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Sensors, Volume 20, Issue 13 (July-1 2020) – 197 articles

Cover Story (view full-size image): Air quality monitors using low-cost, optical PM2.5 sensors can track the dispersion of wildfire smoke but adjustment factors (AFs) are needed for quantitative hazard assessment. This paper reports AFs for 4 monitors with low-cost sensors and 3 professional-grade photometers using data collected in a lab in Berkeley during the 2018 Camp Fire in California. Paired data from 53 PurpleAir PA-II devices and 12 nearby regulatory stations impacted by smoke from the Camp Fire and three others in California and Utah were analyzed to show variations of AF by location. A global AF can reduce bias from roughly a factor of two to 20–30% or less and improve the accuracy of Air Quality Index (AQI) hazard level reporting, e.g., from 14% to 84% correct in Sacramento during the Camp Fire. The adjustment factors reported in this paper enable the use of low-cost monitors for smoke hazard assessment. View [...] Read more.
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Article
Age-Related Differences in Fixation Pattern on a Companion Robot
Sensors 2020, 20(13), 3807; https://doi.org/10.3390/s20133807 - 07 Jul 2020
Cited by 2 | Viewed by 1097
Abstract
Recent studies have addressed the various benefits of companion robots and expanded the research scope to their design. However, the viewpoints of older adults have not been deeply investigated. Therefore, this study aimed to examine the distinctive viewpoints of older adults by comparing [...] Read more.
Recent studies have addressed the various benefits of companion robots and expanded the research scope to their design. However, the viewpoints of older adults have not been deeply investigated. Therefore, this study aimed to examine the distinctive viewpoints of older adults by comparing them with those of younger adults. Thirty-one older and thirty-one younger adults participated in an eye-tracking experiment to investigate their impressions of a bear-like robot mockup. They also completed interviews and surveys to help us understand their viewpoints on the robot design. The gaze behaviors and the impressions of the two groups were significantly different. Older adults focused significantly more on the robot’s face and paid little attention to the rest of the body. In contrast, the younger adults gazed at more body parts and viewed the robot in more detail than the older adults. Furthermore, the older adults rated physical attractiveness and social likeability of the robot significantly higher than the younger adults. The specific gaze behavior of the younger adults was linked to considerable negative feedback on the robot design. Based on these empirical findings, we recommend that impressions of older adults be considered when designing companion robots. Full article
(This article belongs to the Special Issue Human-Robot Interaction and Sensors for Social Robotics)
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Article
Quantized Residual Preference Based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting
Sensors 2020, 20(13), 3806; https://doi.org/10.3390/s20133806 - 07 Jul 2020
Cited by 2 | Viewed by 818
Abstract
In this paper, quantized residual preference is proposed to represent the hypotheses and the points for model selection and inlier segmentation in multi-structure geometric model fitting. First, a quantized residual preference is proposed to represent the hypotheses. Through a weighted similarity measurement and [...] Read more.
In this paper, quantized residual preference is proposed to represent the hypotheses and the points for model selection and inlier segmentation in multi-structure geometric model fitting. First, a quantized residual preference is proposed to represent the hypotheses. Through a weighted similarity measurement and linkage clustering, similar hypotheses are put into one cluster, and hypotheses with good quality are selected from the clusters as the model selection results. After this, the quantized residual preference is also used to present the data points, and through the linkage clustering, the inliers belonging to the same model can be separated from the outliers. To exclude outliers as many as possible, an iterative sampling and clustering process is performed within the clustering process until the clusters are stable. The experiments undertake indicate that the proposed method performs even better on real data than the some state-of-the-art methods. Full article
(This article belongs to the Section Remote Sensors)
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Letter
Application of Inertial Motion Unit-Based Kinematics to Assess the Effect of Boot Modifications on Ski Jump Landings—A Methodological Study
Sensors 2020, 20(13), 3805; https://doi.org/10.3390/s20133805 - 07 Jul 2020
Cited by 1 | Viewed by 820
Abstract
Biomechanical studies of winter sports are challenging due to environmental conditions which cannot be mimicked in a laboratory. In this study, a methodological approach was developed merging 2D video recordings with sensor-based motion capture to investigate ski jump landings. A reference measurement was [...] Read more.
