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Sensors, Volume 20, Issue 15 (August-1 2020) – 265 articles

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Open AccessArticle
Micromachined Vibrating Ring Gyroscope Architecture with High-Linearity, Low Quadrature Error and Improved Mode Ordering
Sensors 2020, 20(15), 4327; https://doi.org/10.3390/s20154327 (registering DOI) - 03 Aug 2020
Abstract
A new micromachined vibrating ring gyroscope (VRG) architecture with low quadrature error and high-linearity is proposed, which successfully optimizes the working modes to first order resonance mode of the structure. The improved mode ordering can significantly reduce the vibration sensitivity of the device [...] Read more.
A new micromachined vibrating ring gyroscope (VRG) architecture with low quadrature error and high-linearity is proposed, which successfully optimizes the working modes to first order resonance mode of the structure. The improved mode ordering can significantly reduce the vibration sensitivity of the device by adopting the hinge-frame mechanism. The frequency difference ratio is introduced to represent the optimization effect of modal characteristic. Furthermore, the influence of the structural parameters of hinge-frame mechanism on frequency difference ratio is clarified through analysis of related factors, which contributes to a more effective design of hinge-frame structure. The designed VRG architecture accomplishes the goal of high-linearity by using combination hinge and variable-area capacitance strategy, in contrast to the conventional approach via variable-separation drive/sense strategy. Finally, finite element method (FEM) simulations are carried out to investigate the stiffness, modal analysis, linearity, and decoupling characteristics of the design. The simulation results are sufficiently in agreement with theoretical calculations. Meanwhile, the hinge-frame mechanism can be widely applied in other existing ring gyroscopes, and the new design provides a path towards ultra-high performance for VRG. Full article
(This article belongs to the Special Issue MEMS Actuators and Sensors 2020)
Open AccessPerspective
Ion Selective Amperometric Biosensors for Environmental Analysis of Nitrate, Nitrite and Sulfate
Sensors 2020, 20(15), 4326; https://doi.org/10.3390/s20154326 (registering DOI) - 03 Aug 2020
Abstract
Inorganic ions that can be redox-transformed by living cells can be sensed by biosensors, where the redox transformation gives rise to a current in a measuring circuit. Such biosensors may be based on enzymes, or they may be based on application of whole [...] Read more.
Inorganic ions that can be redox-transformed by living cells can be sensed by biosensors, where the redox transformation gives rise to a current in a measuring circuit. Such biosensors may be based on enzymes, or they may be based on application of whole cells. In this review focus will be on biosensors for the environmentally important ions NO3, NO2, and SO42−, and for comparison alternative sensor-based detection will also be mentioned. The developed biosensors are generally characterized by a high degree of specificity, but unfortunately also by relatively short lifetimes. There are several investigations where biosensor measurement of NO3 and NO2 have given new insight into the functioning of nitrogen transformations in man-made and natural environments such as sediments and biofilms, but the biosensors have not become routine tools. Future modifications resulting in better long-term stability may enable such general use. Full article
(This article belongs to the Special Issue Ion Selective Electrodes and Optodes)
Open AccessArticle
Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks
Sensors 2020, 20(15), 4325; https://doi.org/10.3390/s20154325 - 03 Aug 2020
Abstract
Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep [...] Read more.
Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep learning approaches, which are fast and accurate at the same time, are proposed to inspect the key components of railway track including rail, bolt, and clip. The inspection results show that the enhanced model, the second version of you only look once (YOLOv2), presents the best component detection performance with 93% mean average precision (mAP) at 35 image per second (IPS), whereas the feature pyramid network (FPN) based model provides a smaller mAP and much longer inference time. Besides, the detection performances of more deep learning approaches are evaluated under varying input sizes, where larger input size usually improves the detection accuracy but results in a longer inference time. Overall, the YOLO series models could achieve faster speed under the same detection accuracy. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs
Sensors 2020, 20(15), 4324; https://doi.org/10.3390/s20154324 - 03 Aug 2020
Abstract
This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence [...] Read more.
