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Sensors, Volume 21, Issue 21 (November-1 2021) – 482 articles

Cover Story (view full-size image): Smart grids may efficiently incorporate a variety of Renewable Energy Sources (RES), reducing their carbon footprint, as well as Energy Storage Systems (ESS), which mitigate the mismatch between the RES production and the load curve. Additional capacity can be attained from Electric Vehicles (EVs) in Vehicle-to-Grid (V2G) mode. Taking advantage of the smart sensors and data collected by the smart grids, e.g., RES production forecasts, schedules of EV owners, etc., a smart grid operator may plan the optimal energy dispatch for the day ahead while considering a rich and environmentally friendly energy mix. Yet, in order to anticipate potential deviations, it is critical to take into account uncertainties related to forecasts, load variations and the availability of resources. View this paper
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Article
Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder
Sensors 2021, 21(21), 7426; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217426 - 08 Nov 2021
Viewed by 361
Abstract
The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts [...] Read more.
The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment’s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified. Full article
(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)
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Article
A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning
Sensors 2021, 21(21), 7425; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217425 - 08 Nov 2021
Viewed by 281
Abstract
This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is [...] Read more.
This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generated by RRT-connect. To check the proposed algorithm’s performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples. Full article
(This article belongs to the Special Issue Sensors and Applications in Computer Science and Intelligent Systems)
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Article
A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers
Sensors 2021, 21(21), 7424; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217424 - 08 Nov 2021
Viewed by 390
Abstract
With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to be crucial for distracted driving and the establishment [...] Read more.
With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to be crucial for distracted driving and the establishment of driver assistance systems is of great value. However, almost all research on the recognition of the driver’s distracted actions using computer vision methods neglected the importance of temporal information for action recognition. This paper proposes a hybrid deep learning model for recognizing the actions of distracted drivers. Specifically, we used OpenPose to obtain skeleton information of the human body and then constructed the vector angle and modulus ratio of the human body structure as features to describe the driver’s actions, thereby realizing the fusion of deep network features and artificial features, which improve the information density of spatial features. The K-means clustering algorithm was used to preselect the original frames, and the method of inter-frame comparison was used to obtain the final keyframe sequence by comparing the Euclidean distance between manually constructed vectors representing frames and the vector representing the cluster center. Finally, we constructed a two-layer long short-term memory neural network to obtain more effective spatiotemporal features, and one softmax layer to identify the distracted driver’s action. The experimental results based on the collected dataset prove the effectiveness of this framework, and it can provide a theoretical basis for the establishment of vehicle distraction warning systems. Full article
(This article belongs to the Topic Artificial Intelligence in Sensors)
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Review
Metal–Organic-Framework- and MXene-Based Taste Sensors and Glucose Detection
Sensors 2021, 21(21), 7423; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217423 - 08 Nov 2021
Viewed by 401
Abstract
Taste sensors can identify various tastes, including saltiness, bitterness, sweetness, sourness, and umami, and have been useful in the food and beverage industry. Metal–organic frameworks (MOFs) and MXenes have recently received considerable attention for the fabrication of high-performance biosensors owing to their large [...] Read more.
Taste sensors can identify various tastes, including saltiness, bitterness, sweetness, sourness, and umami, and have been useful in the food and beverage industry. Metal–organic frameworks (MOFs) and MXenes have recently received considerable attention for the fabrication of high-performance biosensors owing to their large surface area, high ion transfer ability, adjustable chemical structure. Notably, MOFs with large surface areas, tunable chemical structures, and high stability have been explored in various applications, whereas MXenes with good conductivity, excellent ion-transport characteristics, and ease of modification have exhibited great potential in biochemical sensing. This review first outlines the importance of taste sensors, their operation mechanism, and measuring methods in sensing utilization. Then, recent studies focusing on MOFs and MXenes for the detection of different tastes are discussed. Finally, future directions for biomimetic tongues based on MOFs and MXenes are discussed. Full article
(This article belongs to the Special Issue State-of-the-Art Chemical Sensors in Korea)
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Article
Small Object Detection in Traffic Scenes Based on YOLO-MXANet
Sensors 2021, 21(21), 7422; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217422 - 08 Nov 2021
Viewed by 427
Abstract
In terms of small objects in traffic scenes, general object detection algorithms have low detection accuracy, high model complexity, and slow detection speed. To solve the above problems, an improved algorithm (named YOLO-MXANet) is proposed in this paper. Complete-Intersection over Union (CIoU) is [...] Read more.