Biomechanical studies of winter sports are challenging due to environmental conditions which cannot be mimicked in a laboratory. In this study, a methodological approach was developed merging 2D video recordings with sensor-based motion capture to investigate ski jump landings. A reference measurement was carried out in a laboratory, and subsequently, the method was exemplified in a field study by assessing the effect of a ski boot modification on landing kinematics. Landings of four expert skiers were filmed under field conditions in the jump plane, and full body kinematics were measured with an inertial motion unit (IMU) -based motion capture suit. This exemplary study revealed that the combination of video and IMU data is viable. However, only one skier was able to make use of the added boot flexibility, likely due to an extended training time with the modified boot. In this case, maximum knee flexion changed by 36° and maximum ankle flexion by 13°, whereas the other three skiers changed only marginally. The results confirm that 2D video merged with IMU data are suitable for jump analyses in winter sports, and that the modified boot will allow for alterations in landing technique provided that enough time for training is given. Full article
(This article belongs to the Section Wearables)
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Article
A Novel Just-in-Time Learning Strategy for Soft Sensing with Improved Similarity Measure Based on Mutual Information and PLS
Sensors 2020, 20(13), 3804; https://doi.org/10.3390/s20133804 - 07 Jul 2020
Cited by 4 | Viewed by 733
Abstract
In modern industrial process control, just-in-time learning (JITL)-based soft sensors have been widely applied. An accurate similarity measure is crucial in JITL-based soft sensor modeling since it is not only the basis for selecting the nearest neighbor samples but also determines sample weights. [...] Read more.
In modern industrial process control, just-in-time learning (JITL)-based soft sensors have been widely applied. An accurate similarity measure is crucial in JITL-based soft sensor modeling since it is not only the basis for selecting the nearest neighbor samples but also determines sample weights. In recent years, JITL similarity measure methods have been greatly enriched, including methods based on Euclidean distance, weighted Euclidean distance, correlation, etc. However, due to the different influence of input variables on output, the complex nonlinear relationship between input and output, the collinearity between input variables, and other complex factors, the above similarity measure methods may become inaccurate. In this paper, a new similarity measure method is proposed by combining mutual information (MI) and partial least squares (PLS). A two-stage calculation framework, including a training stage and a prediction stage, was designed in this study to reduce the online computational burden. In the prediction stage, to establish the local model, an improved locally weighted PLS (LWPLS) with variables and samples double-weighted was adopted. The above operations constitute a novel JITL modeling strategy, which is named MI-PLS-LWPLS. By comparison with other related JITL methods, the effectiveness of the MI-PLS-LWPLS method was verified through case studies on both a synthetic Friedman dataset and a real industrial dataset. Full article
(This article belongs to the Section Intelligent Sensors)
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Article
Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks
Sensors 2020, 20(13), 3803; https://doi.org/10.3390/s20133803 - 07 Jul 2020
Cited by 2 | Viewed by 1373
Abstract
With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context [...] Read more.
With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train Deep Convolutional Neural Network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy. Full article
(This article belongs to the Special Issue Smart Mobile and Sensor Systems)
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Article
A Maze Matrix-Based Secret Image Sharing Scheme with Cheater Detection
Sensors 2020, 20(13), 3802; https://doi.org/10.3390/s20133802 - 07 Jul 2020
Cited by 3 | Viewed by 947
Abstract
Secret image sharing is a technique for sharing a secret message in such a fashion that stego image shadows are generated and distributed to individual participants. Without the complete set of shadows shared among all participants, the secret could not be deciphered. This [...] Read more.
Secret image sharing is a technique for sharing a secret message in such a fashion that stego image shadows are generated and distributed to individual participants. Without the complete set of shadows shared among all participants, the secret could not be deciphered. This technique may serve as a crucial means for protecting private data in massive Internet of things applications. This can be realized by distributing the stego image shadows to different devices on the Internet so that only the ones who are authorized to access these devices can extract the secret message. In this paper, we proposed a secret image sharing scheme based on a novel maze matrix. A pair of image shadows were produced by hiding secret data into two distinct cover images under the guidance of the maze matrix. A two-layered cheat detection mechanism was devised based on the special characteristics of the proposed maze matrix. In addition to the conventional joint cheating detection, the proposed scheme was able to identify the tampered shadow presented by a cheater without the information from other shadows. Furthermore, in order to improve time efficiency, we derived a pair of Lagrange polynomials to compute the exact pixel values of the shadow images instead of resorting to time-consuming and computationally expensive conventional searching strategies. Experimental results demonstrated the effectiveness and efficiency of the proposed secret sharing scheme and cheat detection mechanism. Full article
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Editorial
InSAR Signal and Data Processing
Sensors 2020, 20(13), 3801; https://doi.org/10.3390/s20133801 - 07 Jul 2020
Viewed by 717
Abstract
We present here the recent advances in exploring new techniques related to interferometric synthetic aperture radar (InSAR) signal and data processing and applications. Full article
(This article belongs to the Special Issue InSAR Signal and Data Processing)
Article
Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks
Sensors 2020, 20(13), 3800; https://doi.org/10.3390/s20133800 - 07 Jul 2020
Viewed by 705
Abstract
Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the [...] Read more.
Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows. Full article
(This article belongs to the Section Physical Sensors)
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Article
Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera
Sensors 2020, 20(13), 3799; https://doi.org/10.3390/s20133799 - 07 Jul 2020
Cited by 1 | Viewed by 725
Abstract
In this study, an image registration algorithm was applied to calculate the rotation angle of objects when matching images. Some commonly used image feature detection algorithms such as features from accelerated segment test (FAST), speeded up robust features (SURF) and maximally stable extremal [...] Read more.
In this study, an image registration algorithm was applied to calculate the rotation angle of objects when matching images. Some commonly used image feature detection algorithms such as features from accelerated segment test (FAST), speeded up robust features (SURF) and maximally stable extremal regions (MSER) algorithms were chosen as feature extraction components. Comparing the running time and accuracy, the image registration algorithm based on SURF has better performance than the other algorithms. Accurately obtaining the roll angle is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. To acquire the roll angle of agriculture machinery, a roll angle acquisition model based on the image registration algorithm was built. Then, the performance of the model with a monocular camera was tested in the field. The field test showed that the average error of the rolling angle was 0.61°, while the minimum error was 0.08°. The field test indicated that the model could accurately obtain the attitude change trend of agricultural machinery when it was working in irregular farmlands. The model described in this paper could provide a foundation for agricultural equipment navigation and autonomous driving. Full article
(This article belongs to the Special Issue Camera as a Smart-Sensor (CaaSS))
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Article
Development of a High Precision Telescopic Instrument Based on Simultaneous Laser Multilateration for Machine Tool Volumetric Verification
Sensors 2020, 20(13), 3798; https://doi.org/10.3390/s20133798 - 07 Jul 2020
Viewed by 675
Abstract
This paper presents the design of a high precision telescopic system consisting in three lines, with measuring principle based on simultaneous laser multilateration. The system offers the high precision of the interferometer systems and allows the autonomous tracking of a sphere joined to [...] Read more.
This paper presents the design of a high precision telescopic system consisting in three lines, with measuring principle based on simultaneous laser multilateration. The system offers the high precision of the interferometer systems and allows the autonomous tracking of a sphere joined to the spindle nose of the machine tool by simultaneous contact of all the lines. The main advantage of the system is that it allows data capture to be carried out in a single cycle thanks to simultaneous operation with at least three telescopic arms using a novel multipoint kinematic coupling. This results in a significant reduction of the time taken for data capture and improves measurement accuracy due to avoiding the effect of temperature variations between cycles and machine tool repeatability. The work explains the working principle of the system, its main components, and the design parameters considered for the development of the system. The system is simple to operate, compact, agile, and suitable for the verification of small- or medium-sized machine tools with linear and/or rotary axes. Full article
(This article belongs to the Special Issue Sensors for Manufacturing Process Monitoring)
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Letter
Laboratory Testing of FBGs for Pipeline Monitoring
Sensors 2020, 20(13), 3797; https://doi.org/10.3390/s20133797 - 07 Jul 2020
Viewed by 738
Abstract
The monitoring of the effects of geohazards on pipelines can be addressed by optical fiber Bragg gratings (FBGs). They are sensitive to strain and bending, and are installed on the external surface of pipelines at discrete locations. A joint approach of theoretical analysis [...] Read more.
The monitoring of the effects of geohazards on pipelines can be addressed by optical fiber Bragg gratings (FBGs). They are sensitive to strain and bending, and are installed on the external surface of pipelines at discrete locations. A joint approach of theoretical analysis and laboratory experiments is useful to check the reliability of the performance of this technology. We focus on the theoretical analysis of pipeline buckling and investigate the reliability of FBG monitoring both by examining the analytical model available and by performing a laboratory-scale experiment. The novelty lies in the analysis of models and methods originally developed for the detection of pipeline upheaval buckling caused by externally imposed forces in the context of service loads (temperature). Although thermal strain is very relevant in view of its potentially disruptive effects on both pipelines and the FBG response, it has not been yet fully investigated. We point out the merits of the approach, such as the functionality and simplicity of design, the accessibility and inexpensiveness of materials, the controllability and repeatability of processes, the drawbacks are also described, such as temperature effects, the problem of slipping of gages and the challenge of performing quasi-distributed strain measurements. Full article
(This article belongs to the Special Issue Development and Applications of Optical Sensing)
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Article
3D Contact Position Estimation of Image-Based Areal Soft Tactile Sensor with Printed Array Markers and Image Sensors
Sensors 2020, 20(13), 3796; https://doi.org/10.3390/s20133796 - 07 Jul 2020
Cited by 1 | Viewed by 976
Abstract
Tactile sensors have been widely used and researched in various fields of medical and industrial applications. Gradually, they will be used as new input devices and contact sensors for interactive robots. If a tactile sensor is to be applied to various forms of [...] Read more.
