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Sensors, Volume 23, Issue 5 (March-1 2023) – 509 articles

Cover Story (view full-size image): Onboard monitoring information, such as Axle Box Accelerometers (ABAs), can support the real-time condition assessment of railways. Such an assessment, albeit spatially dense, suffers from noise influences and uncertainties related to the underlying dynamics, which challenge a reliable assessment. We propose a new approach to improve the monitoring of railway welds by fusing expert feedback, obtained on critical weld samples, with ABA features. A Bayesian Logistic Regression (BLR) model, which comes with the benefit of uncertainty quantification, is compared against alternate approaches employing Random Forests and Binary Classifiers. We further demonstrate the importance of continuous asset monitoring to robustly track the evolution of conditions as a guide for preventive maintenance actions. View this paper
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13 pages, 6438 KiB  
Article
Enabling Modular Robotics with Secure Transducer Identification Based on Extended IEEE 21450 Transducer Electronic Datasheets
by Tobias Mitterer, Christian Lederer and Hubert Zangl
Sensors 2023, 23(5), 2873; https://doi.org/10.3390/s23052873 - 6 Mar 2023
Cited by 1 | Viewed by 1714
Abstract
In robotics, there are many different sensors and actuators mounted onto a robot which may also, in the case of modular robotics, be interchanged during operation. During development of new sensors or actuators, prototypes may also be mounted onto a robot to test [...] Read more.
In robotics, there are many different sensors and actuators mounted onto a robot which may also, in the case of modular robotics, be interchanged during operation. During development of new sensors or actuators, prototypes may also be mounted onto a robot to test functionality, where the new prototypes often have to be integrated manually into the robot environment. Proper, fast and secure identification of new sensor or actuator modules for the robot thus becomes important. In this work, a workflow to add new sensors or actuators to an existing robot environment while establishing trust in an automated manner using electronic datasheets has been developed. The new sensors or actuators are identified via near field communication (NFC) to the system and exchange security information via the same channel. By using electronic datasheets stored on the sensor or actuator, the device can be easily identified and trust can be established by using additional security information contained in the datasheet. In addition, the NFC hardware can simultaneously be used for wireless charging (WLC), thus allowing for wireless sensor and actuator modules. The developed workflow has been tested with prototype tactile sensors mounted onto a robotic gripper. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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15 pages, 4375 KiB  
Article
Advanced Pressure Compensation in High Accuracy NDIR Sensors for Environmental Studies
by Bakhram Gaynullin, Christine Hummelgård, Claes Mattsson, Göran Thungström and Henrik Rödjegård
Sensors 2023, 23(5), 2872; https://doi.org/10.3390/s23052872 - 6 Mar 2023
Cited by 4 | Viewed by 2067
Abstract
Measurements of atmospheric gas concentrations using of NDIR gas sensors requires compensation of ambient pressure variations to achieve reliable result. The extensively used general correction method is based on collecting data for varying pressures for a single reference concentration. This one-dimensional compensation approach [...] Read more.
Measurements of atmospheric gas concentrations using of NDIR gas sensors requires compensation of ambient pressure variations to achieve reliable result. The extensively used general correction method is based on collecting data for varying pressures for a single reference concentration. This one-dimensional compensation approach is valid for measurements carried out in gas concentrations close to reference concentration but will introduce significant errors for concentrations further away from the calibration point. For applications, requiring high accuracy, collecting, and storing calibration data at several reference concentrations can reduce the error. However, this method will cause higher demands on memory capacity and computational power, which is problematic for cost sensitive applications. We present here an advanced, but practical, algorithm for compensation of environmental pressure variations for relatively low-cost/high resolution NDIR systems. The algorithm consists of a two-dimensional compensation procedure, which widens the valid pressure and concentrations range but with a minimal need to store calibration data, compared to the general one-dimensional compensation method based on a single reference concentration. The implementation of the presented two-dimensional algorithm was verified at two independent concentrations. The results show a reduction in the compensation error from 5.1% and 7.3%, for the one-dimensional method, to −0.02% and 0.83% for the two-dimensional algorithm. In addition, the presented two-dimensional algorithm only requires calibration in four reference gases and the storing of four sets of polynomial coefficients used for calculations. Full article
(This article belongs to the Section Optical Sensors)
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17 pages, 9894 KiB  
Article
An Online 3D Modeling Method for Pose Measurement under Uncertain Dynamic Occlusion Based on Binocular Camera
by Xuanchang Gao, Junzhi Yu and Min Tan
Sensors 2023, 23(5), 2871; https://doi.org/10.3390/s23052871 - 6 Mar 2023
Cited by 2 | Viewed by 1752
Abstract
3D modeling plays a significant role in many industrial applications that require geometry information for pose measurements, such as grasping, spraying, etc. Due to random pose changes in the workpieces on the production line, demand for online 3D modeling has increased and many [...] Read more.
3D modeling plays a significant role in many industrial applications that require geometry information for pose measurements, such as grasping, spraying, etc. Due to random pose changes in the workpieces on the production line, demand for online 3D modeling has increased and many researchers have focused on it. However, online 3D modeling has not been entirely determined due to the occlusion of uncertain dynamic objects that disturb the modeling process. In this study, we propose an online 3D modeling method under uncertain dynamic occlusion based on a binocular camera. Firstly, focusing on uncertain dynamic objects, a novel dynamic object segmentation method based on motion consistency constraints is proposed, which achieves segmentation by random sampling and poses hypotheses clustering without any prior knowledge about objects. Then, in order to better register the incomplete point cloud of each frame, an optimization method based on local constraints of overlapping view regions and a global loop closure is introduced. It establishes constraints in covisibility regions between adjacent frames to optimize the registration of each frame, and it also establishes them between the global closed-loop frames to jointly optimize the entire 3D model. Finally, a confirmatory experimental workspace is designed and built to verify and evaluate our method. Our method achieves online 3D modeling under uncertain dynamic occlusion and acquires an entire 3D model. The pose measurement results further reflect the effectiveness. Full article
(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Mechatronics Systems)
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13 pages, 5150 KiB  
Article
Highly Sensitive and Selective Dopamine Determination in Real Samples Using Au Nanoparticles Decorated Marimo-like Graphene Microbead-Based Electrochemical Sensors
by Qichen Tian, Yuanbin She, Yangguang Zhu, Dan Dai, Mingjiao Shi, Wubo Chu, Tao Cai, Hsu-Sheng Tsai, He Li, Nan Jiang, Li Fu, Hongyan Xia, Cheng-Te Lin and Chen Ye
Sensors 2023, 23(5), 2870; https://doi.org/10.3390/s23052870 - 6 Mar 2023
Cited by 6 | Viewed by 2131
Abstract
A sensitive and selective electrochemical dopamine (DA) sensor has been developed using gold nanoparticles decorated marimo-like graphene (Au NP/MG) as a modifier of the glassy carbon electrode (GCE). Marimo-like graphene (MG) was prepared by partial exfoliation on the mesocarbon microbeads (MCMB) through molten [...] Read more.
