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Sensors, Volume 20, Issue 20 (October-2 2020) – 241 articles

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Cover Story (view full-size image) The development of a rapid and point-of-care diagnostic sensor for SARS-CoV-2 screening is [...] Read more.
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Open AccessArticle
Planetary-Scale Geospatial Open Platform Based on the Unity3D Environment
Sensors 2020, 20(20), 5967; https://doi.org/10.3390/s20205967 - 21 Oct 2020
Viewed by 332
Abstract
Digital twin technology based on building a virtual digital city similar to a real one enables the simulation of urban phenomena or the design of a city. A geospatial platform is an essential supporting component of digital twin cities. In this study, we [...] Read more.
Digital twin technology based on building a virtual digital city similar to a real one enables the simulation of urban phenomena or the design of a city. A geospatial platform is an essential supporting component of digital twin cities. In this study, we propose a planetary-scale geospatial open platform that can be used easily in the most widely used game engine environment. The proposed platform can visualize large-capacity geospatial data in real time because it organizes and manages various types of data based on quadtree tiles. The proposed rendering tile decision method provides constant geospatial data visualization according to the camera controls of the user. The platform implemented is based on Unity3D, and therefore, one can use it easily by importing the proposed asset library. The proposed geospatial platform is available on the Asset Store. We believe that the proposed platform can meet the needs of various three-dimensional (3-D) geospatial applications. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle
SAR Target Recognition via Meta-Learning and Amortized Variational Inference
Sensors 2020, 20(20), 5966; https://doi.org/10.3390/s20205966 - 21 Oct 2020
Viewed by 286
Abstract
The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that [...] Read more.
The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessLetter
Wireless Sensing of Concrete Setting Process
Sensors 2020, 20(20), 5965; https://doi.org/10.3390/s20205965 - 21 Oct 2020
Viewed by 262
Abstract
An RFID-based wireless system to measure the evolution of the setting process of cement-based materials is presented in this paper. The system consists of a wireless RFID temperature sensor that works embedded in concrete, and an external RFID reader that communicates with the [...] Read more.
An RFID-based wireless system to measure the evolution of the setting process of cement-based materials is presented in this paper. The system consists of a wireless RFID temperature sensor that works embedded in concrete, and an external RFID reader that communicates with the embedded sensor to extract the temperature measurement conducted by the embedded sensor. Temperature time evolution is a well known proxy to monitor the setting process of concrete. The RFID sensor consisting of an UWB Bow Tie antenna with central frequency 868 MHz, matched to the EM4325 temperature chip through a T-match structure for embedded operation inside concrete is fully characterized. Results for measurements of the full set up conducted in a real-scenario are provided. Full article
(This article belongs to the Section Electronic Sensors)
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Open AccessArticle
Robust Baseline-Free Damage Localization by Using Locally Perturbed Dynamic Equilibrium and Data Fusion Technique
Sensors 2020, 20(20), 5964; https://doi.org/10.3390/s20205964 - 21 Oct 2020
Viewed by 177
Abstract
Mode shape-based structural damage identification methods have been widely investigated due to their good performances in damage localization. Nevertheless, the evaluation of mode shapes is severely affected by the measurement noise. Moreover, the conventional mode shape-based damage localization methods are normally proposed based [...] Read more.