Tactile sensors have been widely used and researched in various fields of medical and industrial applications. Gradually, they will be used as new input devices and contact sensors for interactive robots. If a tactile sensor is to be applied to various forms of human–machine interactions, it needs to be soft to ensure comfort and safety, and it should be easily customizable and inexpensive. The purpose of this study is to estimate 3D contact position of a novel image-based areal soft tactile sensor (IASTS) using printed array markers and multiple cameras. First, we introduce the hardware structure of the prototype IASTS, which consists of a soft material with printed array markers and multiple cameras with LEDs. Second, an estimation algorithm for the contact position is proposed based on the image processing of the array markers and their Gaussian fittings. A series of basic experiments was conducted and their results were analyzed to verify the effectiveness of the proposed IASTS hardware and its estimation software. To ensure the stability of the estimated contact positions a Kalman filter was developed. Finally, it was shown that the contact positions on the IASTS were estimated with a reasonable error value for soft haptic applications. Full article
(This article belongs to the Special Issue Intelligent Sensors and Computer Vision)
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Article
Detecting and Tracking Criminals in the Real World through an IoT-Based System
Sensors 2020, 20(13), 3795; https://doi.org/10.3390/s20133795 - 07 Jul 2020
Cited by 2 | Viewed by 1177
Abstract
Criminals and related illegal activities represent problems that are neither trivial to predict nor easy to handle once they are identified. The Police Forces (PFs) typically base their strategies solely on their intra-communication, by neglecting the involvement of third parties, such as the [...] Read more.
Criminals and related illegal activities represent problems that are neither trivial to predict nor easy to handle once they are identified. The Police Forces (PFs) typically base their strategies solely on their intra-communication, by neglecting the involvement of third parties, such as the citizens, in the investigation chain which results in a lack of timeliness among the occurrence of the criminal event, its identification, and intervention. In this regard, a system based on IoT social devices, for supporting the detection and tracking of criminals in the real world, is proposed. It aims to enable the communication and collaboration between citizens and PFs in the criminal investigation process by combining app-based technologies and embracing the advantages of an Edge-based architecture in terms of responsiveness, energy saving, local data computation, and distribution, along with information sharing. The proposed model as well as the algorithms, defined on the top of it, have been evaluated through a simulator for showing the logic of the system functioning, whereas the functionality of the app was assessed through a user study conducted upon a group of 30 users. Finally, the additional advantage in terms of intervention time was compared to statistical results. Full article
(This article belongs to the Special Issue Sensors and Smart Devices at the Edge: IoT Meets Edge Computing)
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Article
A QoE-Aware Energy Supply Scheme over a FiWi Access Network in the 5G Era
Sensors 2020, 20(13), 3794; https://doi.org/10.3390/s20133794 - 07 Jul 2020
Cited by 1 | Viewed by 845
Abstract
Integrated fiber-wireless (FiWi) should be regarded as a promising access network architecture in future 5G networks, and beyond; this due to its seamless combination of flexibility, ubiquity, mobility of the wireless mesh network (WMN) frontend with a large capacity, high bandwidth, strong robustness [...] Read more.
Integrated fiber-wireless (FiWi) should be regarded as a promising access network architecture in future 5G networks, and beyond; this due to its seamless combination of flexibility, ubiquity, mobility of the wireless mesh network (WMN) frontend with a large capacity, high bandwidth, strong robustness in time, and a wavelength-division multiplexed passive optical network (TWDM-PON) backhaul. However, the key issue in both traditional human-to-human (H2H) traffic and emerging Tactile Internet is the energy conservation network operation. Therefore, a power-saving method should be instrumental in the wireless retransmission-enabled architecture design. Toward this end, this paper firstly proposes a novel energy-supply paradigm of the FiWi converged network infrastructure, i.e., the emerging power over fiber (PoF) technology instead of an external power supply. Then, the existing time-division multiplexing access (TDMA) scheme and PoF technology are leveraged to carry out joint dynamic bandwidth allocation (DBA) and provide enough power for the sleep schedule in each integrated optical network unit mesh portal point (ONU-MPP) branch. Additionally, the correlation between the transmitted optical power of the optical line terminal (OLT) and the quality of experience (QoE) guarantee caused by multiple hops in the wireless frontend is taken into consideration in detail. The research results prove that the envisioned paradigm can significantly reduce the energy consumption of the whole FiWi system while satisfying the average delay constraints, thus providing enough survivability for multimode optical fiber. Full article
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Letter
Use of an Artificial Miniaturized Enzyme in Hydrogen Peroxide Detection by Chemiluminescence
Sensors 2020, 20(13), 3793; https://doi.org/10.3390/s20133793 - 06 Jul 2020
Cited by 5 | Viewed by 1650
Abstract
Advanced oxidation processes represent a viable alternative in water reclamation for potable reuse. Sensing methods of hydrogen peroxide are, therefore, needed to test both process progress and final quality of the produced water. Several bio-based assays have been developed so far, mainly relying [...] Read more.