A sensitive and selective electrochemical dopamine (DA) sensor has been developed using gold nanoparticles decorated marimo-like graphene (Au NP/MG) as a modifier of the glassy carbon electrode (GCE). Marimo-like graphene (MG) was prepared by partial exfoliation on the mesocarbon microbeads (MCMB) through molten KOH intercalation. Characterization via transmission electron microscopy confirmed that the surface of MG is composed of multi-layer graphene nanowalls. The graphene nanowalls structure of MG provided abundant surface area and electroactive sites. Electrochemical properties of Au NP/MG/GCE electrode were investigated by cyclic voltammetry and differential pulse voltammetry techniques. The electrode exhibited high electrochemical activity towards DA oxidation. The oxidation peak current increased linearly in proportion to the DA concentration in a range from 0.02 to 10 μM with a detection limit of 0.016 μM. The detection selectivity was carried out with the presence of 20 μM uric acid in goat serum real samples. This study demonstrated a promising method to fabricate DA sensor-based on MCMB derivatives as electrochemical modifiers. Full article
(This article belongs to the Special Issue State-of-the-Art Electrochemical Biosensors)
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16 pages, 6480 KiB  
Article
Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model
by Dilshod Bazarov Ravshan Ugli, Jingyeom Kim, Alaelddin F. Y. Mohammed and Joohyung Lee
Sensors 2023, 23(5), 2869; https://doi.org/10.3390/s23052869 - 6 Mar 2023
Cited by 5 | Viewed by 2174
Abstract
Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, [...] Read more.
Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, DL-based video surveillance services that require object movement and motion tracking (e.g., for detecting abnormal object behaviors) can consume a substantial amount of computing and memory capacity, such as (i) GPU computing resources for model inference and (ii) GPU memory resources for model loading. This paper presents a novel cognitive video surveillance management with long short-term memory (LSTM) model, denoted as the CogVSM framework. We consider DL-based video surveillance services in a hierarchical edge computing system. The proposed CogVSM forecasts object appearance patterns and smooths out the forecast results needed for an adaptive model release. Here, we aim to reduce standby GPU memory by model release while avoiding unnecessary model reloads for a sudden object appearance. CogVSM hinges on an LSTM-based deep learning architecture explicitly designed for future object appearance pattern prediction by training previous time-series patterns to achieve these objectives. By referring to the result of the LSTM-based prediction, the proposed framework controls the threshold time value in a dynamic manner by using an exponential weighted moving average (EWMA) technique. Comparative evaluations on both simulated and real-world measurement data on the commercial edge devices prove that the LSTM-based model in the CogVSM can achieve a high predictive accuracy, i.e., a root-mean-square error metric of 0.795. In addition, the suggested framework utilizes up to 32.1% less GPU memory than the baseline and 8.9% less than previous work. Full article
(This article belongs to the Special Issue Applications of Video Processing and Computer Vision Sensor II)
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21 pages, 8083 KiB  
Article
PointPainting: 3D Object Detection Aided by Semantic Image Information
by Zhentong Gao, Qiantong Wang, Zongxu Pan, Zhenyu Zhai and Hui Long
Sensors 2023, 23(5), 2868; https://doi.org/10.3390/s23052868 - 6 Mar 2023
Viewed by 2192
Abstract
A multi-modal 3D object-detection method, based on data from cameras and LiDAR, has become a subject of research interest. PointPainting proposes a method for improving point-cloud-based 3D object detectors using semantic information from RGB images. However, this method still needs to improve on [...] Read more.
A multi-modal 3D object-detection method, based on data from cameras and LiDAR, has become a subject of research interest. PointPainting proposes a method for improving point-cloud-based 3D object detectors using semantic information from RGB images. However, this method still needs to improve on the following two complications: first, there are faulty parts in the image semantic segmentation results, leading to false detections. Second, the commonly used anchor assigner only considers the intersection over union (IoU) between the anchors and ground truth boxes, meaning that some anchors contain few target LiDAR points assigned as positive anchors. In this paper, three improvements are suggested to address these complications. Specifically, a novel weighting strategy is proposed for each anchor in the classification loss. This enables the detector to pay more attention to anchors containing inaccurate semantic information. Then, SegIoU, which incorporates semantic information, instead of IoU, is proposed for the anchor assignment. SegIoU measures the similarity of the semantic information between each anchor and ground truth box, avoiding the defective anchor assignments mentioned above. In addition, a dual-attention module is introduced to enhance the voxelized point cloud. The experiments demonstrate that the proposed modules obtained significant improvements in various methods, consisting of single-stage PointPillars, two-stage SECOND-IoU, anchor-base SECOND, and an anchor-free CenterPoint on the KITTI dataset. Full article
(This article belongs to the Section Sensing and Imaging)
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21 pages, 3245 KiB  
Article
Real-Time Evaluation of Perception Uncertainty and Validity Verification of Autonomous Driving
by Mingliang Yang, Kun Jiang, Junze Wen, Liang Peng, Yanding Yang, Hong Wang, Mengmeng Yang, Xinyu Jiao and Diange Yang
Sensors 2023, 23(5), 2867; https://doi.org/10.3390/s23052867 - 6 Mar 2023
Cited by 2 | Viewed by 2490
Abstract
Deep neural network algorithms have achieved impressive performance in object detection. Real-time evaluation of perception uncertainty from deep neural network algorithms is indispensable for safe driving in autonomous vehicles. More research is required to determine how to assess the effectiveness and uncertainty of [...] Read more.