In terms of small objects in traffic scenes, general object detection algorithms have low detection accuracy, high model complexity, and slow detection speed. To solve the above problems, an improved algorithm (named YOLO-MXANet) is proposed in this paper. Complete-Intersection over Union (CIoU) is utilized to improve loss function for promoting the positioning accuracy of the small object. In order to reduce the complexity of the model, we present a lightweight yet powerful backbone network (named SA-MobileNeXt) that incorporates channel and spatial attention. Our approach can extract expressive features more effectively by applying the Shuffle Channel and Spatial Attention (SCSA) module into the SandGlass Block (SGBlock) module while increasing the parameters by a small number. In addition, the data enhancement method combining Mosaic and Mixup is employed to improve the robustness of the training model. The Multi-scale Feature Enhancement Fusion (MFEF) network is proposed to fuse the extracted features better. In addition, the SiLU activation function is utilized to optimize the Convolution-Batchnorm-Leaky ReLU (CBL) module and the SGBlock module to accelerate the convergence of the model. The ablation experiments on the KITTI dataset show that each improved method is effective. The improved algorithm reduces the complexity and detection speed of the model while improving the object detection accuracy. The comparative experiments on the KITTY dataset and CCTSDB dataset with other algorithms show that our algorithm also has certain advantages. Full article
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Article
Experimental Investigation of the Atmosphere-Regolith Water Cycle on Present-Day Mars
Sensors 2021, 21(21), 7421; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217421 - 08 Nov 2021
Viewed by 550
Abstract
The water content of the upper layers of the surface of Mars is not yet quantified. Laboratory simulations are the only feasible way to investigate this in a controlled way on Earth, and then compare it with remote and in situ observations of [...] Read more.
The water content of the upper layers of the surface of Mars is not yet quantified. Laboratory simulations are the only feasible way to investigate this in a controlled way on Earth, and then compare it with remote and in situ observations of spacecrafts on Mars. Describing the processes that may induce changes in the water content of the surface is critical to determine the present-day habitability of the Martian surface, to understand the atmospheric water cycle, and to estimate the efficiency of future water extraction procedures from the regolith for In Situ Resource Utilization (ISRU). This paper illustrates the application of the SpaceQ facility to simulate the near-surface water cycle under Martian conditions. Rover Environmental Monitoring Station (REMS) observations at Gale crater show a non-equilibrium situation in the atmospheric H2O volume mixing ratio (VMR) at night-time, and there is a decrease in the atmospheric water content by up to 15 g/m2 within a few hours. This reduction suggests that the ground may act at night as a cold sink scavenging atmospheric water. Here, we use an experimental approach to investigate the thermodynamic and kinetics of water exchange between the atmosphere, a non-porous surface (LN2-chilled metal), various salts, Martian regolith simulant, and mixtures of salts and simulant within an environment which is close to saturation. We have conducted three experiments: the stability of pure liquid water around the vicinity of the triple point is studied in experiment 1, as well as observing the interchange of water between the atmosphere and the salts when the surface is saturated; in experiment 2, the salts were mixed with Mojave Martian Simulant (MMS) to observe changes in the texture of the regolith caused by the interaction with hydrates and liquid brines, and to quantify the potential of the Martian regolith to absorb and retain water; and experiment 3 investigates the evaporation of pure liquid water away from the triple point temperature when both the air and ground are at the same temperature and the relative humidity is near saturation. We show experimentally that frost can form spontaneously on a surface when saturation is reached and that, when the temperature is above 273.15 K (0 °C), this frost can transform into liquid water, which can persist for up to 3.5 to 4.5 h at Martian surface conditions. For comparison, we study the behavior of certain deliquescent salts that exist on the Martian surface, which can increase their mass between 32% and 85% by absorption of atmospheric water within a few hours. A mixture of these salts in a 10% concentration with simulant produces an aggregated granular structure with a water gain of approximately 18- to 50-wt%. Up to 53% of the atmospheric water was captured by the simulated ground, as pure liquid water, hydrate, or brine. Full article