Advanced oxidation processes represent a viable alternative in water reclamation for potable reuse. Sensing methods of hydrogen peroxide are, therefore, needed to test both process progress and final quality of the produced water. Several bio-based assays have been developed so far, mainly relying on peroxidase enzymes, which have the advantage of being fast, efficient, reusable, and environmentally safe. However, their production/purification and, most of all, batch-to-batch consistency may inherently prevent their standardization. Here, we provide evidence that a synthetic de novo miniaturized designed heme-enzyme, namely Mimochrome VI*a, can be proficiently used in hydrogen peroxide assays. Furthermore, a fast and automated assay has been developed by using a lab-bench microplate reader. Under the best working conditions, the assay showed a linear response in the 10.0–120 μM range, together with a second linearity range between 120 and 500 μM for higher hydrogen peroxide concentrations. The detection limit was 4.6 μM and quantitation limits for the two datasets were 15.5 and 186 μM, respectively. In perspective, Mimochrome VI*a could be used as an active biological sensing unit in different sensor configurations. Full article
(This article belongs to the Special Issue Advances in Optical, Fluorescent and Luminescent Biosensors)
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Article
Local Seeing Measurement for Increasing Astrophysical Observatory Quality Images Using an Autonomous Wireless Sensor Network
Sensors 2020, 20(13), 3792; https://doi.org/10.3390/s20133792 - 06 Jul 2020
Viewed by 876
Abstract
Astrophysical observatories (AOs) are used to acquire high-quality images from the sky. However, AOs are amenable to distortion phenomena such as seeing. In this paper, we consider specifically the local seeing produced from indoor and outdoor temperature variations. Local seeing contributes to the [...] Read more.
Astrophysical observatories (AOs) are used to acquire high-quality images from the sky. However, AOs are amenable to distortion phenomena such as seeing. In this paper, we consider specifically the local seeing produced from indoor and outdoor temperature variations. Local seeing contributes to the captured image quality, therefore it must be measured. Local seeing has been considered, to the best of our knowledge, in observatories using ad hoc solutions, typically with high cost and complexity. This paper presents the complete development of an autonomous wireless sensor network (WSN) composed of temperature-measuring for real-time local seeing measurement. Therefore, a WSN is deployed using Zigbee as a data communication exchange. As a result, a long continuous-operating system is constructed and tested in a real AO infrastructure. Finally, we calculate a preliminary dome local seeing, from the acquired temperature data, achieving reasonable results. Full article
(This article belongs to the Section Sensor Networks)
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Article
Space State Representation Product Evaluation in Satellite Position and Receiver Position Domain
Sensors 2020, 20(13), 3791; https://doi.org/10.3390/s20133791 - 06 Jul 2020
Cited by 1 | Viewed by 766
Abstract
In Global Navigation Satellite Systems (GNSS) positioning, important terms in error budget are satellite orbits and satellite clocks correction errors. International services are developing and providing models and correction to minimize the influence of these errors both in post-processing and real-time applications. The [...] Read more.