Deep neural network algorithms have achieved impressive performance in object detection. Real-time evaluation of perception uncertainty from deep neural network algorithms is indispensable for safe driving in autonomous vehicles. More research is required to determine how to assess the effectiveness and uncertainty of perception findings in real-time.This paper proposes a novel real-time evaluation method combining multi-source perception fusion and deep ensemble. The effectiveness of single-frame perception results is evaluated in real-time. Then, the spatial uncertainty of the detected objects and influencing factors are analyzed. Finally, the accuracy of spatial uncertainty is validated with the ground truth in the KITTI dataset. The research results show that the evaluation of perception effectiveness can reach 92% accuracy, and a positive correlation with the ground truth is found for both the uncertainty and the error. The spatial uncertainty is related to the distance and occlusion degree of detected objects. Full article
(This article belongs to the Section Vehicular Sensing)
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16 pages, 3012 KiB  
Article
A Novel Catheter Distal Contact Force Sensing for Cardiac Ablation Based on Fiber Bragg Grating with Temperature Compensation
by Yuyang Lou, Tianyu Yang, Dong Luo, Jianwei Wu and Yuming Dong
Sensors 2023, 23(5), 2866; https://doi.org/10.3390/s23052866 - 6 Mar 2023
Cited by 3 | Viewed by 2525
Abstract
Objective: To accurately achieve distal contact force, a novel temperature-compensated sensor is developed and integrated into an atrial fibrillation (AF) ablation catheter. Methods: A dual elastomer-based dual FBGs structure is used to differentiate the strain on the two FBGs to achieve temperature compensation, [...] Read more.
Objective: To accurately achieve distal contact force, a novel temperature-compensated sensor is developed and integrated into an atrial fibrillation (AF) ablation catheter. Methods: A dual elastomer-based dual FBGs structure is used to differentiate the strain on the two FBGs to achieve temperature compensation, and the design is optimized and validated by finite element simulation. Results: The designed sensor has a sensitivity of 90.5 pm/N, resolution of 0.01 N, and root–mean–square error (RMSE) of 0.02 N and 0.04 N for dynamic force loading and temperature compensation, respectively, and can stably measure distal contact forces with temperature disturbances. Conclusion: Due to the advantages, i.e., simple structure, easy assembly, low cost, and good robustness, the proposed sensor is suitable for industrial mass production. Full article
(This article belongs to the Special Issue Fiber Optic Sensing and Applications)
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12 pages, 2336 KiB  
Article
Bioluminescent-Triple-Enzyme-Based Biosensor with Lactate Dehydrogenase for Non-Invasive Training Load Monitoring
by Galina V. Zhukova, Oleg S. Sutormin, Irina E. Sukovataya, Natalya V. Maznyak and Valentina A. Kratasyuk
Sensors 2023, 23(5), 2865; https://doi.org/10.3390/s23052865 - 6 Mar 2023
Cited by 2 | Viewed by 1740
Abstract
Saliva is one of the most significant biological liquids for the development of a simple, rapid, and non-invasive biosensor for training load diagnostics. There is an opinion that enzymatic bioassays are more relevant in terms of biology. The present paper is aimed at [...] Read more.
Saliva is one of the most significant biological liquids for the development of a simple, rapid, and non-invasive biosensor for training load diagnostics. There is an opinion that enzymatic bioassays are more relevant in terms of biology. The present paper is aimed at investigating the effects of saliva samples, upon altering the lactate content, on the activity of a multi-enzyme, namely lactate dehydrogenase + NAD(P)H:FMN-oxidoreductase + luciferase (LDH + Red + Luc). Optimal enzymes and their substrate composition of the proposed multi-enzyme system were chosen. During the tests of the lactate dependence, the enzymatic bioassay showed good linearity to lactate in the range from 0.05 mM to 0.25 mM. The activity of the LDH + Red + Luc enzyme system was tested in the presence of 20 saliva samples taken from students whose lactate levels were compared by the Barker and Summerson colorimetric method. The results showed a good correlation. The proposed LDH + Red + Luc enzyme system could be a useful, competitive, and non-invasive tool for correct and rapid monitoring of lactate in saliva. This enzyme-based bioassay is easy to use, rapid, and has the potential to deliver point-of-care diagnostics in a cost-effective manner. Full article
(This article belongs to the Special Issue Biosensors for Surveillance and Diagnosis)
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19 pages, 7524 KiB  
Article
A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography
by Changhee Yun, Bomi Eom, Sungjun Park, Chanho Kim, Dohwan Kim, Farah Jabeen, Won Hwa Kim, Hye Jung Kim and Jaeil Kim
Sensors 2023, 23(5), 2864; https://doi.org/10.3390/s23052864 - 6 Mar 2023
Cited by 3 | Viewed by 1937
Abstract
In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as [...] Read more.
In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as the quality and interpretation of images can vary depending on the operator’s experience and proficiency. Therefore, computer-aided diagnosis technology can facilitate diagnosis by visualizing abnormal information such as tumors and masses in ultrasound images. In this study, we implemented deep learning-based anomaly detection methods for breast ultrasound images and validated their effectiveness in detecting abnormal regions. Herein, we specifically compared the sliced-Wasserstein autoencoder with two representative unsupervised learning models autoencoder and variational autoencoder. The anomalous region detection performance is estimated with the normal region labels. Our experimental results showed that the sliced-Wasserstein autoencoder model outperformed the anomaly detection performance of others. However, anomaly detection using the reconstruction-based approach may not be effective because of the occurrence of numerous false-positive values. In the following studies, reducing these false positives becomes an important challenge. Full article
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17 pages, 14109 KiB  
Article
A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images
by Tangfei Tao, Yuxiang Gao, Yaguang Jia, Ruiquan Chen, Ping Li and Guanghua Xu
Sensors 2023, 23(5), 2863; https://doi.org/10.3390/s23052863 - 6 Mar 2023
Cited by 1 | Viewed by 1596
Abstract
An error-related potential (ErrP) occurs when people’s expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related [...] Read more.