(This article belongs to the Section Environmental Sensing)
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Article
Effect of the Substrate Crystallinity on Morphological and Magnetic Properties of Fe70Pd30 Nanoparticles Obtained by the Solid-State Dewetting
Sensors 2021, 21(21), 7420; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217420 - 08 Nov 2021
Viewed by 291
Abstract
Advances in nanofabrication techniques are undoubtedly needed to obtain nanostructured magnetic materials with physical and chemical properties matching the pressing and relentless technological demands of sensors. Solid-state dewetting is known to be a low-cost and “top-down” nanofabrication technique able to induce a controlled [...] Read more.
Advances in nanofabrication techniques are undoubtedly needed to obtain nanostructured magnetic materials with physical and chemical properties matching the pressing and relentless technological demands of sensors. Solid-state dewetting is known to be a low-cost and “top-down” nanofabrication technique able to induce a controlled morphological transformation of a continuous thin film into an ordered nanoparticle array. Here, magnetic Fe70Pd30 thin film with 30 nm thickness is deposited by the co-sputtering technique on a monocrystalline (MgO) or amorphous (Si3N4) substrate and, subsequently, annealed to promote the dewetting process. The different substrate properties are able to tune the activation thermal energy of the dewetting process, which can be tuned by depositing on substrates with different microstructures. In this way, it is possible to tailor the final morphology of FePd nanoparticles as observed by advanced microscopy techniques (SEM and AFM). The average size and height of the nanoparticles are in the ranges 150–300 nm and 150–200 nm, respectively. Moreover, the induced spatial confinement of magnetic materials in almost-spherical nanoparticles strongly affects the magnetic properties as observed by in-plane and out-of-plane hysteresis loops. Magnetization reversal in dewetted FePd nanoparticles is mainly characterized by a rotational mechanism leading to a slower approach to saturation and smaller value of the magnetic susceptibility than the as-deposited thin film. Full article
(This article belongs to the Special Issue Sensors and Biosensors Related to Magnetic Nanoparticles)
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Article
Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry
Sensors 2021, 21(21), 7419; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217419 - 08 Nov 2021
Viewed by 317
Abstract
The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and [...] Read more.
The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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Article
How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?
Sensors 2021, 21(21), 7418; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217418 - 08 Nov 2021
Viewed by 587
Abstract
Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, [...] Read more.
Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm. Full article
(This article belongs to the Collection Sensor Technology for Sports Science)
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Article
Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment
Sensors 2021, 21(21), 7417; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217417 - 08 Nov 2021
Viewed by 524
Abstract
Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining [...] Read more.
Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor and several candidate machine learning (ML) models. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Health Applications)
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Article
Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region
Sensors 2021, 21(21), 7416; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217416 - 08 Nov 2021
Viewed by 514
Abstract
Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity [...] Read more.
Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region. Full article
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Article
MIMO Antenna System for Modern 5G Handheld Devices with Healthcare and High Rate Delivery
Sensors 2021, 21(21), 7415; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217415 - 08 Nov 2021
Viewed by 455
Abstract
In this work, a new prototype of the eight-element MIMO antenna system for 5G communications, internet of things, and networks has been proposed. This system is based on an H-shaped monopole antenna system that offers 200 MHz bandwidth ranges between 3.4–3.6 GHz, and [...] Read more.