Mode shape-based structural damage identification methods have been widely investigated due to their good performances in damage localization. Nevertheless, the evaluation of mode shapes is severely affected by the measurement noise. Moreover, the conventional mode shape-based damage localization methods are normally proposed based on a certain mode and not effective for multi-damage localization. To tackle these problems, a novel damage localization approach is proposed based on locally perturbed dynamic equilibrium and data fusion approach. The main contributions cover three aspects. Firstly, a joint singular value decomposition technique is proposed to simultaneously decompose several power spectral density transmissibility matrices for robust mode shape estimation, which statistically deals better with the measurement noise than the traditional transmissibility-based methods. Secondly, with the identified mode shapes, an improved pseudo-excitation method is proposed to construct a baseline-free damage localization index by quantifying the locally damage perturbed dynamic equilibrium without the knowledge of material/structural properties. Thirdly, to circumvent the conflicting damage information in different modes and integrate it for robust damage localization, a data fusion scheme is developed, which performs better than the Bayesian fusion approach. Both numerical and experimental studies of cantilever beams with two cracks were conducted to validate the feasibility and effectiveness of the proposed damage localization method. It was found that the proposed method outperforms the traditional transmissibility-based methods in terms of localization accuracy and robustness. Full article
(This article belongs to the Special Issue Sensors for Structural Damage Identification)
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Open AccessLetter
Quantification of Arm Swing during Walking in Healthy Adults and Parkinson’s Disease Patients: Wearable Sensor-Based Algorithm Development and Validation
Sensors 2020, 20(20), 5963; https://doi.org/10.3390/s20205963 - 21 Oct 2020
Viewed by 275
Abstract
Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). [...] Read more.
Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)
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Open AccessArticle
Fabricating a Portable ECG Device Using AD823X Analog Front-End Microchips and Open-Source Development Validation
Sensors 2020, 20(20), 5962; https://doi.org/10.3390/s20205962 - 21 Oct 2020
Viewed by 255
Abstract
Relevant to mobile health, the design of a portable electrocardiograph (ECG) device using AD823X microchips as the analog front-end is presented. Starting with the evaluation board of the chip, open-source hardware and software components were integrated into a breadboard prototype. This required modifying [...] Read more.
Relevant to mobile health, the design of a portable electrocardiograph (ECG) device using AD823X microchips as the analog front-end is presented. Starting with the evaluation board of the chip, open-source hardware and software components were integrated into a breadboard prototype. This required modifying the microchip with the breadboard-friendly Arduino Nano board in addition to a data logger and a Bluetooth breakout board. The digitized ECG signal can be transmitted by serial cable, via Bluetooth to a PC, or to an Android smartphone system for visualization. The data logging shield provides gigabytes of storage, as the signal is recorded to a microSD card adapter. A menu incorporates the device’s several operating modes. Simulation and testing assessed the system stability and performance parameters in terms of not losing any sample data throughout the length of the recording and finding the maximum sampling frequency; and validation determined and resolved problems that arose in open-source development. Ultimately, a custom printed circuit board was produced requiring advanced manufacturing options of 2.5 mils trace widths for the small package components. The fabricated device did not degrade the AD823X noise performance, and an ECG waveform with negligible distortion was obtained. The maximum number of samples/second was 2380 Hz in serial cable transmission, whereas in microSD recording mode, a continuous ECG signal for up to 36 h at 500 Hz was verified. A low-cost, high-quality portable ECG for long-term monitoring prototype that reasonably complies with electrical safety regulations and medical equipment design was realized. Full article
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Open AccessArticle
Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks
Sensors 2020, 20(20), 5961; https://doi.org/10.3390/s20205961 - 21 Oct 2020
Viewed by 215
Abstract
Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and [...] Read more.
Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and particle swarm optimization. The algorithm we proposed is a two-tier PSO approach. In the first tier, a PSO-based clustering protocol is proposed to synthetically consider the energy consumption and uniformity of cluster head distribution. Then in the second tier, a PSO-based routing protocol is proposed to implement inner-cluster one-hop routing and outer-cluster multi-hop routing. The nodes selected to constitute i-th effective routing path decide which positions in the i-th row of the measurement matrix are nonzero. As a result, in this tier the protocol comprehensively considers energy efficiency, network balance and data recovery quality. The Bayesian Cramér-Rao Bound (BCRB) in such a case is analyzed and added in the fitness function to monitor the mean square error of the reconstructed signal. The experimental results validate that our algorithm maintains a longer life time and postpones the appearance of the first dead node while keeps the reconstruction error lower compared with the cutting-edge algorithms which are also based on distributed multi-hop compressive sensing approaches. Full article
(This article belongs to the Special Issue Underwater Sensor Networks)
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Open AccessArticle
Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods
Sensors 2020, 20(20), 5960; https://doi.org/10.3390/s20205960 - 21 Oct 2020
Viewed by 190
Abstract
This work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction [...] Read more.