An error-related potential (ErrP) occurs when people’s expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain–computer interfaces. Full article
(This article belongs to the Section Biomedical Sensors)
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21 pages, 5853 KiB  
Article
Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach
by Alessandro Grecucci, Harold Dadomo, Gerardo Salvato, Gaia Lapomarda, Sara Sorella and Irene Messina
Sensors 2023, 23(5), 2862; https://doi.org/10.3390/s23052862 - 6 Mar 2023
Cited by 5 | Viewed by 2742
Abstract
Borderline personality disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study, we applied for the first time a combination of an unsupervised [...] Read more.
Borderline personality disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study, we applied for the first time a combination of an unsupervised machine learning approach known as multimodal canonical correlation analysis plus joint independent component analysis (mCCA+jICA), in combination with a supervised machine learning approach known as random forest, to possibly find covarying gray matter and white matter (GM-WM) circuits that separate BPD from controls and that are also predictive of this diagnosis. The first analysis was used to decompose the brain into independent circuits of covarying grey and white matter concentrations. The second method was used to develop a predictive model able to correctly classify new unobserved BPD cases based on one or more circuits derived from the first analysis. To this aim, we analyzed the structural images of patients with BPD and matched healthy controls (HCs). The results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in both GM and WM circuits related to early traumatic experiences and specific symptoms. Full article
(This article belongs to the Special Issue Brain Activity Monitoring and Measurement)
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19 pages, 7913 KiB  
Article
Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
by Veton Hamza, Bojan Stopar, Oskar Sterle and Polona Pavlovčič-Prešeren
Sensors 2023, 23(5), 2861; https://doi.org/10.3390/s23052861 - 6 Mar 2023
Cited by 10 | Viewed by 3241
Abstract
Low-cost dual-frequency global navigation satellite system (GNSS) receivers have recently been tested in various positioning applications. Considering that these sensors can now provide high positioning accuracy at a lower cost, they can be considered an alternative to high-quality geodetic GNSS devices. The main [...] Read more.
Low-cost dual-frequency global navigation satellite system (GNSS) receivers have recently been tested in various positioning applications. Considering that these sensors can now provide high positioning accuracy at a lower cost, they can be considered an alternative to high-quality geodetic GNSS devices. The main objectives of this work were to analyze the differences between geodetic and low-cost calibrated antennas on the quality of observations from low-cost GNSS receivers and to evaluate the performance of low-cost GNSS devices in urban areas. In this study, a simple RTK2B V1 board u-blox ZED-F9P (Thalwil, Switzerland) was tested in combination with a low-cost calibrated and geodetic antenna in open-sky and adverse conditions in urban areas, while a high-quality geodetic GNSS device was used as a reference for comparison. The results of the observation quality check show that low-cost GNSS instruments have a lower carrier-to-noise ratio (C/N0) than geodetic instruments, especially in the urban areas where the difference is larger and in favor of the geodetic GNSS instruments. The root-mean-square error (RMSE) of the multipath error in the open sky is twice as high for low-cost as for geodetic instruments, while this difference is up to four times greater in urban areas. The use of a geodetic GNSS antenna does not show a significant improvement in the C/N0 and multipath of low-cost GNSS receivers. However, the ambiguity fix ratio is larger when geodetic antennas are used, with a difference of 1.5% and 18.4% for the open-sky and urban conditions, respectively. It should be noted that float solutions may become more evident when low-cost equipment is used, especially for short sessions and in urban areas with more multipath. In relative positioning mode, low-cost GNSS devices were able to provide horizontal accuracy lower than 10 mm in urban areas in 85% of sessions, while the vertical and spatial accuracy was lower than 15 mm in 82.5% and 77.5% of the sessions, respectively. In the open sky, low-cost GNSS receivers achieve a horizontal, vertical, and spatial accuracy of 5 mm for all sessions considered. In RTK mode, positioning accuracy varies between 10–30 mm in the open-sky and urban areas, while better performance is demonstrated for the former. Full article
(This article belongs to the Special Issue Advances in GNSS Positioning and GNSS Remote Sensing)
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26 pages, 8373 KiB  
Article
Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management
by Gerald K. Ijemaru, Li-Minn Ang and Kah Phooi Seng
Sensors 2023, 23(5), 2860; https://doi.org/10.3390/s23052860 - 6 Mar 2023
Cited by 9 | Viewed by 2272
Abstract
Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city [...] Read more.
Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics. Full article
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29 pages, 6191 KiB  
Review
Natural Intelligence as the Brain of Intelligent Systems
by Mahdi Naghshvarianjahromi, Shiva Kumar and Mohammed Jamal Deen
Sensors 2023, 23(5), 2859; https://doi.org/10.3390/s23052859 - 6 Mar 2023
Cited by 2 | Viewed by 1774
Abstract
This article discusses the concept and applications of cognitive dynamic systems (CDS), which are a type of intelligent system inspired by the brain. There are two branches of CDS, one for linear and Gaussian environments (LGEs), such as cognitive radio and cognitive radar, [...] Read more.
This article discusses the concept and applications of cognitive dynamic systems (CDS), which are a type of intelligent system inspired by the brain. There are two branches of CDS, one for linear and Gaussian environments (LGEs), such as cognitive radio and cognitive radar, and another one for non-Gaussian and nonlinear environments (NGNLEs), such as cyber processing in smart systems. Both branches use the same principle, called the perception action cycle (PAC), to make decisions. The focus of this review is on the applications of CDS, including cognitive radios, cognitive radar, cognitive control, cyber security, self-driving cars, and smart grids for LGEs. For NGNLEs, the article reviews the use of CDS in smart e-healthcare applications and software-defined optical communication systems (SDOCS), such as smart fiber optic links. The results of implementing CDS in these systems are very promising, with improved accuracy, performance, and lower computational costs. For example, CDS implementation in cognitive radars achieved a range estimation error that is as good as 0.47 (m) and a velocity estimation error of 3.30 (m/s), outperforming traditional active radars. Similarly, CDS implementation in smart fiber optic links improved the quality factor by 7 dB and the maximum achievable data rate by 43% compared to those of other mitigation techniques. Full article
(This article belongs to the Section Intelligent Sensors)
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26 pages, 27778 KiB  
Article
Home Chimney Pinwheels (HCP) as Steh and Remote Monitoring for Smart Building IoT and WSN Applications
by Ajibike Eunice Akin-Ponnle, Paulo Capitão, Ricardo Torres and Nuno Borges Carvalho
Sensors 2023, 23(5), 2858; https://doi.org/10.3390/s23052858 - 6 Mar 2023
Cited by 3 | Viewed by 2394
Abstract
Smart, and ultra-low energy consuming Internet of Things (IoTs), wireless sensor networks (WSN), and autonomous devices are being deployed to smart buildings and cities, which require continuous power supply, whereas battery usage has accompanying environmental problems, coupled with additional maintenance cost. We present [...] Read more.