In this work, a new prototype of the eight-element MIMO antenna system for 5G communications, internet of things, and networks has been proposed. This system is based on an H-shaped monopole antenna system that offers 200 MHz bandwidth ranges between 3.4–3.6 GHz, and the isolation between any two elements is well below −12 dB without using any decoupling structure. The proposed system is designed on a commercially available 0.8 mm-thick FR4 substrate. One side of the chassis is used to place the radiating elements, while the copper from the other side is being removed to avoid short-circuiting with other components and devices. This also enables space for other systems, sub-systems, and components. A prototype is fabricated and excellent agreement is observed between the experimental and the computed results. It was found that ECC is 0.2 for any two radiating elements which is consistent with the desirable standards, and channel capacity is 38 bps/Hz which is 2.9 times higher than 4 × 4 MIMO configuration. In addition, single hand mode and dual hand mode analysis are conducted to understand the operation of the system under such operations and to identify losses and/or changes in the key performance parameters. Based on the results, the proposed antenna system will find its applications in modern 5G handheld devices and internet of things with healthcare and high rate delivery. Besides that, its design simplicity will make it applicable for mass production to be used in industrial demands. Full article
(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)
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Article
An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging
Sensors 2021, 21(21), 7414; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217414 - 08 Nov 2021
Viewed by 300
Abstract
Traditional supervised time series classification (TSC) tasks assume that all training data are labeled. However, in practice, manually labelling all unlabeled data could be very time-consuming and often requires the participation of skilled domain experts. In this paper, we concern with the positive [...] Read more.
Traditional supervised time series classification (TSC) tasks assume that all training data are labeled. However, in practice, manually labelling all unlabeled data could be very time-consuming and often requires the participation of skilled domain experts. In this paper, we concern with the positive unlabeled time series classification problem (PUTSC), which refers to automatically labelling the large unlabeled set U based on a small positive labeled set PL. The self-training (ST) is the most widely used method for solving the PUTSC problem and has attracted increased attention due to its simplicity and effectiveness. The existing ST methods simply employ the one-nearest-neighbor (1NN) formula to determine which unlabeled time-series should be labeled. Nevertheless, we note that the 1NN formula might not be optimal for PUTSC tasks because it may be sensitive to the initial labeled data located near the boundary between the positive and negative classes. To overcome this issue, in this paper we propose an exploratory methodology called ST-average. Unlike conventional ST-based approaches, ST-average utilizes the average sequence calculated by DTW barycenter averaging technique to label the data. Compared with any individuals in PL set, the average sequence is more representative. Our proposal is insensitive to the initial labeled data and is more reliable than existing ST-based methods. Besides, we demonstrate that ST-average can naturally be implemented along with many existing techniques used in original ST. Experimental results on public datasets show that ST-average performs better than related popular methods. Full article
(This article belongs to the Section Intelligent Sensors)
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Article
Decentralized P2P Electricity Trading Model for Thailand
Sensors 2021, 21(21), 7413; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217413 - 08 Nov 2021
Viewed by 346
Abstract
Thailand’s power system has been facing an energy transition due to the increasing amount of Renewable Energy (RE) integration, prosumers with self-consumption, and digitalization-based business models in a Local Energy Market (LEM). This paper introduces a decentralized business model and a possible trading [...] Read more.