This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft. Full article
(This article belongs to the Special Issue Sensors for Aerial Unmanned Systems)
Open AccessArticle
A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals
Sensors 2020, 20(15), 4323; https://doi.org/10.3390/s20154323 - 03 Aug 2020
Abstract
People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, [...] Read more.
People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Sensors)
Open AccessArticle
A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines
Sensors 2020, 20(15), 4322; https://doi.org/10.3390/s20154322 - 03 Aug 2020
Abstract
The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis (PCA) was applied to extract the [...] Read more.
The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis (PCA) was applied to extract the raw features of events and blasts through their waveform images that established by the recorded field data, and transform them into the new uncorrelated features. The amount of initial information retained in the derived features could be determined quantitatively by the contribution rate. The binary classification models were established by utilizing the support vector machine (SVM) algorithm and the PCA derived waveform image features. Results of four groups of cross validation show that the optimal values for the accuracy of events and blasts, total accuracy, and quality evaluation parameter MCC are 97.1%, 93.8%, 93.60%, and 0.8723, respectively. Moreover, the computation efficiency per accuracy (CEA) was introduced to quantitatively evaluate the effects of contribution rate on classification accuracy and computation efficiency. The optimal contribution rate was determined to be 0.90. The waveform image discriminant method can automatically classify events and blasts in underground mines, ensuring the efficient establishment of high-quality micro-seismic databases and providing adequate data for the subsequent seismicity analysis. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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Open AccessArticle
Validity Evaluation Method Based on Data Driving for On-Line Monitoring Data of Transformer under DC-Bias
Sensors 2020, 20(15), 4321; https://doi.org/10.3390/s20154321 - 03 Aug 2020
Abstract
The DC-bias monitoring device of a transformer is easily affected by external noise interference, equipment aging, and communication failure, which makes it difficult to guarantee the validity of monitoring data and causes great problems for future data analysis. For this reason, this paper [...] Read more.
The DC-bias monitoring device of a transformer is easily affected by external noise interference, equipment aging, and communication failure, which makes it difficult to guarantee the validity of monitoring data and causes great problems for future data analysis. For this reason, this paper proposes a validity evaluation method based on data driving for the on-line monitoring data of a transformer under DC-bias. First, the variation rule and threshold range of monitoring data for neutral point DC, vibration, and noise of the transformer under different working conditions are obtained through statistical analysis. Then, the data validity criterion of DC bias monitoring data is proposed to achieve a comprehensive evaluation of data validity based on data threshold, continuity, impact, and correlation. In addition, case studies are carried out on the real measured data of the DC bias magnetic monitoring system of a regional power grid by using this evaluation method. The results show that the proposed method can systematically and comprehensively evaluate the validity of the DC bias monitoring data and can judge whether the monitoring device fails to a certain extent. Full article
(This article belongs to the Special Issue Sensors and Data Analytics for the Smart Grid)
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Open AccessArticle
Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet
Sensors 2020, 20(15), 4320; https://doi.org/10.3390/s20154320 - 03 Aug 2020
Abstract
This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. [...] Read more.
This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value. Full article
(This article belongs to the Special Issue Signal Processing Techniques for Smart Sensor Communications)
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Open AccessLetter
Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging
Sensors 2020, 20(15), 4319; https://doi.org/10.3390/s20154319 - 03 Aug 2020
Abstract
Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel [...] Read more.
Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessLetter
Precise Method of Ambiguity Initialization for Short Baselines with L1-L5 or E5-E5a GPS/GALILEO Data
Sensors 2020, 20(15), 4318; https://doi.org/10.3390/s20154318 - 02 Aug 2020
Viewed by 210
Abstract
This paper presents a precise and fast method of ambiguity resolution (PREFMAR) for frequencies L1/E1 and L5/E5a of GPS/GALILEO data. The developed method is designed for precise and fast determination of ambiguities in GNSS phase observations. Ambiguities are chosen based on mathematical search [...] Read more.