Smart, and ultra-low energy consuming Internet of Things (IoTs), wireless sensor networks (WSN), and autonomous devices are being deployed to smart buildings and cities, which require continuous power supply, whereas battery usage has accompanying environmental problems, coupled with additional maintenance cost. We present Home Chimney Pinwheels (HCP) as the Smart Turbine Energy Harvester (STEH) for wind; and Cloud-based remote monitoring of its output data. The HCP commonly serves as an external cap to home chimney exhaust outlets; they have very low inertia to wind; and are available on the rooftops of some buildings. Here, an electromagnetic converter adapted from a brushless DC motor was mechanically fastened to the circular base of an 18-blade HCP. In simulated wind, and rooftop experiments, an output voltage of 0.3 V to 16 V was realised for a wind speed between 0.6 to 16 km/h. This is sufficient to operate low-power IoT devices deployed around a smart city. The harvester was connected to a power management unit and its output data was remotely monitored via the IoT analytic Cloud platform “ThingSpeak” by means of LoRa transceivers, serving as sensors; while also obtaining supply from the harvester. The HCP can be a battery-less “stand-alone” low-cost STEH, with no grid connection, and can be installed as attachments to IoT or wireless sensors nodes in smart buildings and cities. Full article
(This article belongs to the Collection Wireless Sensor Networks towards the Internet of Things)
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11 pages, 2951 KiB  
Communication
A Dew-Condensation Sensor Exploiting Local Variations in the Relative Refractive Index on the Dew-Friendly Surface of a Waveguide
by Subin Hwa, Eun-Seon Sim, Jun-Hee Na, Ik-Hoon Jang, Jin-Hyuk Kwon and Min-Hoi Kim
Sensors 2023, 23(5), 2857; https://doi.org/10.3390/s23052857 - 6 Mar 2023
Viewed by 1823
Abstract
We propose a sensor technology for detecting dew condensation, which exploits a variation in the relative refractive index on the dew-friendly surface of an optical waveguide. The dew-condensation sensor is composed of a laser, waveguide, medium (i.e., filling material for the waveguide), and [...] Read more.
We propose a sensor technology for detecting dew condensation, which exploits a variation in the relative refractive index on the dew-friendly surface of an optical waveguide. The dew-condensation sensor is composed of a laser, waveguide, medium (i.e., filling material for the waveguide), and photodiode. The formation of dewdrops on the waveguide surface causes local increases in the relative refractive index accompanied by the transmission of the incident light rays, hence reducing the light intensity inside the waveguide. In particular, the dew-friendly surface of the waveguide is obtained by filling the interior of the waveguide with liquid H2O, i.e., water. A geometric design for the sensor was first carried out considering the curvature of the waveguide and the incident angles of the light rays. Moreover, the optical suitability of waveguide media with various absolute refractive indices, i.e., water, air, oil, and glass, were evaluated through simulation tests. In actual experiments, the sensor with the water-filled waveguide displayed a wider gap between the measured photocurrent levels under conditions with and without dew, than those with the air- and glass-filled waveguides, as a result of the relatively high specific heat of the water. The sensor with the water-filled waveguide exhibited excellent accuracy and repeatability as well. Full article
(This article belongs to the Section Physical Sensors)
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14 pages, 4036 KiB  
Article
Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery
by Tao Zhang, Yuge Bi, Xiangbing Zhu and Xinchao Gao
Sensors 2023, 23(5), 2856; https://doi.org/10.3390/s23052856 - 6 Mar 2023
Cited by 4 | Viewed by 1449
Abstract
Desert steppes are the last barrier to protecting the steppe ecosystem. However, existing grassland monitoring methods still mainly use traditional monitoring methods, which have certain limitations in the monitoring process. Additionally, the existing deep learning classification models of desert and grassland still use [...] Read more.
Desert steppes are the last barrier to protecting the steppe ecosystem. However, existing grassland monitoring methods still mainly use traditional monitoring methods, which have certain limitations in the monitoring process. Additionally, the existing deep learning classification models of desert and grassland still use traditional convolutional neural networks for classification, which cannot adapt to the classification task of irregular ground objects, which limits the classification performance of the model. To address the above problems, this paper uses a UAV hyperspectral remote sensing platform for data acquisition and proposes a spatial neighborhood dynamic graph convolution network (SN_DGCN) for degraded grassland vegetation community classification. The results show that the proposed classification model had the highest classification accuracy compared to the seven classification models of MLP, 1DCNN, 2DCNN, 3DCNN, Resnet18, Densenet121, and SN_GCN; its OA, AA, and kappa were 97.13%, 96.50%, and 96.05% in the case of only 10 samples per class of features, respectively; The classification performance was stable under different numbers of training samples, had better generalization ability in the classification task of small samples, and was more effective for the classification task of irregular features. Meanwhile, the latest desert grassland classification models were also compared, which fully demonstrated the superior classification performance of the proposed model in this paper. The proposed model provides a new method for the classification of vegetation communities in desert grasslands, which is helpful for the management and restoration of desert steppes. Full article
(This article belongs to the Section Remote Sensors)
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19 pages, 3939 KiB  
Article
Multiple Dipole Source Position and Orientation Estimation Using Non-Invasive EEG-like Signals
by Saina Namazifard and Kamesh Subbarao
Sensors 2023, 23(5), 2855; https://doi.org/10.3390/s23052855 - 6 Mar 2023
Cited by 8 | Viewed by 1631
Abstract
The problem of precisely estimating the position and orientation of multiple dipoles using synthetic EEG signals is considered in this paper. After determining a proper forward model, a nonlinear constrained optimization problem with regularization is solved, and the results are compared with a [...] Read more.
