemius, plantarflexion and dorsiflexion of right leg during walking were evaluated by the developed system and the Vicon system. The existence of significant changes in a gait cycle, comparison of the ankle kinetic data captured by the developed system and the Vicon system, and test-retest reliability (evaluated by the intraclass correlation coefficient, ICC) in each channel’s data captured by the developed system were examined. Results: Moderate to good test-retest reliability of various channels of the developed system (0.512 ≤ ICC ≤ 0.988, p < 0.05), significantly high correlation between the developed system and Vicon system in ankle joint angles (0.638R ≤ 0.707, p < 0.05), and significant changes in muscle activity of gastrocnemius during a gait cycle (p < 0.05) were found. Conclusion: A newly developed wearable motion capture and measurement system with ultrasound imaging that can accurately capture the motion of one leg was evaluated in this study, which paves the way towards real-time comprehensive evaluation of muscles and joint motions during different activities in both indoor and outdoor environments. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
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Open AccessReview An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images
Sensors 2019, 19(1), 194; https://doi.org/10.3390/s19010194
Received: 16 November 2018 / Revised: 28 December 2018 / Accepted: 31 December 2018 / Published: 7 January 2019
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Abstract
Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key [...] Read more.
Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Multiple Wire-Mesh Sensors Applied to the Characterization of Two-Phase Flow inside a Cyclonic Flow Distribution System
Sensors 2019, 19(1), 193; https://doi.org/10.3390/s19010193
Received: 25 November 2018 / Revised: 20 December 2018 / Accepted: 21 December 2018 / Published: 7 January 2019
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Abstract
Wire-mesh sensors are used to determine the phase fraction of gas–liquid two-phase flow in many industrial applications. In this paper, we report the use of the sensor to study the flow behavior inside an offshore oil and gas industry device for subsea phase [...] Read more.
Wire-mesh sensors are used to determine the phase fraction of gas–liquid two-phase flow in many industrial applications. In this paper, we report the use of the sensor to study the flow behavior inside an offshore oil and gas industry device for subsea phase separation. The study focused on the behavior of gas–liquid slug flow inside a flow distribution device with four outlets, which is part of the subsea phase separator system. The void fraction profile and the flow symmetry across the outlets were investigated using tomographic wire-mesh sensors and a camera. Results showed an ascendant liquid film in the cyclonic chamber with the gas phase at the center of the pipe generating a symmetrical flow. Dispersed bubbles coalesced into a gas vortex due to the centrifugal force inside the cyclonic chamber. The behavior favored the separation of smaller bubbles from the liquid bulk, which was an important parameter for gas-liquid separator sizing. The void fraction analysis of the outlets showed an even flow distribution with less than 10% difference, which was a satisfactorily result that may contribute to a reduction on the subsea gas–liquid separators size. From the outcomes of this study, detailed information regarding this type of flow distribution system was extracted. Thereby, wire-mesh sensors were successfully applied to investigate a new type of equipment for the offshore oil and gas industry. Full article
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Open AccessArticle Robust Entangled-Photon Ghost Imaging with Compressive Sensing
Sensors 2019, 19(1), 192; https://doi.org/10.3390/s19010192
Received: 7 December 2018 / Revised: 29 December 2018 / Accepted: 5 January 2019 / Published: 7 January 2019
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Abstract
This work experimentally demonstrates that the imaging quality of quantum ghost imaging (GI) with entangled photons can be significantly improved by properly handling the errors caused by the imperfection of optical devices. We also consider compressive GI to reduce the number of measurements [...] Read more.
This work experimentally demonstrates that the imaging quality of quantum ghost imaging (GI) with entangled photons can be significantly improved by properly handling the errors caused by the imperfection of optical devices. We also consider compressive GI to reduce the number of measurements and thereby the data acquisition time. The image reconstruction is formulated as a sparse total least square problem which is solved with an iterative algorithm. Our experiments show that, compared with existing methods, the new method can achieve a significant performance gain in terms of mean square error and peak signal–noise ratio. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessCorrection Correction: Yan, Y.; et al. A Dynamic Multi-Projection-Contour Approximating Framework for the 3D Reconstruction of Buildings by Super-Generalized Optical Stereo-Pairs. Sensors 2017, 17, 2153
Sensors 2019, 19(1), 191; https://doi.org/10.3390/s19010191
Received: 20 December 2018 / Accepted: 27 December 2018 / Published: 7 January 2019
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Abstract
The authors wish to make the following corrections to this paper [...] Full article
(This article belongs to the Section Remote Sensors)
Open AccessArticle Removal of Gross Artifacts of Transcranial Alternating Current Stimulation in Simultaneous EEG Monitoring
Sensors 2019, 19(1), 190; https://doi.org/10.3390/s19010190
Received: 19 September 2018 / Revised: 8 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
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Abstract
Transcranial electrical stimulation is a widely used non-invasive brain stimulation approach. To date, EEG has been used to evaluate the effect of transcranial Direct Current Stimulation (tDCS) and transcranial Alternating Current Stimulation (tACS), but most studies have been limited to exploring changes in [...] Read more.