This paper presents a precise and fast method of ambiguity resolution (PREFMAR) for frequencies L1/E1 and L5/E5a of GPS/GALILEO data. The developed method is designed for precise and fast determination of ambiguities in GNSS phase observations. Ambiguities are chosen based on mathematical search functions. The fact that no variance–covariance matrix (VC matrix) with a so-called float solution is needed proves the innovativeness of the developed method. Moreover, the developed method enables determination of the ambiguities for short baseline double-difference (DD) observations. The presented algorithms for the developed method enable unique and reliable calculation of the ambiguity if the actual errors of code measurements of DD observations are less than 0.38 m and the relative errors of phase observations are in the range of ±3 cm. The paper presents both mathematical derivations of the functions used in the PREFMAR and numerical calculations based on real double-difference GPS observations (L1-L5). The elaborated algorithms can be easily implemented into GNSS receivers or mobile phones. Therefore, they can be widely used in many geoscience applications, as well as in precise GPS/GALILEO navigation. Full article
(This article belongs to the Special Issue GNSS Sensors in Aerial Navigation)
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Open AccessArticle
Smartphone App with an Accelerometer Enhances Patients’ Physical Activity Following Elective Orthopedic Surgery: A Pilot Study
Sensors 2020, 20(15), 4317; https://doi.org/10.3390/s20154317 - 02 Aug 2020
Viewed by 191
Abstract
Low physical activity (PA) levels are common in hospitalized patients. Digital health tools could be valuable in preventing the negative effects of inactivity. We therefore developed Hospital Fit; which is a smartphone application with an accelerometer, designed for hospitalized patients. It enables objective [...] Read more.
Low physical activity (PA) levels are common in hospitalized patients. Digital health tools could be valuable in preventing the negative effects of inactivity. We therefore developed Hospital Fit; which is a smartphone application with an accelerometer, designed for hospitalized patients. It enables objective activity monitoring and provides patients with insights into their recovery progress and offers a tailored exercise program. The aim of this study was to investigate the potential of Hospital Fit to enhance PA levels and functional recovery following orthopedic surgery. PA was measured with an accelerometer postoperatively until discharge. The control group received standard physiotherapy, while the intervention group used Hospital Fit in addition to physiotherapy. The time spent active and functional recovery (modified Iowa Level of Assistance Scale) on postoperative day one (POD1) were measured. Ninety-seven patients undergoing total knee or hip arthroplasty were recruited. Hospital Fit use, corrected for age, resulted in patients standing and walking on POD1 for an average increase of 28.43 min (95% confidence interval (CI): 5.55–51.32). The odds of achieving functional recovery on POD1, corrected for the American Society of Anesthesiologists classification, were 3.08 times higher (95% CI: 1.14–8.31) with Hospital Fit use. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ PA levels and functional recovery during hospitalization. Full article
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Open AccessReview
Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications
Sensors 2020, 20(15), 4316; https://doi.org/10.3390/s20154316 - 02 Aug 2020
Viewed by 187
Abstract
The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of [...] Read more.
The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems. Full article
(This article belongs to the Special Issue Sensor-Based Systems for Kinematics and Kinetics)
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Open AccessArticle
Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification
Sensors 2020, 20(15), 4315; https://doi.org/10.3390/s20154315 - 02 Aug 2020
Viewed by 190
Abstract
Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own [...] Read more.
Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD’s use fall in the category of transport, which demonstrates our first statement. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
Study on the Evaluation Method of Sound Phase Cloud Maps Based on an Improved YOLOv4 Algorithm
Sensors 2020, 20(15), 4314; https://doi.org/10.3390/s20154314 - 02 Aug 2020
Viewed by 184
Abstract
Most sound imaging instruments are currently used as measurement tools which can provide quantitative data, however, a uniform method to directly and comprehensively evaluate the results of combining acoustic and optical images is not available. Therefore, in this study, we define a localization [...] Read more.