This work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator’s arm) to generate a system capable of recognition of the operator’s skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes’ purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators. Full article
(This article belongs to the Special Issue Human-Robot Interaction and Sensors for Social Robotics)
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Open AccessArticle
How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG
Sensors 2020, 20(20), 5959; https://doi.org/10.3390/s20205959 - 21 Oct 2020
Viewed by 276
Abstract
Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data [...] Read more.
Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data is a more delicate matter. The aim of this paper is to supply detailed methodological discussion and new data material in order to provide a helpful notice of HRV monitoring issues depending on recording conditions and study populations. Special consideration is given to the monitoring over long periods, across periods with different levels of activity, and in adults versus children. Specifically, the paper aims at making users aware of neglected methodological limitations and at providing substantiated recommendations for the selection of appropriate HRV variables and their interpretation. To this end, 30-h HRV data of 48 healthy adults (18–40 years) and 47 healthy toddlers (16–37 months) were analyzed in detail. Time-domain, frequency-domain, and nonlinear HRV variables were calculated after strict signal preprocessing, using six different high-frequency band definitions including frequency bands dynamically adjusted for the individual respiration rate. The major conclusion of the in-depth analyses is that for most applications that implicate long-term monitoring across varying circumstances and activity levels in healthy individuals, the time-domain variables are adequate to gain an impression of an individual’s HRV and, thus, the dynamic adaptation of an organism’s behavior in response to the ever-changing demands of daily life. The sound selection and interpretation of frequency-domain variables requires considerably more consideration of physiological and mathematical principles. For those who prefer using frequency-domain variables, the paper provides detailed guidance and recommendations for the definition of appropriate frequency bands in compliance with their specific recording conditions and study populations. Full article
(This article belongs to the Special Issue ECG Monitoring System)
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Open AccessArticle
Photon Counting Imaging with Low Noise and a Wide Dynamic Range for Aurora Observations
Sensors 2020, 20(20), 5958; https://doi.org/10.3390/s20205958 - 21 Oct 2020
Viewed by 198
Abstract
The radiation intensity of observed auroras in the far-ultraviolet (FUV) band varies dramatically with location for aerospace applications, requiring a photon counting imaging apparatus with a wide dynamic range. However, combining high spatial resolution imaging with high event rates is technically challenging. We [...] Read more.
The radiation intensity of observed auroras in the far-ultraviolet (FUV) band varies dramatically with location for aerospace applications, requiring a photon counting imaging apparatus with a wide dynamic range. However, combining high spatial resolution imaging with high event rates is technically challenging. We developed an FUV photon counting imaging system for aurora observation. Our system mainly consists of a microchannel plate (MCP) stack readout using a wedge strip anode (WSA) with charge induction and high-speed electronics, such as a charge sensitive amplifier (CSA) and pulse shaper. Moreover, we constructed an anode readout model and a time response model for readout circuits to investigate the counting error in high counting rate applications. This system supports global rates of 500 kilo counts, 0.610 dark counts s−1 cm−2 at an ambient temperature of 300 K and 111 µm spatial resolution at 400 kilo counts s−1 (kcps). We demonstrate an obvious photon count loss at incident intensities close to the counting capacity of the system. To preserve image quality, the response time should be improved and some noise performance may be sacrificed. Finally, we also describe the correlation between counting rate and imaging resolution, which further guides the design of space observation instruments. Full article
(This article belongs to the Section Sensing and Imaging)
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Open AccessArticle
Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor
Sensors 2020, 20(20), 5957; https://doi.org/10.3390/s20205957 - 21 Oct 2020
Viewed by 339
Abstract
Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations [...] Read more.
Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user’s body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application. Full article
(This article belongs to the Special Issue Low Cost Mid-Infrared Sensor Technologies)
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Open AccessCommunication
Development of Magnetic Nanobeads Modified by Artificial Fluorescent Peptides for the Highly Sensitive and Selective Analysis of Oxytocin
Sensors 2020, 20(20), 5956; https://doi.org/10.3390/s20205956 - 21 Oct 2020
Viewed by 207
Abstract
We describe two novel fluorescent peptides (compounds 1 and 2) targeting oxytocin with a boron-dipyrromethenyl group as the fluorophore bound to an artificial peptide based on the oxytocin receptor, and their application for the analysis of oxytocin levels in human serum using nanometer-sized [...] Read more.
We describe two novel fluorescent peptides (compounds 1 and 2) targeting oxytocin with a boron-dipyrromethenyl group as the fluorophore bound to an artificial peptide based on the oxytocin receptor, and their application for the analysis of oxytocin levels in human serum using nanometer-sized magnetic beads modified by fluorescent peptides (FMB-1 and FMB-2). Under the optimized experimental protocols, FMB-1 and FMB-2 emitted low levels of fluorescence but emitted much higher levels of fluorescence when associated with oxytocin. The detection limit of oxytocin by FMB-2 was 0.4 pM, which is approximately 37.5 times higher than that of conventional methods, such as ELISA. Using these fluorescent sensors, oxytocin was specifically detected over a wide linear range with high sensitivity, good reusability, stability, precision, and reproducibility. This fluorescent sensor-based detection system thus enabled the measurement of oxytocin levels in human serum, which has widespread applications for oxytocin assays across varied research fields. Full article
(This article belongs to the Special Issue Optical Probes and Sensors)
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Open AccessLetter
Non-Contact Monitoring on the Flow Status inside a Pulsating Heat Pipe
Sensors 2020, 20(20), 5955; https://doi.org/10.3390/s20205955 - 21 Oct 2020
Viewed by 262
Abstract
The paper presents a concept of thermal-to-electrical energy conversion by using the oscillatory motion of magnetic fluid slugs which has potential to be applied in the field of sensors. A pulsating heat pipe (PHP) is introduced to produce vapor-magnetic fluid plug–slug flow in [...] Read more.
The paper presents a concept of thermal-to-electrical energy conversion by using the oscillatory motion of magnetic fluid slugs which has potential to be applied in the field of sensors. A pulsating heat pipe (PHP) is introduced to produce vapor-magnetic fluid plug–slug flow in a snake-shaped capillary tube. As the magnetic fluid is magnetized by the permanent magnet, the slugs of magnetic fluid passing through the copper coils make the magnetic flux vary and produce the electromotive force. The peak values of induced voltage observed in our tests are from 0.1 mV to 4.4 mV. The effects of the slug velocity, heat input and magnetic particle volume concentration on the electromotive force are discussed. Furthermore, a theoretical model considering the fluid velocity of the working fluid, the inner radius of the PHP and the contact angle between the working fluid and the pipe wall is established. At the same time, the theoretical and experimental results are compared, and the influences of tube inner radius, working fluid velocity and contact angle on the induced electromotive force are analyzed. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Development of a Linear Acoustic Array for Aero-Acoustic Quantification of Camber-Bladed Vertical Axis Wind Turbine
Sensors 2020, 20(20), 5954; https://doi.org/10.3390/s20205954 - 21 Oct 2020
Viewed by 240
Abstract
Vertical axis wind turbines (VAWT) are a source of renewable energy and are used for both industrial and domestic purposes. The study of noise characteristics of a VAWT is an important performance parameter for the turbine. This study focuses on the development of [...] Read more.