In Global Navigation Satellite Systems (GNSS) positioning, important terms in error budget are satellite orbits and satellite clocks correction errors. International services are developing and providing models and correction to minimize the influence of these errors both in post-processing and real-time applications. The International GNSS Service (IGS) Real-Time Service (RTS) provides real-time orbits and clock corrections for the broadcast ephemeris. Real-time products provided by IGS are generated by different analysis centres using different algorithms. In this paper, four RTS products—IGC01, CLK01, CLK50, and CLK90—were evaluated and analysed. To evaluate State Space Representation (SSR) products’ GPS satellites, the analyses were made in three variants. In the first approach, geocentric real-time Satellite Vehicle (SV) coordinates and clock corrections were calculated. The obtained results were compared with the final IGS, ESA, GFZ, and GRG ephemerides. The second approach was to use the corrected satellite positions and clock corrections to determine the Precise Point Position (PPP) of the receiver. In the third analysis, the impact of SSR corrections on receiver Single Point Position (SPP) was evaluated. The first part of the research showed that accuracy of the satellite position is better than 10 cm (average 3 to 5 cm), while in the case of clock corrections, mean residuals range from 2 cm to 17 cm. It should be noted that the errors of the satellites positions obtained from one stream differ depending on the reference data used. This shows the need for an evaluation of correction streams in the domain of the receiver position. In the case of PPP in a kinematic mode, the tests allowed to determine the impact that the use of different streams has on the final positioning results. These studies showed differences between specific streams, which could not be seen in the first study. The best results (3D RMS at 0.13 m level) were obtained for the CLK90 stream, while for IGC01, the results were three times worse. The SPP tests clearly indicate that regardless of the selected SSR stream, one can see a significant improvement in positioning accuracy as compared to positioning results using only broadcast ephemeris. Full article
(This article belongs to the Special Issue GNSS Signals and Sensors)
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Article
Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features
Sensors 2020, 20(13), 3790; https://doi.org/10.3390/s20133790 - 06 Jul 2020
Cited by 10 | Viewed by 1166
Abstract
Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of [...] Read more.
Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals. Full article
(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)
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Article
Optimizing Irradiation Geometry in LED-Based Photoacoustic Imaging with 3D Printed Flexible and Modular Light Delivery System
Sensors 2020, 20(13), 3789; https://doi.org/10.3390/s20133789 - 06 Jul 2020
Cited by 3 | Viewed by 1111
Abstract
Photoacoustic (PA) imaging–a technique combining the ability of optical imaging to probe functional properties of the tissue and deep structural imaging ability of ultrasound–has gained significant popularity in the past two decades for its utility in several biomedical applications. More recently, light-emitting diodes [...] Read more.
Photoacoustic (PA) imaging–a technique combining the ability of optical imaging to probe functional properties of the tissue and deep structural imaging ability of ultrasound–has gained significant popularity in the past two decades for its utility in several biomedical applications. More recently, light-emitting diodes (LED) are being explored as an alternative to bulky and expensive laser systems used in PA imaging for their portability and low-cost. Due to the large beam divergence of LEDs compared to traditional laser beams, it is imperative to quantify the angular dependence of LED-based illumination and optimize its performance for imaging superficial or deep-seated lesions. A custom-built modular 3-D printed hinge system and tissue-mimicking phantoms with various absorption and scattering properties were used in this study to quantify the angular dependence of LED-based illumination. We also experimentally calculated the source divergence of the pulsed-LED arrays to be 58° ± 8°. Our results from point sources (pencil lead phantom) in non-scattering medium obey the cotangential relationship between the angle of irradiation and maximum PA intensity obtained at various imaging depths, as expected. Strong dependence on the angle of illumination at superficial depths (−5°/mm at 10 mm) was observed that becomes weaker at intermediate depths (−2.5°/mm at 20 mm) and negligible at deeper locations (−1.1°/mm at 30 mm). The results from the tissue-mimicking phantom in scattering media indicate that angles between 30–75° could be used for imaging lesions at various depths (12 mm–28 mm) where lower LED illumination angles (closer to being parallel to the imaging plane) are preferable for deep tissue imaging and superficial lesion imaging is possible with higher LED illumination angles (closer to being perpendicular to the imaging plane). Our results can serve as a priori knowledge for the future LED-based PA system designs employed for both preclinical and clinical applications. Full article
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Article
ACE: ARIA-CTR Encryption for Low-End Embedded Processors
Sensors 2020, 20(13), 3788; https://doi.org/10.3390/s20133788 - 06 Jul 2020
Cited by 3 | Viewed by 761
Abstract
In this paper, we present the first optimized implementation of ARIA block cipher on low-end 8-bit Alf and Vegard’s RISC processor (AVR) microcontrollers. To achieve high-speed implementation, primitive operations, including rotation operation, a substitute layer, and a diffusion layer, are carefully optimized for [...] Read more.