Most sound imaging instruments are currently used as measurement tools which can provide quantitative data, however, a uniform method to directly and comprehensively evaluate the results of combining acoustic and optical images is not available. Therefore, in this study, we define a localization error index for sound imaging instruments, and propose an acoustic phase cloud map evaluation method based on an improved YOLOv4 algorithm to directly and objectively evaluate the sound source localization results of a sound imaging instrument. The evaluation method begins with the image augmentation of acoustic phase cloud maps obtained from the different tests of a sound imaging instrument to produce the dataset required for training the convolutional network. Subsequently, we combine DenseNet with existing clustering algorithms to improve the YOLOv4 algorithm to train the neural network for easier feature extraction. The trained neural network is then used to localize the target sound source and its pseudo-color map in the acoustic phase cloud map to obtain a pixel-level localization error. Finally, a standard chessboard grid is used to obtain the proportional relationship between the size of the acoustic phase cloud map and the actual physical space distance; then, the true lateral and longitudinal positioning error of sound imaging instrument can be obtained. Experimental results show that the mean average precision of the improved YOLOv4 algorithm in acoustic phase cloud map detection is 96.3%, the F1-score is 95.2%, and detection speed is up to 34.6 fps. The improved algorithm can rapidly and accurately determine the positioning error of sound imaging instrument, which can be used to analyze and evaluate the positioning performance of sound imaging instrument. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Miniature Resistance Measurement Device for Structural Health Monitoring of Reinforced Concrete Infrastructure
Sensors 2020, 20(15), 4313; https://doi.org/10.3390/s20154313 - 02 Aug 2020
Viewed by 195
Abstract
A vast amount of civil infrastructure is constructed using reinforced concrete, which can be susceptible to corrosion, posing significant risks. Corrosion of reinforced concrete has various causes, with chloride ingress known to be a major contributor. Monitoring this chloride ingress would allow for [...] Read more.
A vast amount of civil infrastructure is constructed using reinforced concrete, which can be susceptible to corrosion, posing significant risks. Corrosion of reinforced concrete has various causes, with chloride ingress known to be a major contributor. Monitoring this chloride ingress would allow for preventative maintenance to be less intrusive at a lower cost. Currently, chloride sensing methods are bulky and expensive, leaving the majority of concrete infrastructures unmonitored. This paper presents the design and fabrication of a miniature, low-cost device that can be embedded into concrete at various locations and depths. The device measures localized concrete resistance, correlating to the chloride ingress in the concrete using equations listed in this paper, and calculated results from two experiments are presented. The device benefits from a four-probe architecture, injecting a fixed frequency AC waveform across its outer electrodes within the cement block. Voltage across the internal electrodes is measured with a microcontroller and converted to a resistance value, communicated serially to an external computer. A final test showcases the ability of the device for three-dimensional mass deployment. Full article
(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)
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Open AccessArticle
An Online Tea Fixation State Monitoring Algorithm Based on Image Energy Attention Mechanism and Supervised Clustering (IEAMSC)
Sensors 2020, 20(15), 4312; https://doi.org/10.3390/s20154312 - 02 Aug 2020
Viewed by 178
Abstract
This study aimed at the shortcomings of existing fixation algorithms that are image-based only, and an effective tea fixation state monitoring algorithm was proposed. An adaptive filtering algorithm was used to automatically filter the ineffective information. Using the energy extractor, the complete energy [...] Read more.
This study aimed at the shortcomings of existing fixation algorithms that are image-based only, and an effective tea fixation state monitoring algorithm was proposed. An adaptive filtering algorithm was used to automatically filter the ineffective information. Using the energy extractor, the complete energy information of each fixation image was extracted. The image energy attention mechanism was used to identify the prominent features, and based on these, the energy data was mapped to generate the data points as the training data. The cluster idea was adopted, and the training data feed the features trainer. The trend center data of the tea processing energy clustering was generated from different color channels. The corresponding decision function was designed which is based on the distance of the cluster center. The fixation degree of each monitoring image set was measured by the decision function. The Euclidean distance of the energy clustering center of the three channels with the same fixation time progressively approached. The triangle formed by these three points had a trend of gradually shrinking, which was first discovered by us. The detection results showed high accuracy compared with the common classification algorithms. It indicates that the algorithm proposed has positive guiding and reference significance. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
Characterization of the Noise Induced by Stimulated Brillouin Scattering in Distributed Sensing
Sensors 2020, 20(15), 4311; https://doi.org/10.3390/s20154311 - 02 Aug 2020
Viewed by 192
Abstract
The excess noise due to stimulated Brillouin scattering (SBS) in gain and loss-based Brillouin optical time-domain analyzers (BOTDA) has been investigated theoretically and experimentally for the first time to the best of our knowledge. This investigation provides a full insight to the SBS-induced [...] Read more.