Vertical axis wind turbines (VAWT) are a source of renewable energy and are used for both industrial and domestic purposes. The study of noise characteristics of a VAWT is an important performance parameter for the turbine. This study focuses on the development of a linear microphone array and measuring acoustic signals on a cambered five-bladed 45 W VAWT in an anechoic chamber at different tip speed ratios. The sound pressure level spectrum of VAWT shows that tonal noises such as blade passing frequencies dominate at lower frequencies whereas broadband noise corresponds to all audible ranges of frequencies. This study shows that the major portion of noise from the source is dominated by aerodynamic noises generated due to vortex generation and trailing edge serrations. The research also predicts that dynamic stall is evident in the lower Tip speed ratio (TSR) region making smaller TSR values unsuitable for a quiet VAWT. This paper compares the results of linear aeroacoustic array with a 128-MEMS acoustic camera with higher resolution. The study depicts a 3 dB margin between two systems at lower TSR values. The research approves the usage of the 8 mic linear array for small radius rotary machinery considering the results comparison with a NORSONIC camera and its resolution. These observations serve as a basis for noise reduction and blade optimization techniques. Full article
(This article belongs to the Special Issue Sensors for Wind Turbine Fault Diagnosis and Prognosis)
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Open AccessArticle
Pervasive Lying Posture Tracking
Sensors 2020, 20(20), 5953; https://doi.org/10.3390/s20205953 - 21 Oct 2020
Viewed by 260
Abstract
Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding [...] Read more.
Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Tetramethylbenzidine: An Acoustogenic Photoacoustic Probe for Reactive Oxygen Species Detection
Sensors 2020, 20(20), 5952; https://doi.org/10.3390/s20205952 - 21 Oct 2020
Viewed by 231
Abstract
Photoacoustic imaging is attracting a great deal of interest owing to its distinct advantages over other imaging techniques such as fluorescence or magnetic resonance image. The availability of photoacoustic probes for reactive oxygen and nitrogen species (ROS/RNS) could shed light on a plethora [...] Read more.
Photoacoustic imaging is attracting a great deal of interest owing to its distinct advantages over other imaging techniques such as fluorescence or magnetic resonance image. The availability of photoacoustic probes for reactive oxygen and nitrogen species (ROS/RNS) could shed light on a plethora of biological processes mediated by these key intermediates. Tetramethylbenzidine (TMB) is a non-toxic and non-mutagenic colorless dye that develops a distinctive blue color upon oxidation. In this work, we have investigated the potential of TMB as an acoustogenic photoacoustic probe for ROS/RNS. Our results indicate that TMB reacts with hypochlorite, hydrogen peroxide, singlet oxygen, and nitrogen dioxide to produce the blue oxidation product, while ROS, such as the superoxide radical anion, sodium peroxide, hydroxyl radical, or peroxynitrite, yield a colorless oxidation product. TMB does not penetrate the Escherichia coli cytoplasm but is capable of detecting singlet oxygen generated in its outer membrane. Full article
(This article belongs to the Special Issue Luminescent/Colorimetric Probes and Sensors)
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Open AccessLetter
Quality Assessment during Incubation Using Image Processing
Sensors 2020, 20(20), 5951; https://doi.org/10.3390/s20205951 - 21 Oct 2020
Viewed by 312
Abstract
The fertilized egg is an indispensable production platform for making egg-based vaccines. This study was divided into two parts. In the first part, image processing was employed to analyze the absorption spectrum of fertilized eggs; the results show that the 580-nm band had [...] Read more.