Thailand’s power system has been facing an energy transition due to the increasing amount of Renewable Energy (RE) integration, prosumers with self-consumption, and digitalization-based business models in a Local Energy Market (LEM). This paper introduces a decentralized business model and a possible trading platform for electricity trading in Thailand’s Micro-Grid to deal with the power system transformation. This approach is Hybrid P2P, a market structure in which sellers and buyers negotiate on energy exchanging by themselves called Fully P2P trading or through the algorithm on the market platform called Community-based trading. A combination of Auction Mechanism (AM), Bill Sharing (BS), and Traditional Mechanism (TM) is the decentralized price mechanism proposed for the Community-based trading. The approach is validated through a test case in which, during the daytime, the energy import and export of the community are significantly reduced when 75 consumers and 25 PV rooftop prosumers participate in this decentralized trading model. Furthermore, a comparison analysis confirms that the decentralized business model outperforms a centralized approach on community and individual levels. Full article
(This article belongs to the Special Issue Blockchain Applications in Smart Energy Grids)
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Article
A Self-Calibration Stitching Method for Pitch Deviation Evaluation of a Long-Range Linear Scale by Using a Fizeau Interferometer
Sensors 2021, 21(21), 7412; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217412 - 08 Nov 2021
Viewed by 272
Abstract
An interferometric self-calibration method for the evaluation of the pitch deviation of scale grating has been extended to evaluate the pitch deviation of the long-range type linear scale by utilizing the stitching interferometry technique. Following the previous work, in which the interferometric self-calibration [...] Read more.
An interferometric self-calibration method for the evaluation of the pitch deviation of scale grating has been extended to evaluate the pitch deviation of the long-range type linear scale by utilizing the stitching interferometry technique. Following the previous work, in which the interferometric self-calibration method was proposed to assess the pitch deviation of the scale grating by combing the first-order diffracted beams from the grating, a stitching calibration method is proposed to enlarge the measurement range. Theoretical analysis is performed to realize the X-directional pitch deviation calibration of the long-range linear scale while reducing the second-order accumulation effect by canceling the influence of the reference flat error in the sub-apertures’ measurements. In this paper, the stitching interferometry theory is briefly reviewed, and theoretical equations of the X-directional pitch deviation stitching are derived for evaluation of the pitch deviation of the long-range linear scale. Followed by the simulation verification, some experiments with a linear scale of 105 mm length from a commercial interferential scanning-type optical encoder are conducted to verify the feasibility of the self-calibration stitching method for the calibration of the X-directional pitch deviation of the linear scale over its whole area. Full article
(This article belongs to the Special Issue Optical Sensors Technology and Applications)
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Article
Multiband Ambient RF Energy Harvester with High Gain Wideband Circularly Polarized Antenna toward Self-Powered Wireless Sensors
Sensors 2021, 21(21), 7411; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217411 - 08 Nov 2021
Viewed by 356
Abstract
In this work toward a sustainable operation of a self-powered wireless sensor, we investigated a multiband Wi-Fi/3G/4G/5G energy harvester based on a novel wideband circularly polarized antenna, a quadplexer, and rectifiers at four corresponding bands. This proposed antenna consisted of four sequentially rotated [...] Read more.
In this work toward a sustainable operation of a self-powered wireless sensor, we investigated a multiband Wi-Fi/3G/4G/5G energy harvester based on a novel wideband circularly polarized antenna, a quadplexer, and rectifiers at four corresponding bands. This proposed antenna consisted of four sequentially rotated dual-dipoles, fed by a hybrid feeding network with equal amplitude and an incremental 90° phase delay. The feeding network was composed of three Wilkinson power dividers and Schiffman phase shifters. Based on the sequential rotation method, the antenna obtained a −10 dB reflection coefficient bandwidth of 71.2% from 1.4 GHz to 2.95 GHz and a 3 dB axial ratio (AR) bandwidth of 63.6%, from 1.5 GHz to 2.9 GHz. In addition, this antenna gain was higher than 6 dBi in a wide bandwidth from 1.65 GHz to 2.8 GHz, whereas the peak gain was 9.9 dBi. The quad-band rectifier yielded the maximum AC–DC conversion efficiency of 1.8 GHz and was 60% at −1 dBm input power, 2.1 GHz was 55% at 0 dBm, 2.45 GHz was 55% at −1 dBm, and 2.6 GHz was 54% at 0.5 dBm, respectively. The maximum RF–DC conversion efficiency using the wideband circularly polarized antenna was 27%, 26%, 25.5%, and 27.5% at −6 dBm of input power, respectively. Full article
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Article
Low-Cost Polyethylene Terephthalate Fluidic Sensor for Ultrahigh Accuracy Measurement of Liquid Concentration Variation
Sensors 2021, 21(21), 7410; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217410 - 08 Nov 2021
Viewed by 282
Abstract
A low-cost polyethylene terephthalate fluidic sensor (PET-FS) is demonstrated for the concentration variation measurement on fluidic solutions. The PET-FS consisted of a triangular fluidic container attached with a birefringent PET thin layer. The PET-FS was injected with the test liquid solution that was [...] Read more.