The excess noise due to stimulated Brillouin scattering (SBS) in gain and loss-based Brillouin optical time-domain analyzers (BOTDA) has been investigated theoretically and experimentally for the first time to the best of our knowledge. This investigation provides a full insight to the SBS-induced noise distribution, which mainly comes from phase-to-intensity conversion noise and the beating noise between the probe wave and spontaneous Brillouin scattering. A complete theoretical model, which is in good agreement with the experimental results, is presented to describe the noise. The results show that a loss-based BOTDA setup gives a better noise performance than a gain-based one in both time and frequency domain. The SBS-induced noise has been characterized in dependence on the pump and probe power and the spatial resolution. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessLetter
Lensless Scheme for Measuring Laser Aberrations Based on Computer-Generated Holograms
Sensors 2020, 20(15), 4310; https://doi.org/10.3390/s20154310 - 02 Aug 2020
Viewed by 164
Abstract
All of the existing holographic wavefront sensors are either bulky or have low accuracy of measuring wavefront aberrations. In this paper, we present an improvement of the holographic method of measuring wavefront aberrations using computer-generated Fourier holograms. The novelty of this work lies [...] Read more.
All of the existing holographic wavefront sensors are either bulky or have low accuracy of measuring wavefront aberrations. In this paper, we present an improvement of the holographic method of measuring wavefront aberrations using computer-generated Fourier holograms. The novelty of this work lies in the proposed approach to the synthesis of Fourier holograms, which are implemented using phase-only SLM. The main advantages of this method are the increased diffraction efficiency compared to the previously known methods, and the more compact implementation scheme due to the elimination of the conventional Fourier-lens. The efficiency of the proposed method was confirmed by numerical simulation and optical experiments. Full article
(This article belongs to the Special Issue Sensors Based on Diffraction Structures)
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Open AccessArticle
A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
Sensors 2020, 20(15), 4309; https://doi.org/10.3390/s20154309 - 02 Aug 2020
Viewed by 168
Abstract
Nowadays, there is a growing trend in smart cities. Therefore, the Internet of Things (IoT) enabled Underwater and Wireless Sensor Networks (I-UWSN) are mostly used for monitoring and exploring the environment with the help of smart technology, such as smart cities. The acoustic [...] Read more.
Nowadays, there is a growing trend in smart cities. Therefore, the Internet of Things (IoT) enabled Underwater and Wireless Sensor Networks (I-UWSN) are mostly used for monitoring and exploring the environment with the help of smart technology, such as smart cities. The acoustic medium is used in underwater communication and radio frequency is mostly used for wireless sensor networks to make communication more reliable. Therefore, some challenging tasks still exist in I-UWSN, i.e., selection of multiple nodes’ reliable paths towards the sink nodes; and efficient topology of the network. In this research, the novel routing protocol, namely Time Based Reliable Link (TBRL), for dynamic topology is proposed to support smart city. TBRL works in three phases. In the first phase, it discovers the topology of each node in network area using a topology discovery algorithm. In the second phase, the reliability of each established link has been determined while using two nodes reliable model for a smart environment. This reliability model reduces the chances of horizontal and higher depth level communication between nodes and selects next reliable forwarders. In the third phase, all paths are examined and the most reliable path is selected to send data packets. TBRL is simulated with the help of a network simulator tool (NS-2 AquaSim). The TBRL is compared with other well known routing protocols, i.e., Depth Based Routing (DBR) and Reliable Energy-efficient Routing Protocol (R-ERP2R), to check the performance in terms of end to end delay, packet delivery ratio, and energy consumption of a network. Furthermore, the reliability of TBRL is compared with 2H-ACK and 3H-RM. The simulation results proved that TBRL performs approximately 15% better as compared to DBR and 10% better as compared to R-ERP2R in terms of aforementioned performance metrics. Full article
(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)
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Open AccessArticle
Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications
Sensors 2020, 20(15), 4308; https://doi.org/10.3390/s20154308 - 02 Aug 2020
Viewed by 178
Abstract
The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of [...] Read more.