The problem of precisely estimating the position and orientation of multiple dipoles using synthetic EEG signals is considered in this paper. After determining a proper forward model, a nonlinear constrained optimization problem with regularization is solved, and the results are compared with a widely used research code, namely EEGLAB. A thorough sensitivity analysis of the estimation algorithm to the parameters (such as the number of samples and sensors) in the assumed signal measurement model is conducted. To confirm the efficacy of the proposed source identification algorithm on any category of data sets, three different kinds of data-synthetic model data, visually evoked clinical EEG data, and seizure clinical EEG data are used. Furthermore, the algorithm is tested on both the spherical head model and the realistic head model based on the MNI coordinates. The numerical results and comparisons with the EEGLAB show very good agreement, with little pre-processing required for the acquired data. Full article
(This article belongs to the Section Biomedical Sensors)
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16 pages, 1559 KiB  
Article
Morphological Autoencoders for Beat-by-Beat Atrial Fibrillation Detection Using Single-Lead ECG
by Rafael Silva, Ana Fred and Hugo Plácido da Silva
Sensors 2023, 23(5), 2854; https://doi.org/10.3390/s23052854 - 6 Mar 2023
Cited by 2 | Viewed by 2054
Abstract
Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to [...] Read more.
Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them. In this work we show that morphological features extracted using a Sparse AE are sufficient to distinguish AFib from Normal Sinus Rhythm (NSR) beats. In addition to the morphological features, rhythm information was included in the model using a proposed short-term feature called Local Change of Successive Differences (LCSD). Using single-lead ECG recordings from two referenced public databases, and with features from the AE, the model was able to achieve an F1-score of 88.8%. These results show that morphological features appear to be a distinct and sufficient factor for detecting AFib in ECG recordings, especially when designed for patient-specific applications. This is an advantage over state-of-the-art algorithms that need longer acquisition times to extract engineered rhythm features, which also requires careful preprocessing steps. To the best of our knowledge, this is the first work that presents a near real-time morphological approach for AFib detection under naturalistic ECG acquisition with a mobile device. Full article
(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)
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23 pages, 20417 KiB  
Article
Sign2Pose: A Pose-Based Approach for Gloss Prediction Using a Transformer Model
by Jennifer Eunice, Andrew J, Yuichi Sei and D. Jude Hemanth
Sensors 2023, 23(5), 2853; https://doi.org/10.3390/s23052853 - 6 Mar 2023
Cited by 7 | Viewed by 2621
Abstract
Word-level sign language recognition (WSLR) is the backbone for continuous sign language recognition (CSLR) that infers glosses from sign videos. Finding the relevant gloss from the sign sequence and detecting explicit boundaries of the glosses from sign videos is a persistent challenge. In [...] Read more.
Word-level sign language recognition (WSLR) is the backbone for continuous sign language recognition (CSLR) that infers glosses from sign videos. Finding the relevant gloss from the sign sequence and detecting explicit boundaries of the glosses from sign videos is a persistent challenge. In this paper, we propose a systematic approach for gloss prediction in WLSR using the Sign2Pose Gloss prediction transformer model. The primary goal of this work is to enhance WLSR’s gloss prediction accuracy with reduced time and computational overhead. The proposed approach uses hand-crafted features rather than automated feature extraction, which is computationally expensive and less accurate. A modified key frame extraction technique is proposed that uses histogram difference and Euclidean distance metrics to select and drop redundant frames. To enhance the model’s generalization ability, pose vector augmentation using perspective transformation along with joint angle rotation is performed. Further, for normalization, we employed YOLOv3 (You Only Look Once) to detect the signing space and track the hand gestures of the signers in the frames. The proposed model experiments on WLASL datasets achieved the top 1% recognition accuracy of 80.9% in WLASL100 and 64.21% in WLASL300. The performance of the proposed model surpasses state-of-the-art approaches. The integration of key frame extraction, augmentation, and pose estimation improved the performance of the proposed gloss prediction model by increasing the model’s precision in locating minor variations in their body posture. We observed that introducing YOLOv3 improved gloss prediction accuracy and helped prevent model overfitting. Overall, the proposed model showed 17% improved performance in the WLASL 100 dataset. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition II)
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23 pages, 14661 KiB  
Article
A Non-Equal Time Interval Incremental Motion Prediction Method for Maritime Autonomous Surface Ships
by Zhijie Zhou, Haixiang Xu, Hui Feng and Wenjuan Li
Sensors 2023, 23(5), 2852; https://doi.org/10.3390/s23052852 - 6 Mar 2023
Cited by 1 | Viewed by 1403
Abstract
Recent technological advancements facilitate the autonomous navigation of maritime surface ships. The accurate data given by a range of various sensors serve as the primary assurance of a voyage’s safety. Nevertheless, as sensors have various sample rates, they cannot obtain information at the [...] Read more.