A low-cost polyethylene terephthalate fluidic sensor (PET-FS) is demonstrated for the concentration variation measurement on fluidic solutions. The PET-FS consisted of a triangular fluidic container attached with a birefringent PET thin layer. The PET-FS was injected with the test liquid solution that was placed in a common path polarization interferometer by utilizing a heterodyne scheme. The measured phase variation of probe light was used to obtain the information regarding the concentration change in the fluidic liquids. The sensor was experimentally tested using different concentrations of sodium chloride solution showing a sensitivity of 3.52 ×104 deg./refractive index unit (RIU) and a detection resolution of 6.25 × 10−6 RIU. The estimated sensitivity and detection resolutions were 5.62 × 104 (deg./RIU) and 6.94 × 10−6 RIU, respectively, for the hydrochloric acid. The relationship between the measured phase and the concentration is linear with an R-squared value reaching above 0.995. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)
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Article
An Electromagnetic Time-Reversal Imaging Algorithm for Moisture Detection in Polymer Foam in an Industrial Microwave Drying System
Sensors 2021, 21(21), 7409; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217409 - 08 Nov 2021
Viewed by 426
Abstract
Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A [...] Read more.
Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A decomposition of the time-reversal operator (DORT) algorithm with a proper Green’s function of multilayered media is utilized to localize the moisture location. The derived Green’s function can be applied to the media with low or high contrast layers. It is shown that the time-reversal imaging (TRI) with the proposed Green’s function can be applied to the multilayered media with a moderately rough surface. Moreover, a single frequency TRI is proposed to decrease the measurement time. Numerical results for different moisture scenarios are presented to demonstrate the efficacy of the proposed method. The developed method is then tested on the experimental data for different moisture scenarios from our developed MWT experimental prototype. Image reconstruction results show promising capabilities of the TRI algorithm in estimating the moisture location in the polymer foam. Full article
(This article belongs to the Special Issue Tomographic Sensors for Industrial Process Control)
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Article
Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
Sensors 2021, 21(21), 7408; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217408 - 08 Nov 2021
Viewed by 344
Abstract
Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris [...] Read more.
Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation. Full article
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Article
Computational Large Field-of-View RGB-D Integral Imaging System
Sensors 2021, 21(21), 7407; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217407 - 08 Nov 2021
Viewed by 308
Abstract
The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens [...] Read more.
The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach’s robustness. Full article
(This article belongs to the Special Issue Computational Intelligence in Data Fusion and Image Analysis)
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Article
A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data
Sensors 2021, 21(21), 7406; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217406 - 08 Nov 2021
Viewed by 291
Abstract
Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite [...] Read more.
Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration. Full article
(This article belongs to the Special Issue Applications and Downscaling of Remote Sensing Soil Moisture)
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Article
Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
Sensors 2021, 21(21), 7405; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217405 - 08 Nov 2021
Viewed by 443
Abstract
Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images [...] Read more.
Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detection. The advantage of this method is the highly accurate results in vector-based images, which are convenient for mathematical analysis of the detected cracks at a later time. However, images taken under established parameters are different from images in real-world contexts. Another potential problem of cGAN is that it is difficult to detect the shape of an object when the resulting accuracy is low, which can seriously affect any further mathematical analysis of the detected crack. To tackle this issue, this paper proposes a method called improved cGAN with attention gate (ICGA) for roadway surface crack detection. To obtain a more accurate shape of the detected target object, ICGA establishes a multi-level model with independent stages. In the first stage, everything except the road is treated as noise and removed from the image. These images are stored in a new dataset. In the second stage, ICGA determines the cracks. Therefore, ICGA focuses on the redistribution of cracks, not the auxiliary elements in the image. ICGA adds two attention gates to a U-net architecture and improves the segmentation capacities of the generator in pix2pix. Extensive experimental results on dashboard camera images of the Unsupervised Llamas dataset show that our method has better performance than other state-of-the-art methods. Full article
(This article belongs to the Section Sensor Networks)
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Article
An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition
Sensors 2021, 21(21), 7404; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217404 - 07 Nov 2021
Viewed by 482
Abstract
Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific [...] Read more.
Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development. Full article
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Article
Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series
Sensors 2021, 21(21), 7403; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217403 - 07 Nov 2021
Viewed by 455
Abstract
In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes [...] Read more.
In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations. Full article
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Article
Diagnostic Accuracy of Cystic Lesions Using a Pre-Programmed Low-Dose and Standard-Dose Dental Cone-Beam Computed Tomography Protocol: An Ex Vivo Comparison Study
Sensors 2021, 21(21), 7402; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217402 - 07 Nov 2021
Viewed by 434
Abstract
Background: This study aimed to analyze the diagnostic reliability of radiographic assessment of cystic lesions using a pre-set, manufacturer-specific, low-dose mode compared to a standard-dose dental cone-beam computed tomography (CBCT) imaging protocol. Methods: Forty pig mandible models were prepared with cystic lesions and [...] Read more.
Background: This study aimed to analyze the diagnostic reliability of radiographic assessment of cystic lesions using a pre-set, manufacturer-specific, low-dose mode compared to a standard-dose dental cone-beam computed tomography (CBCT) imaging protocol. Methods: Forty pig mandible models were prepared with cystic lesions and underwent both CBCT protocols on an Orthophos SL Unit (Dentsply-Sirona, Bensheim, Germany). Qualitative and quantitative analysis of CBCT data was performed by twelve investigators independently in SIDEXIS 4 (Dentsply-Sirona) using a trial-specific digital examination software tool. Thereby, the effect of the two dose types on overall detectability rate, the visibility on a scale of 1 (very low) to 10 (very high) and the difference between measured radiographic and actual lesion size was assessed. Results: Low-dose CBCT imaging showed no significant differences considering detectability (78.8% vs. 81.6%) and visibility (9.16 vs. 9.19) of cystic lesions compared to the standard protocol. Both imaging protocols performed very similarly in lesion size assessment, with an apparent underestimation of the actual size. Conclusion: Low-dose protocols providing confidential diagnostic evaluation with an improved benefit–risk ratio according to the ALADA principle could become a promising alternative as a primary diagnostic tool as well as for radiological follow-up in the treatment of cystic lesions. Full article
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Article
Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection
Sensors 2021, 21(21), 7401; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217401 - 07 Nov 2021
Viewed by 370
Abstract
The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault [...] Read more.
The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N. Full article
(This article belongs to the Section Intelligent Sensors)
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Review
Optical Fiber Interferometers Based on Arc-Induced Long Period Gratings at INESC TEC
Sensors 2021, 21(21), 7400; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217400 - 07 Nov 2021
Viewed by 407
Abstract
In this work, we review the most important achievements of an INESC TEC long-period-grating-based fiber optic Michelson and Mach–Zehnder configuration modal interferometer with coherence addressing and heterodyne interrogation as a sensing structure for measuring environmental refractive index and temperature. The theory for Long [...] Read more.