The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies. Full article
(This article belongs to the Special Issue Intelligent Sensors in the Industry 4.0 and Smart Factory)
Open AccessLetter
High-Efficiency Inscription of Fiber Bragg Grating Array with High-Energy Nanosecond-Pulsed Laser Talbot Interferometer
Sensors 2020, 20(15), 4307; https://doi.org/10.3390/s20154307 - 01 Aug 2020
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Abstract
A high-energy nanosecond-pulsed ultraviolet (UV) laser Talbot interferometer for high-efficiency, mass production of fiber Bragg grating (FBG) array was experimentally demonstrated. High-quality FBG arrays were successfully inscribed in both H2-free and H2-loaded standard single-mode fibers (SMFs) with high inscription [...] Read more.
A high-energy nanosecond-pulsed ultraviolet (UV) laser Talbot interferometer for high-efficiency, mass production of fiber Bragg grating (FBG) array was experimentally demonstrated. High-quality FBG arrays were successfully inscribed in both H2-free and H2-loaded standard single-mode fibers (SMFs) with high inscription efficiency and excellent reproducibility. Compared with the femtosecond pulse that had a coherent length of several tens of micrometers, a longer coherent length (~10 mm) of the employed laser rendered a wider FBG wavelength versatility over 700 nm band (1200–1900 nm) without the need for optical path difference (OPD) compensation. Dense FBG array with center wavelength separation of ~0.4 nm was achieved and more than 1750 FBGs with separated center wavelength could be inscribed in a single H2-free or H2-loaded SMF in theory, which is promising for mass production of FBG arrays in industry. Moreover, precise focusing of laser beam was superfluous for the proposed system due to the high energy density of pulse. The proposed FBG inscription system was promising for industrialization production of dense FBG arrays. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
Weather Classification Using an Automotive LIDAR Sensor Based on Detections on Asphalt and Atmosphere
Sensors 2020, 20(15), 4306; https://doi.org/10.3390/s20154306 - 01 Aug 2020
Viewed by 227
Abstract
A semi-/autonomous driving car requires local weather information to identify if it is working inside its operational design domain and adapt itself accordingly. This information can be extracted from changes in the detections of a light detection and ranging (LIDAR) sensor. These changes [...] Read more.
A semi-/autonomous driving car requires local weather information to identify if it is working inside its operational design domain and adapt itself accordingly. This information can be extracted from changes in the detections of a light detection and ranging (LIDAR) sensor. These changes are caused by modifications in the volumetric scattering of the atmosphere or surface reflection of objects in the field of view of the LIDAR. In order to evaluate the use of an automotive LIDAR as a weather sensor, a LIDAR is placed outdoor in a fixed position for a period of 9 months covering all seasons. As target, an asphalt region from a parking lot is chosen. The collected sensor raw data is labeled depending on the occurring weather conditions as: clear, rain, fog and snow, and the presence of sunlight: with or without background radiation. The influence of different weather types and background radiations on the measurement results is analyzed and different parameters are chosen in order to maximize the classification accuracy. The classification is done per frame in order to provide fast update rates while still keeping an F1 score higher than 80%. Additionally, the field of view is divided into two regions: atmosphere and street, where the influences of different weather types are most notable. The resulting classifiers can be used separately or together increasing the versatility of the system. A possible way of extending the method for a moving platform and alternatives to virtually simulate the scene are also discussed. Full article
(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)
Open AccessArticle
Gaussia Luciferase as a Reporter for Quorum Sensing in Staphylococcus aureus
Sensors 2020, 20(15), 4305; https://doi.org/10.3390/s20154305 - 01 Aug 2020
Viewed by 218
Abstract
Gaussia luciferase (GLuc) is a secreted protein with significant potential for use as a reporter of gene expression in bacterial pathogenicity studies. To date there are relatively few examples of its use in bacteriology. In this study we show that GLuc can be [...] Read more.