The fertilized egg is an indispensable production platform for making egg-based vaccines. This study was divided into two parts. In the first part, image processing was employed to analyze the absorption spectrum of fertilized eggs; the results show that the 580-nm band had the most significant change. In the second part, a 590-nm-wavelength LED was selected as the light source for the developed detection device. Using this device, sample images (in RGB color space) of the eggs were obtained every day during the experiment. After calculating the grayscale value of the red layer, the receiver operating characteristic curve was used to analyze the daily data to obtain the area under the curve. Subsequently, the best daily grayscale value for classifying unfertilized eggs and dead-in-shell eggs was obtained. Finally, an industrial prototype of the device designed and fabricated in this study was operated and verified. The results show that the accuracy for detecting unfertilized eggs was up to 98% on the seventh day, with the sensitivity and Youden’s index being 82% and 0.813, respectively. On the ninth day, both accuracy and sensitivity reached 100%, and Youden’s index reached a value of 1, showing good classification ability. Considering the industrial operating conditions, this method was demonstrated to be commercially applicable because, when used to detect unfertilized eggs and dead-in-shell eggs on the ninth day, it could achieve accuracy and sensitivity of 100% at the speed of five eggs per second. Full article
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Open AccessArticle
The Measurement of Nanoparticle Concentrations by the Method of Microcavity Mode Broadening Rate
Sensors 2020, 20(20), 5950; https://doi.org/10.3390/s20205950 - 21 Oct 2020
Viewed by 228
Abstract
A measurement system for the detection of a low concentration of nanoparticles based on optical microcavities with whispering-gallery modes (WGMs) is developed and investigated. A novel method based on the WGM broadening allows us to increase the precision of concentration measurements up to [...] Read more.
A measurement system for the detection of a low concentration of nanoparticles based on optical microcavities with whispering-gallery modes (WGMs) is developed and investigated. A novel method based on the WGM broadening allows us to increase the precision of concentration measurements up to 0.005 ppm for nanoparticles of a known size. We describe WGM microcavity manufacturing and quality control methods. The collective interaction process of suspended Ag nanoparticles in a liquid and TiO2 in the air with a microcavity surface is studied. Full article
(This article belongs to the Section Nanosensors)
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Open AccessArticle
Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors
Sensors 2020, 20(20), 5949; https://doi.org/10.3390/s20205949 - 21 Oct 2020
Viewed by 224
Abstract
An inductive debris sensor can monitor a mechanical system’s debris in real time. The measuring accuracy is significantly affected by the signal aliasing issue happening in the monitoring process. In this study, a mathematical model was built to explain two debris particles’ aliasing [...] Read more.
An inductive debris sensor can monitor a mechanical system’s debris in real time. The measuring accuracy is significantly affected by the signal aliasing issue happening in the monitoring process. In this study, a mathematical model was built to explain two debris particles’ aliasing behavior. Then, a cross-correlation-based method was proposed to deal with this aliasing. Afterwards, taking advantage of the processed signal along with the original signal, an optimization strategy was proposed to make the evaluation of the aliasing debris more accurate than that merely using initial signals. Compared to other methods, the proposed method has fewer limitations in practical applications. The simulation and experimental results also verified the advantage of the proposed method. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm
Sensors 2020, 20(20), 5948; https://doi.org/10.3390/s20205948 - 21 Oct 2020
Viewed by 234
Abstract
Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and [...] Read more.
Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems
Sensors 2020, 20(20), 5947; https://doi.org/10.3390/s20205947 - 21 Oct 2020
Viewed by 267
Abstract
Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data [...] Read more.
Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection
Sensors 2020, 20(20), 5946; https://doi.org/10.3390/s20205946 - 21 Oct 2020
Viewed by 217
Abstract
Automatic reading of pointer meters is of great significance for efficient measurement of industrial meters. However, existing algorithms are defective in the accuracy and robustness to illumination shooting angle when detecting various pointer meters. Hence, a novel algorithm for adaptive detection of different [...] Read more.