In this paper, we present the first optimized implementation of ARIA block cipher on low-end 8-bit Alf and Vegard’s RISC processor (AVR) microcontrollers. To achieve high-speed implementation, primitive operations, including rotation operation, a substitute layer, and a diffusion layer, are carefully optimized for the target low-end embedded processor. The proposed ARIA implementation supports the electronic codebook (ECB) and the counter (CTR) modes of operation. In particular, the CTR mode of operation is further optimized with the pre-computed table of two add-round-key, one substitute layer, and one diffusion layer operations. Finally, the proposed ARIA-CTR implementations on 8-bit AVR microcontrollers achieved 187.1, 216.8, and 246.6 clock cycles per byte for 128-bit, 192-bit, and 256-bit security levels, respectively. Compared with previous reference implementations, the execution timing is improved by 69.8%, 69.6%, and 69.5% for 128-bit, 192-bit, and 256-bit security levels, respectively. Full article
(This article belongs to the Special Issue Cryptography and Information Security in Wireless Sensor Networks)
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Article
Foot-Mounted Pedestrian Navigation Method by Comparing ADR and Modified ZUPT Based on MEMS IMU Array
Sensors 2020, 20(13), 3787; https://doi.org/10.3390/s20133787 - 06 Jul 2020
Cited by 1 | Viewed by 875
Abstract
Using an MEMS Inertial Measurement Unit (MEMS IMU) array mounted on foot is a feasible approach to improve the pedestrian tracking accuracy for the pedestrian navigation system (PNS). Based on the in-house developed IMU array, the paper proposes a new integrated framework that [...] Read more.
Using an MEMS Inertial Measurement Unit (MEMS IMU) array mounted on foot is a feasible approach to improve the pedestrian tracking accuracy for the pedestrian navigation system (PNS). Based on the in-house developed IMU array, the paper proposes a new integrated framework that combines adaptive deck reckoning (ADR) with the modified zero velocity update (ZUPT). In the proposed ADR algorithm, the IMUs with large drift errors on the array are selected and removed according to the step length and the track angle computed by each IMU on the array. Then, by using the step length and the track angle of each step computed by remaining IMUs, the foot position extracted from the traditional ZUPT model is estimated on the basis of least squares (LS) so as to improve the traveled distance calculation accuracy. Compared with the traditional IMU array fusion method based on a maximum likelihood estimator (MLE) when it is used in the PNS, which is approximately taking the mean value of array readings, the proposed method is equivalent to adaptively fusing the array readings and thus improves the pedestrian tracking accuracy. To compare the proposed method with MLE, two different types of walking tracks are designed. The 161 m straight line experiments show that the end position by ADR/modified ZUPT method is much closer to the one of the reference trajectory compared with the MLE in repeated walks, and the closed-loop tracks about 300 m show that the positioning error with respect to the total traveled distance is less than 0.6% (1σ), which is higher than 1% (1σ) of MLE. Full article
(This article belongs to the Section Sensor Networks)
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Article
Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker’s Age in Construction Site
Sensors 2020, 20(13), 3786; https://doi.org/10.3390/s20133786 - 06 Jul 2020
Cited by 2 | Viewed by 1399
Abstract
It is important for construction companies to sustain a productive workforce without sacrificing its health and safety. This study aims to develop a practical judgement method to estimate the workload risk of individual construction workers. Based on studies, we developed a workload model [...] Read more.
It is important for construction companies to sustain a productive workforce without sacrificing its health and safety. This study aims to develop a practical judgement method to estimate the workload risk of individual construction workers. Based on studies, we developed a workload model comprising a hygrothermal environment, behavioral information, and the physical characteristics of workers). The construction workers’ heart rate and physical activity were measured using the data collected from a wearable device equipped with a biosensor and an acceleration sensor. This study is the first report to use worker physical activity, age, and the wet bulb globe temperature (WBGT) to determine a worker’s physical workload. The accuracy of this health risk judgment result was 89.2%, indicating that it is possible to easily judge the health risk of workers even in an environment where it is difficult to measure the subject in advance. The proposed model and its findings can aid in monitoring the health impacts of working conditions during construction activities, and thereby contribute toward determining workers’ health damage. However, the sampled construction workers are 12 workers, further studies in other working conditions are required to accumulate more evidence and assure the accuracy of the models. Full article
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Article
Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision
Sensors 2020, 20(13), 3785; https://doi.org/10.3390/s20133785 - 06 Jul 2020
Cited by 6 | Viewed by 1421
Abstract
Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using [...] Read more.
Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection. Full article
(This article belongs to the Collection Robotics, Sensors and Industry 4.0)
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Article
Understanding Smartwatch Battery Utilization in the Wild
Sensors 2020, 20(13), 3784; https://doi.org/10.3390/s20133784 - 06 Jul 2020
Cited by 3 | Viewed by 1236
Abstract
Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches [...] Read more.
Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization. Full article
(This article belongs to the Section Physical Sensors)
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Article
Viewpoint Analysis for Maturity Classification of Sweet Peppers
Sensors 2020, 20(13), 3783; https://doi.org/10.3390/s20133783 - 06 Jul 2020
Cited by 1 | Viewed by 1191
Abstract
The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue [...] Read more.
The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant. Full article
(This article belongs to the Section Optical Sensors)
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Article
Architecture for Trajectory-Based Fishing Ship Classification with AIS Data
Sensors 2020, 20(13), 3782; https://doi.org/10.3390/s20133782 - 06 Jul 2020
Cited by 3 | Viewed by 1441
Abstract
This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in [...] Read more.