Gaussia luciferase (GLuc) is a secreted protein with significant potential for use as a reporter of gene expression in bacterial pathogenicity studies. To date there are relatively few examples of its use in bacteriology. In this study we show that GLuc can be functionally expressed in the human pathogen Staphylococcus aureus and furthermore show that it can be used as a biosensor for the agr quorum sensing (QS) system which employs autoinducing peptides to control virulence. GLuc was linked to the P3 promoter of the S. aureusagr operon. Biosensor strains were validated by evaluation of chemical agent-mediated activation and inhibition of agr. Use of GLuc enabled quantitative assessment of agr activity. This demonstrates the utility of Gaussia luciferase for in vitro monitoring of agr activation and inhibition. Full article
(This article belongs to the Special Issue Advances in Optical, Fluorescent and Luminescent Biosensors)
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Open AccessArticle
Sensor Based on Molecularly Imprinted Polymer Membranes and Smartphone for Detection of Fusarium Contamination in Cereals
Sensors 2020, 20(15), 4304; https://doi.org/10.3390/s20154304 - 01 Aug 2020
Viewed by 235
Abstract
The combination of the generic mobile technology and inherent stability, versatility and cost-effectiveness of the synthetic receptors allows producing optical sensors for potentially any analyte of interest, and, therefore, to qualify as a platform technology for a fast routine analysis of a large [...] Read more.
The combination of the generic mobile technology and inherent stability, versatility and cost-effectiveness of the synthetic receptors allows producing optical sensors for potentially any analyte of interest, and, therefore, to qualify as a platform technology for a fast routine analysis of a large number of contaminated samples. To support this statement, we present here a novel miniature sensor based on a combination of molecularly imprinted polymer (MIP) membranes and a smartphone, which could be used for the point-of-care detection of an important food contaminant, oestrogen-like toxin zearalenone associated with Fusarium contamination of cereals. The detection is based on registration of natural fluorescence of zearalenone using a digital smartphone camera after it binds to the sensor recognition element. The recorded image is further processed using a mobile application. It shows here a first example of the zearalenone-specific MIP membranes synthesised in situ using “dummy template”-based approach with cyclododecyl 2, 4-dihydroxybenzoate as the template and 1-allylpiperazine as a functional monomer. The novel smartphone sensor system based on optimized MIP membranes provides zearalenone detection in cereal samples within the range of 1–10 µg mL−1 demonstrating a detection limit of 1 µg mL−1 in a direct sensing mode. In order to reach the level of sensitivity required for practical application, a competitive sensing mode is also developed. It is based on application of a highly-fluorescent structural analogue of zearalenone (2-[(pyrene-l-carbonyl) amino]ethyl 2,4-dihydroxybenzoate) which is capable to compete with the target mycotoxin for the binding to zearalenone-selective sites in the membrane’s structure. The competitive mode increases 100 times the sensor’s sensitivity and allows detecting zearalenone at 10 ng mL−1. The linear dynamic range in this case comprised 10–100 ng mL−1. The sensor system is tested and found effective for zearalenone detection in maize, wheat and rye flour samples both spiked and naturally contaminated. The developed MIP membrane-based smartphone sensor system is an example of a novel, inexpensive tool for food quality analysis, which is portable and can be used for the “field” measurements and easily translated into the practice. Full article
Open AccessReview
Health Care Monitoring and Treatment for Coronary Artery Diseases: Challenges and Issues
Sensors 2020, 20(15), 4303; https://doi.org/10.3390/s20154303 - 01 Aug 2020
Viewed by 165
Abstract
In-stent restenosis concerning the coronary artery refers to the blood clotting-caused re-narrowing of the blocked section of the artery, which is opened using a stent. The failure rate for stents is in the range of 10% to 15%, where they do not remain [...] Read more.