Recent technological advancements facilitate the autonomous navigation of maritime surface ships. The accurate data given by a range of various sensors serve as the primary assurance of a voyage’s safety. Nevertheless, as sensors have various sample rates, they cannot obtain information at the same time. Fusion decreases the accuracy and reliability of perceptual data if different sensor sample rates are not taken into account. Hence, it is helpful to increase the quality of the fusion information to precisely anticipate the motion status of ships at the sampling time of each sensor. This paper proposes a non-equal time interval incremental prediction method. In this method, the high dimensionality of the estimated state and nonlinearity of the kinematic equation are taken into consideration. First, the cubature Kalman filter is employed to estimate a ship’s motion at equal intervals based on the ship’s kinematic equation. Next, a ship motion state predictor based on a long short-term memory network structure is created, using the increment and time interval of the historical estimation sequence as the network input and the increment of the motion state at the projected time as the network output. The suggested technique can lessen the effect of the speed difference between the test set and the training set on the prediction accuracy compared with the traditional long short-term memory prediction method. Finally, comparison experiments are carried out to validate the precision and effectiveness of the proposed approach. The experimental results show that the root-mean-square error coefficient of the prediction error is decreased on average by roughly 78% for various modes and speeds when compared with the conventional non-incremental long short-term memory prediction approach. Additionally, the proposed prediction technology and the traditional approach have virtually the same algorithm times, which may fulfill the real engineering requirements. Full article
(This article belongs to the Special Issue Algorithms, Systems and Applications of Smart Sensor Networks)
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17 pages, 3557 KiB  
Article
Detecting Grapevine Virus Infections in Red and White Winegrape Canopies Using Proximal Hyperspectral Sensing
by Yeniu Mickey Wang, Bertram Ostendorf and Vinay Pagay
Sensors 2023, 23(5), 2851; https://doi.org/10.3390/s23052851 - 6 Mar 2023
Cited by 3 | Viewed by 2381
Abstract
Grapevine virus-associated disease such as grapevine leafroll disease (GLD) affects grapevine health worldwide. Current diagnostic methods are either highly costly (laboratory-based diagnostics) or can be unreliable (visual assessments). Hyperspectral sensing technology is capable of measuring leaf reflectance spectra that can be used for [...] Read more.
Grapevine virus-associated disease such as grapevine leafroll disease (GLD) affects grapevine health worldwide. Current diagnostic methods are either highly costly (laboratory-based diagnostics) or can be unreliable (visual assessments). Hyperspectral sensing technology is capable of measuring leaf reflectance spectra that can be used for the non-destructive and rapid detection of plant diseases. The present study used proximal hyperspectral sensing to detect virus infection in Pinot Noir (red-berried winegrape cultivar) and Chardonnay (white-berried winegrape cultivar) grapevines. Spectral data were collected throughout the grape growing season at six timepoints per cultivar. Partial least squares-discriminant analysis (PLS-DA) was used to build a predictive model of the presence or absence of GLD. The temporal change of canopy spectral reflectance showed that the harvest timepoint had the best prediction result. Prediction accuracies of 96% and 76% were achieved for Pinot Noir and Chardonnay, respectively. Our results provide valuable information on the optimal time for GLD detection. This hyperspectral method can also be deployed on mobile platforms including ground-based vehicles and unmanned aerial vehicles (UAV) for large-scale disease surveillance in vineyards. Full article
(This article belongs to the Special Issue Methodologies Used in Hyperspectral Remote Sensing in Agriculture)
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6 pages, 1805 KiB  
Communication
Epoxy-Coated Side-Polished Fiber-Optic Temperature Sensor for Cryogenic Conditions
by Umesh Sampath and Minho Song
Sensors 2023, 23(5), 2850; https://doi.org/10.3390/s23052850 - 6 Mar 2023
Viewed by 1409
Abstract
We propose coating side-polished optical fiber (SPF) with epoxy polymer to form a fiber-optic sensor for cryogenic temperature measuring applications. The thermo-optic effect of the epoxy polymer coating layer enhances the interaction between the SPF evanescent field and surrounding medium, considerably improving the [...] Read more.
We propose coating side-polished optical fiber (SPF) with epoxy polymer to form a fiber-optic sensor for cryogenic temperature measuring applications. The thermo-optic effect of the epoxy polymer coating layer enhances the interaction between the SPF evanescent field and surrounding medium, considerably improving the temperature sensitivity and robustness of the sensor head in a very low-temperature environment. In tests, due to the evanescent field–polymer coating interlinkage, transmitted optical intensity variation of 5 dB and an average sensitivity of 0.024 dB/K were obtained in the 90–298 K range. Full article
(This article belongs to the Special Issue Applications of Optical Fiber Sensors and Measurement Systems)
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16 pages, 770 KiB  
Article
Self-Excited Microcantilever with Higher Mode Using Band-Pass Filter
by Yuji Hyodo and Hiroshi Yabuno
Sensors 2023, 23(5), 2849; https://doi.org/10.3390/s23052849 - 6 Mar 2023
Cited by 1 | Viewed by 1564
Abstract
Microresonators have a variety of scientific and industrial applications. The measurement methods based on the natural frequency shift of a resonator have been studied for a wide range of applications, including the detection of the microscopic mass and measurements of viscosity and stiffness. [...] Read more.
Microresonators have a variety of scientific and industrial applications. The measurement methods based on the natural frequency shift of a resonator have been studied for a wide range of applications, including the detection of the microscopic mass and measurements of viscosity and stiffness. A higher natural frequency of the resonator realizes an increase in the sensitivity and a higher-frequency response of the sensors. In the present study, by utilizing the resonance of a higher mode, we propose a method to produce the self-excited oscillation with a higher natural frequency without downsizing the resonator. We establish the feedback control signal for the self-excited oscillation using the band-pass filter so that the signal consists of only the frequency corresponding to the desired excitation mode. It results that careful position setting of the sensor for constructing a feedback signal, which is needed in the method based on the mode shape, is not necessary. By the theoretical analysis of the equations governing the dynamics of the resonator coupled with the band-pass filter, it is clarified that the self-excited oscillation is produced with the second mode. Furthermore, the validity of the proposed method is experimentally confirmed by an apparatus using a microcantilever. Full article
(This article belongs to the Special Issue Feature Papers in Physical Sensors 2022)
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12 pages, 1436 KiB  
Article
Pre-Trained Joint Model for Intent Classification and Slot Filling with Semantic Feature Fusion
by Yan Chen and Zhenghang Luo
Sensors 2023, 23(5), 2848; https://doi.org/10.3390/s23052848 - 6 Mar 2023
Cited by 3 | Viewed by 2712
Abstract
The comprehension of spoken language is a crucial aspect of dialogue systems, encompassing two fundamental tasks: intent classification and slot filling. Currently, the joint modeling approach for these two tasks has emerged as the dominant method in spoken language understanding modeling. However, the [...] Read more.