This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information. Full article
(This article belongs to the Special Issue Information Fusion and Machine Learning for Sensors)
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Article
Counting Crowds with Perspective Distortion Correction via Adaptive Learning
Sensors 2020, 20(13), 3781; https://doi.org/10.3390/s20133781 - 06 Jul 2020
Cited by 1 | Viewed by 712
Abstract
The goal of crowd counting is to estimate the number of people in the image. Presently, use regression to count people number became a mainstream method. It is worth noting that, with the development of convolutional neural networks (CNN), methods that are based [...] Read more.
The goal of crowd counting is to estimate the number of people in the image. Presently, use regression to count people number became a mainstream method. It is worth noting that, with the development of convolutional neural networks (CNN), methods that are based on CNN have become a research hotspot. It is a more interesting topic that how to locate the site of the person in the image than simply predicting the number of people in the image. The perspective transformation present is still a challenge, because perspective distortion will cause differences in the size of the crowd in the image. To devote perspective distortion and locate the site of the person more accuracy, we design a novel framework named Adaptive Learning Network (CAL). We use the VGG as the backbone. After each pooling layer is output, we collect the 1/2, 1/4, 1/8, and 1/16 features of the original image and combine them with the weights learned by an adaptive learning branch. The object of our adaptive learning branch is each image in the datasets. By combining the output features of different sizes of each image, the challenge of drastic changes in the size of the image crowd due to perspective transformation is reduced. We conducted experiments on four population counting data sets (i.e., ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF), and the results show that our model has a good performance. Full article
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Article
Improving Object Tracking by Added Noise and Channel Attention
Sensors 2020, 20(13), 3780; https://doi.org/10.3390/s20133780 - 06 Jul 2020
Cited by 2 | Viewed by 921
Abstract
CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the [...] Read more.
CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers. Full article
(This article belongs to the Section Intelligent Sensors)
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Article
Sensor Modeling and Calibration Method Based on Extinction Ratio Error for Camera-Based Polarization Navigation Sensor
Sensors 2020, 20(13), 3779; https://doi.org/10.3390/s20133779 - 06 Jul 2020
Cited by 3 | Viewed by 841
Abstract
The performance of camera-based polarization sensors largely depends on the estimated model parameters obtained through calibration. Limited by manufacturing processes, the low extinction ratio and inconsistency of the polarizer can reduce the measurement accuracy of the sensor. To account for the challenges, one [...] Read more.
The performance of camera-based polarization sensors largely depends on the estimated model parameters obtained through calibration. Limited by manufacturing processes, the low extinction ratio and inconsistency of the polarizer can reduce the measurement accuracy of the sensor. To account for the challenges, one extinction ratio coefficient was introduced into the calibration model to unify the light intensity of two orthogonal channels. Since the introduced extinction ratio coefficient is associated with degree of polarization (DOP), a new calibration method considering both azimuth of polarization (AOP) error and DOP error for the bionic camera-based polarization sensor was proposed to improve the accuracy of the calibration model parameter estimation. To evaluate the performance of the proposed camera-based polarization calibration model using the new calibration method, both indoor and outdoor calibration experiments were carried out. It was found that the new calibration method for the proposed calibration model could achieve desirable performance in terms of stability and robustness of the calculated AOP and DOP values. Full article
(This article belongs to the Section Physical Sensors)
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Article
Health Promotion for Childhood Obesity: An Approach Based on Self-Tracking of Data
Sensors 2020, 20(13), 3778; https://doi.org/10.3390/s20133778 - 06 Jul 2020
Cited by 3 | Viewed by 1284
Abstract
At present, obesity and overweight are a global health epidemic. Traditional interventions for promoting healthy habits do not appear to be effective. However, emerging technological solutions based on wearables and mobile devices can be useful in promoting healthy habits. These applications generate a [...] Read more.
At present, obesity and overweight are a global health epidemic. Traditional interventions for promoting healthy habits do not appear to be effective. However, emerging technological solutions based on wearables and mobile devices can be useful in promoting healthy habits. These applications generate a considerable amount of tracked activity data. Consequently, our approach is based on the quantified-self model for recommending healthy activities. Gamification can also be used as a mechanism to enhance personalization, increasing user motivation. This paper describes the quantified-self model and its data sources, the activity recommender system, and the PROVITAO App user experience model. Furthermore, it presents the results of a gamified program applied for three years in children with obesity and the process of evaluating the quantified-self model with experts. Positive outcomes were obtained in children’s medical parameters and health habits. Full article
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