Automatic reading of pointer meters is of great significance for efficient measurement of industrial meters. However, existing algorithms are defective in the accuracy and robustness to illumination shooting angle when detecting various pointer meters. Hence, a novel algorithm for adaptive detection of different pointer meters was presented. Above all, deep learning was introduced to detect and recognize scale value text in the meter dial. Then, the image was rectified and meter center was determined based on text coordinate. Next, the circular arc scale region was transformed into a linear scale region by polar transform, and the horizontal positions of pointer and scale line were obtained based on secondary search in the expanded graph. Finally, the distance method was used to read the scale region where the pointer is located. Test results showed that the algorithm proposed in this paper has higher accuracy and robustness in detecting different types of meters. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessEditorial
Smart Sensors and Devices in Artificial Intelligence
Sensors 2020, 20(20), 5945; https://doi.org/10.3390/s20205945 - 21 Oct 2020
Viewed by 219
Abstract
As stated in the Special Issue call, “sensors are eyes or/and ears of an intelligent system, such as Unmanned Aerial Vehicle (UAV), Automated Guided Vehicle (AGV) and robots [...] Full article
(This article belongs to the Special Issue Smart Sensors and Devices in Artificial Intelligence)
Open AccessArticle
Dwell Time Allocation Algorithm for Multiple Target Tracking in LPI Radar Network Based on Cooperative Game
Sensors 2020, 20(20), 5944; https://doi.org/10.3390/s20205944 - 21 Oct 2020
Viewed by 186
Abstract
To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low [...] Read more.
To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low interception performance by optimizing the allocation of the dwell time of each radar under the constraints of the given target detection performance, minimizing the total dwell time of radar network. By introducing two variables, dwell time and target allocation indicators, we decompose the dwell time and target allocation into two subproblems. Firstly, combining the Lagrange relaxation algorithm with the Newton iteration method, we derive the iterative formula for the dwell time of each radar. The dwell time allocation of the radars corresponding to each target is obtained. Secondly, we use the fixed Hungarian algorithm to determine the target allocation scheme based on the dwell time allocation results. Simulation results show that the proposed algorithm can effectively reduce the total dwell time of the radar network, and hence, improve the LPI performance. Full article
(This article belongs to the Special Issue Radio Sensing and Sensor Networks)
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Open AccessArticle
Bi-Layer Shortest-Path Network Interdiction Game for Internet of Things
Sensors 2020, 20(20), 5943; https://doi.org/10.3390/s20205943 - 21 Oct 2020
Viewed by 209
Abstract
Network security is a crucial challenge facing Internet-of-Things (IoT) systems worldwide, which leads to serious safety alarms and great economic loss. This paper studies the problem of malicious interdicting network exploitation of IoT systems that are modeled as a bi-layer logical–physical network. In [...] Read more.
Network security is a crucial challenge facing Internet-of-Things (IoT) systems worldwide, which leads to serious safety alarms and great economic loss. This paper studies the problem of malicious interdicting network exploitation of IoT systems that are modeled as a bi-layer logical–physical network. In this problem, a virtual attack takes place at the logical layer (the layer of Things), while the physical layer (the layer of Internet) provides concrete support for the attack. In the interdiction problem, the attacker attempts to access a target node on the logical layer with minimal communication cost, but the defender can strategically interdict some key edges on the physical layer given a certain budget of interdiction resources. This setting generalizes the classic single-layer shortest-path network interdiction problem, but brings in nonlinear objective functions, which are notoriously challenging to optimize. We reformulate the model and apply Benders decomposition process to solve this problem. A layer-mapping module is introduced to improve the decomposition algorithm and a random-search process is proposed to accelerate the convergence. Extensive numerical experiments demonstrate the computational efficiency of our methods. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle
Chitosan-Based Nanocomposites for Glyphosate Detection Using Surface Plasmon Resonance Sensor
Sensors 2020, 20(20), 5942; https://doi.org/10.3390/s20205942 - 21 Oct 2020
Viewed by 213
Abstract
This article describes an optical method based on the association of surface plasmon resonance (SPR) with chitosan (CS) film and its nanocomposites, including zinc oxide (ZnO) or graphene oxide (GO) for glyphosate detection. CS and CS/ZnO or CS/GO thin films were deposited on [...] Read more.