The comprehension of spoken language is a crucial aspect of dialogue systems, encompassing two fundamental tasks: intent classification and slot filling. Currently, the joint modeling approach for these two tasks has emerged as the dominant method in spoken language understanding modeling. However, the existing joint models have limitations in terms of their relevancy and utilization of contextual semantic features between the multiple tasks. To address these limitations, a joint model based on BERT and semantic fusion (JMBSF) is proposed. The model employs pre-trained BERT to extract semantic features and utilizes semantic fusion to associate and integrate this information. The results of experiments on two benchmark datasets, ATIS and Snips, in spoken language comprehension demonstrate that the proposed JMBSF model attains 98.80% and 99.71% intent classification accuracy, 98.25% and 97.24% slot-filling F1-score, and 93.40% and 93.57% sentence accuracy, respectively. These results reveal a significant improvement compared to other joint models. Furthermore, comprehensive ablation studies affirm the effectiveness of each component in the design of JMBSF. Full article
(This article belongs to the Section Intelligent Sensors)
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4 pages, 195 KiB  
Editorial
Editorial–Special Issue on “Sensor Technology for Enhancing Training and Performance in Sport”
by Pui Wah Kong
Sensors 2023, 23(5), 2847; https://doi.org/10.3390/s23052847 - 6 Mar 2023
Cited by 1 | Viewed by 1406
Abstract
Sensor technology opens up exciting opportunities for sports [...] Full article
(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)
13 pages, 4017 KiB  
Article
An Adaptive Pedaling Assistive Device for Asymmetric Torque Assistant in Cycling
by Jesse Lozinski, Seyed Hamidreza Heidary, Scott C. E. Brandon and Amin Komeili
Sensors 2023, 23(5), 2846; https://doi.org/10.3390/s23052846 - 6 Mar 2023
Cited by 3 | Viewed by 2169
Abstract
Dynamic loads have short and long-term effects in the rehabilitation of lower limb joints. However, an effective exercise program for lower limb rehabilitation has been debated for a long time. Cycling ergometers were instrumented and used as a tool to mechanically load the [...] Read more.
Dynamic loads have short and long-term effects in the rehabilitation of lower limb joints. However, an effective exercise program for lower limb rehabilitation has been debated for a long time. Cycling ergometers were instrumented and used as a tool to mechanically load the lower limbs and track the joint mechano-physiological response in rehabilitation programs. Current cycling ergometers apply symmetrical loading to the limbs, which may not reflect the actual load-bearing capacity of each limb, as in Parkinson’s and Multiple Sclerosis diseases. Therefore, the present study aimed to develop a new cycling ergometer capable of applying asymmetric loads to the limbs and validate its function using human tests. The instrumented force sensor and crank position sensing system recorded the kinetics and kinematics of pedaling. This information was used to apply an asymmetric assistive torque only to the target leg using an electric motor. The performance of the proposed cycling ergometer was studied during a cycling task at three different intensities. It was shown that the proposed device reduced the pedaling force of the target leg by 19% to 40%, depending on the exercise intensity. This reduction in pedal force caused a significant reduction in the muscle activity of the target leg (p < 0.001), without affecting the muscle activity of the non-target leg. These results demonstrated that the proposed cycling ergometer device is capable of applying asymmetric loading to lower limbs, and thus has the potential to improve the outcome of exercise interventions in patients with asymmetric function in lower limbs. Full article
(This article belongs to the Special Issue Sensors and Actuators for Wearable and Implantable Devices)
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19 pages, 4136 KiB  
Article
LiDAR-as-Camera for End-to-End Driving
by Ardi Tampuu, Romet Aidla, Jan Aare van Gent and Tambet Matiisen
Sensors 2023, 23(5), 2845; https://doi.org/10.3390/s23052845 - 6 Mar 2023
Cited by 7 | Viewed by 2911
Abstract
The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving commands, e.g., steering [...] Read more.
The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving commands, e.g., steering angle, as output. However, simulation studies have shown that depth-sensing can make the end-to-end driving task easier. On a real car, combining depth and visual information can be challenging due to the difficulty of obtaining good spatial and temporal alignment of the sensors. To alleviate alignment problems, Ouster LiDARs can output surround-view LiDAR images with depth, intensity, and ambient radiation channels. These measurements originate from the same sensor, rendering them perfectly aligned in time and space. The main goal of our study is to investigate how useful such images are as inputs to a self-driving neural network. We demonstrate that such LiDAR images are sufficient for the real-car road-following task. Models using these images as input perform at least as well as camera-based models in the tested conditions. Moreover, LiDAR images are less sensitive to weather conditions and lead to better generalization. In a secondary research direction, we reveal that the temporal smoothness of off-policy prediction sequences correlates with the actual on-policy driving ability equally well as the commonly used mean absolute error. Full article
(This article belongs to the Special Issue Advances in Sensor Related Technologies for Autonomous Driving)
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24 pages, 783 KiB  
Review
Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions
by Mohammed Ayalew Belay, Sindre Stenen Blakseth, Adil Rasheed and Pierluigi Salvo Rossi
Sensors 2023, 23(5), 2844; https://doi.org/10.3390/s23052844 - 6 Mar 2023
Cited by 11 | Viewed by 13297
Abstract
The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of [...] Read more.
The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of multivariate time series that may capture normal conditions or anomalies. Multivariate Time Series Anomaly Detection (MTSAD), i.e., the ability to identify normal or irregular operative conditions of a system through the analysis of data from multiple sensors, is crucial in many fields. However, MTSAD is challenging due to the need for simultaneous analysis of temporal (intra-sensor) patterns and spatial (inter-sensor) dependencies. Unfortunately, labeling massive amounts of data is practically impossible in many real-world situations of interest (e.g., the reference ground truth may not be available or the amount of data may exceed labeling capabilities); therefore, robust unsupervised MTSAD is desirable. Recently, advanced techniques in machine learning and signal processing, including deep learning methods, have been developed for unsupervised MTSAD. In this article, we provide an extensive review of the current state of the art with a theoretical background about multivariate time-series anomaly detection. A detailed numerical evaluation of 13 promising algorithms on two publicly available multivariate time-series datasets is presented, with advantages and shortcomings highlighted. Full article
(This article belongs to the Special Issue Signal Processing and AI in Sensor Networks and IoT)
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