In this work, we review the most important achievements of an INESC TEC long-period-grating-based fiber optic Michelson and Mach–Zehnder configuration modal interferometer with coherence addressing and heterodyne interrogation as a sensing structure for measuring environmental refractive index and temperature. The theory for Long Period Grating (LPG) interferometers and coherence addressing and heterodyne interrogation is presented. To increase the sensitivity to external refractive index and temperature, several LPG interferometers parameters are studied, including order of cladding mode, a reduction of the fiber diameter, different type of fiber, cavity length and the antisymmetric nature of cladding modes. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Portugal 2020-2021)
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Article
FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting
Sensors 2021, 21(21), 7399; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217399 - 07 Nov 2021
Viewed by 343
Abstract
The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting [...] Read more.
The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The ‘FGSC’ blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The ‘FGSC’ blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications. Full article
(This article belongs to the Section Intelligent Sensors)
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Article
Vital Signal Detection Using Multi-Radar for Reductions in Body Movement Effects
Sensors 2021, 21(21), 7398; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217398 - 07 Nov 2021
Viewed by 393
Abstract
Vital signal detection using multiple radars is proposed to reduce the signal degradation from a subject’s body movement. The phase variation in the transceiving signals of continuous-wave radar due to respiration and heartbeat is generated by the body surface movement of the organs [...] Read more.
Vital signal detection using multiple radars is proposed to reduce the signal degradation from a subject’s body movement. The phase variation in the transceiving signals of continuous-wave radar due to respiration and heartbeat is generated by the body surface movement of the organs monitored in the line-of-sight (LOS) of the radar. The body movement signals obtained by two adjacent radars can be assumed to be the same over a certain distance. However, the vital signals are different in each radar, and each radar has a different LOS because of the asymmetric movement of lungs and heart. The proposed method uses two adjacent radars with different LOS to obtain correlated signals that reinforce the difference in the asymmetrical movement of the organs. The correlated signals can improve the signal-to-noise ratio in vital signal detection because of a reduction in the body movement effect. Two radars at different frequencies in the 5.8 GHz band are implemented to reduce direct signal coupling. Measurement results using the radars arranged at angles of 30°, 45°, and 60° showed that the proposed method can detect the vital signals with a mean accuracy of 97.8% for the subject moving at a maximum velocity of 53.4 mm/s. Full article
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors II)
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Article
Lightweight Deep Neural Network Method for Water Body Extraction from High-Resolution Remote Sensing Images with Multisensors
Sensors 2021, 21(21), 7397; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217397 - 07 Nov 2021
Viewed by 456
Abstract
Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, [...] Read more.
Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water body extraction method based on lightweight MobileNetV2 is proposed and applied to multisensor high-resolution remote sensing images, such as GF-2, WorldView-2 and UAV orthoimages. This method was validated in two typical complex geographical scenes: water bodies for farmland irrigation, which have a broken shape and long and narrow area and are surrounded by many buildings in towns and villages; and water bodies in mountainous areas, which have undulating topography, vegetation coverage and mountain shadows all over. The results were compared with those of the support vector machine, random forest and U-Net models and also verified by generalization tests and the influence of spatial resolution changes. First, the results show that the F1-score and Kappa coefficients of the MobileNetV2 model extracting water bodies from three different high-resolution images were 0.75 and 0.72 for GF-2, 0.86 and 0.85 for Worldview-2 and 0.98 and 0.98 for UAV, respectively, which are higher than those of traditional machine learning models and U-Net. Second, the training time, number of parameters and calculation amount of the MobileNetV2 model were much lower than those of the U-Net model, which greatly improves the water body extraction efficiency. Third, in other more complex surface areas, the MobileNetV2 model still maintained relatively high accuracy of water body extraction. Finally, we tested the effects of multisensor models and found that training with lower and higher spatial resolution images combined can be beneficial, but that using just lower resolution imagery is ineffective. This study provides a reference for the efficient automation of water body classification and extraction under complex geographical environment conditions and can be extended to water resource investigation, management and planning. Full article
(This article belongs to the Section Sensing and Imaging)
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