This article describes an optical method based on the association of surface plasmon resonance (SPR) with chitosan (CS) film and its nanocomposites, including zinc oxide (ZnO) or graphene oxide (GO) for glyphosate detection. CS and CS/ZnO or CS/GO thin films were deposited on an Au chip using the spin coating technique. The characterization, morphology, and composition of these films were performed by Fourier-transform infrared spectroscopy (FTIR), atomic force microscopy (AFM), and contact angle technique. Sensor preparation conditions including the cross-linking and mobile phase (pH and salinity) were investigated and thoroughly optimized. Results showed that the CS/ZnO thin-film composite provides the highest sensitivity for glyphosate sensing with a low detection limit of 8 nM and with high reproducibility. From the Langmuir-type adsorption model and the effect of ionic strength, the adsorption mechanisms of glyphosate could be controlled by electrostatic and steric interaction with possible formation of 1:1 outer-sphere surface complexes. The selectivity of the optical method was investigated with respect to the sorption of glyphosate metabolite (aminomethylphosphonic acid) (AMPA), glufosinate, and one of the glufonisate metabolites (3-methyl-phosphinico-propionic acid) (MPPA). Results showed that the SPR sensor offers a very good selectivity for glyphosate, but the competition of other molecules could still occur in aqueous systems. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
End-to-End Monocular Range Estimation for Forward Collision Warning
by Jie Tang and Jian Li
Sensors 2020, 20(20), 5941; https://doi.org/10.3390/s20205941 - 21 Oct 2020
Viewed by 185
Abstract
Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for [...] Read more.
Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specific categories relying on expensive object-level annotation for training, but not for unseen categories. In this paper, we present an end-to-end deep learning architecture to solve the above problems. Specifically, we represent the target range as a weighted sum of a set of potential distances. These potential distances are generated by inverse perspective projection based on intrinsic and extrinsic camera parameters, while a deep neural network predicts the corresponding weights of these distances. The whole architecture is optimized towards the range estimation task directly in an end-to-end manner with only the target range as supervision. As object category is not restricted in the training stage, the proposed method can generalize to objects with unseen categories. Furthermore, camera parameters are explicitly considered in the proposed method, making it able to generalize to images taken with different cameras and novel views. Additionally, the proposed method is not a pure black box, but provides partial interpretability by visualizing the produced weights to see which part of the image dominates the final result. We conduct experiments to verify the above properties of the proposed method on synthetic and real-world collected data. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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Open AccessArticle
An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions
Sensors 2020, 20(20), 5940; https://doi.org/10.3390/s20205940 - 21 Oct 2020
Viewed by 192
Abstract
Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem [...] Read more.
Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones. Full article
(This article belongs to the Special Issue Advanced Radar Techniques, Applications and Developments)
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Open AccessArticle
Variational Channel Estimation with Tempering: An Artificial Intelligence Algorithm for Wireless Intelligent Networks
Sensors 2020, 20(20), 5939; https://doi.org/10.3390/s20205939 - 21 Oct 2020
Viewed by 199
Abstract
With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue [...] Read more.
With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs. Full article
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Open AccessReview
A Review of Solar Energy Harvesting Electronic Textiles
Sensors 2020, 20(20), 5938; https://doi.org/10.3390/s20205938 - 21 Oct 2020
Viewed by 413
Abstract
An increased use in wearable, mobile, and electronic textile sensing devices has led to a desire to keep these devices continuously powered without the need for frequent recharging or bulky energy storage. To achieve this, many have proposed integrating energy harvesting capabilities into [...] Read more.
An increased use in wearable, mobile, and electronic textile sensing devices has led to a desire to keep these devices continuously powered without the need for frequent recharging or bulky energy storage. To achieve this, many have proposed integrating energy harvesting capabilities into clothing: solar energy harvesting has been one of the most investigated avenues for this due to the abundance of solar energy and maturity of photovoltaic technologies. This review provides a comprehensive, contemporary, and accessible overview of electronic textiles that are capable of harvesting solar energy. The review focusses on the suitability of the textile-based energy harvesting devices for wearable applications. While multiple methods have been employed to integrate solar energy harvesting with textiles, there are only a few examples that have led to devices with textile properties. Full article
(This article belongs to the Special Issue Textile Electrodes and Sensors)
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