In-stent restenosis concerning the coronary artery refers to the blood clotting-caused re-narrowing of the blocked section of the artery, which is opened using a stent. The failure rate for stents is in the range of 10% to 15%, where they do not remain open, thereby leading to about 40% of the patients with stent implantations requiring repeat procedure within one year, despite increased risk factors and the administration of expensive medicines. Hence, today stent restenosis is a significant cause of deaths globally. Monitoring and treatment matter a lot when it comes to early diagnosis and treatment. A review of the present stent monitoring technology as well as the practical treatment for addressing stent restenosis was conducted. The problems and challenges associated with current stent monitoring technology were illustrated, along with its typical applications. Brief suggestions were given and the progress of stent implants was discussed. It was revealed that prime requisites are needed to achieve good quality implanted stent devices in terms of their size, reliability, etc. This review would positively prompt researchers to augment their efforts towards the expansion of healthcare systems. Lastly, the challenges and concerns associated with nurturing a healthcare system were deliberated with meaningful evaluations. Full article
(This article belongs to the Section Biomedical Sensors)
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Open AccessLetter
Time-Domain Investigation of Switchable Filter Wide-Band Antenna for Microwave Breast Imaging
Sensors 2020, 20(15), 4302; https://doi.org/10.3390/s20154302 - 01 Aug 2020
Viewed by 183
Abstract
This paper investigates the time-domain performance of a switchable filter impulse radio ultra-wideband (IR-UWB) antenna for microwave breast imaging applications. A miniaturized CPW-fed integrated filter antenna with switchable performance in the range of the Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local [...] Read more.
This paper investigates the time-domain performance of a switchable filter impulse radio ultra-wideband (IR-UWB) antenna for microwave breast imaging applications. A miniaturized CPW-fed integrated filter antenna with switchable performance in the range of the Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN) bands could operate well within a 3.0 to 11 GHz frequency range. The time-domain performance of the filter antenna was investigated in comparison to that of the designed reference wideband antenna. By comparing both antennas’ time-domain characteristics, it was seen that the switchable filter antenna had good time-domain resolution along with the frequency-domain operation. Additionally, the time-domain investigation revealed that the switchable filter wide-band antenna performed similarly to the reference wide band antenna. This antenna was also utilized for a tumor detection application, and it was seen that the switchable filter wide-band antenna could detect a miniaturized irregularly shaped tumor easily, which is quite promising. Such an antenna with a good time-domain resolution and tumor detection capability will be a good candidate and will find potential applications in microwave breast imaging. Full article
(This article belongs to the Special Issue Ultra Wideband (UWB) Systems in Biomedical Sensing)
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Open AccessArticle
A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks
Sensors 2020, 20(15), 4301; https://doi.org/10.3390/s20154301 (registering DOI) - 01 Aug 2020
Viewed by 169
Abstract
Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing [...] Read more.
Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the “suspicious nodules” in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the “coarse-to-fine” detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity. Full article
(This article belongs to the Special Issue Sensors for Biomedical Imaging)
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Open AccessArticle
A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals
Sensors 2020, 20(15), 4300; https://doi.org/10.3390/s20154300 - 01 Aug 2020
Viewed by 189
Abstract
Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of [...] Read more.
Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments. Full article
Open AccessLetter
Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
Sensors 2020, 20(15), 4299; https://doi.org/10.3390/s20154299 - 01 Aug 2020
Viewed by 175
Abstract
There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using [...] Read more.
There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of “fresh”, “likely spoiled”, and “spoiled” based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment. Full article
(This article belongs to the Section Biomedical Sensors)
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Open AccessArticle
Vision Measurement of Gear Pitting Under Different Scenes by Deep Mask R-CNN
Sensors 2020, 20(15), 4298; https://doi.org/10.3390/s20154298 - 01 Aug 2020
Viewed by 222
Abstract
To accurately and quantitatively detect the gear pitting of different levels on the actual site, this paper studies a new vision measurement approach based on a tunable vision detection platform and the mask region-based convolutional neural network (Mask R-CNN). The shooting angle can [...] Read more.
To accurately and quantitatively detect the gear pitting of different levels on the actual site, this paper studies a new vision measurement approach based on a tunable vision detection platform and the mask region-based convolutional neural network (Mask R-CNN). The shooting angle can be properly set according to the specification of the target gear. With the obtained sample set of 1500 gear pitting images, an optimized deep Mask R-CNN was designed for the quantitative measurement of gear pitting. The effective tooth surface and pitting was firstly and simultaneously recognized, then they were segmented to calculate the pitting area ratio. Considering three situations of multi-level pitting, multi-illumination, and multi-angle, several indexes were used to evaluate detection and segmentation results of deep Mask R-CNN. Experimental results show that the proposed method has higher measurement accuracy than the traditional method based on image processing, thus it has significant practical potential. Full article
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