Transcranial electrical stimulation is a widely used non-invasive brain stimulation approach. To date, EEG has been used to evaluate the effect of transcranial Direct Current Stimulation (tDCS) and transcranial Alternating Current Stimulation (tACS), but most studies have been limited to exploring changes in EEG before and after stimulation due to the presence of stimulation artifacts in the EEG data. This paper presents two different algorithms for removing the gross tACS artifact from simultaneous EEG recordings. These give different trade-offs in removal performance, in the amount of data required, and in their suitability for closed loop systems. Superposition of Moving Averages and Adaptive Filtering techniques are investigated, with significant emphasis on verification. We present head phantom testing results for controlled analysis, together with on-person EEG recordings in the time domain, frequency domain, and Event Related Potential (ERP) domain. The results show that EEG during tACS can be recovered free of large scale stimulation artifacts. Previous studies have not quantified the performance of the tACS artifact removal procedures, instead focusing on the removal of second order artifacts such as respiration related oscillations. We focus on the unresolved challenge of removing the first order stimulation artifact, presented with a new multi-stage validation strategy. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle Multi-Type Sensor Placements in Gaussian Spatial Fields for Environmental Monitoring
Sensors 2019, 19(1), 189; https://doi.org/10.3390/s19010189
Received: 30 November 2018 / Revised: 27 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
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Abstract
As citizens are increasingly concerned about the surrounding environment, it is important for modern cities to provide sufficient and accurate environmental information to the public for decision making in the era of smart cities. Due to the limited budget, we often need to [...] Read more.
As citizens are increasingly concerned about the surrounding environment, it is important for modern cities to provide sufficient and accurate environmental information to the public for decision making in the era of smart cities. Due to the limited budget, we often need to optimize the sensor placement in order to maximize the overall information gain according to certain criteria. Existing work is primarily concerned with single-type sensor placement; however, the environment usually requires accurate measurements of multiple types of environmental characteristics. In this paper, we focus on the optimal multi-type sensor placement in Gaussian spatial field for environmental monitoring. We study two representative cases: the one-with-all case when each station is equipped with all types of sensors and the general case when each station is equipped with at least one type of sensor. We propose two greedy algorithms accordingly, each with a provable approximation guarantee. We evaluated the proposed approach via an application in air quality monitoring scenario in Hong Kong and experimental results demonstrate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Selected Papers from ISC2 2018)
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Open AccessArticle Highly Sensitive Diode-Based Micro-Pirani Vacuum Sensor with Low Power Consumption
Sensors 2019, 19(1), 188; https://doi.org/10.3390/s19010188
Received: 27 November 2018 / Revised: 22 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
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Abstract
Micro-Pirani vacuum sensors usually operate at hundreds of microwatts, which limits their application in battery-powered sensor systems. This paper reports a diode-based, low power consumption micro-Pirani vacuum sensor that has high sensitivity. Optimizations to the micro-Pirani vacuum sensor were made regarding two aspects. [...] Read more.
Micro-Pirani vacuum sensors usually operate at hundreds of microwatts, which limits their application in battery-powered sensor systems. This paper reports a diode-based, low power consumption micro-Pirani vacuum sensor that has high sensitivity. Optimizations to the micro-Pirani vacuum sensor were made regarding two aspects. On the one hand, a greater temperature coefficient was obtained without increasing power consumption by taking advantage of series diodes; on the other hand, the sensor structure and geometries were redesigned to enlarge temperature variation. After that, the sensor was fabricated and tested. Test results indicated that the dynamic vacuum pressure range of the sensor was from 10−1 to 104 Pa when the forward bias current was as low as 10 μA with a power consumption of 50 μW. Average sensitivity was up to 90 μV/Pa and the sensitivity of unit power consumption increased to 1.8 V/W/Pa. In addition, the sensor could also work at a greater forward bias current for better sensor performance. Full article
(This article belongs to the Section Physical Sensors)
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