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Sensors, Volume 21, Issue 15 (August-1 2021) – 306 articles

Cover Story (view full-size image): Prostate cancer (PCa) remains one of the most prominent forms of cancer for men. Prostate-specific antigen (PSA) has been a commonly recognized PCa-associated protein biomarker, but it lacks in specificity and sensitivity when needed to diagnose, monitor, and/or treat PCa patients successfully. One enhancement could include the simultaneous detection of multiple PCa-associated protein biomarkers alongside PSA. Recent research for multiplexed PCa protein biomarker detection using optical and electrochemical biosensor platforms shows great promise toward companion diagnostic devices, which can be portable and cost-effective with multiplexing capacities. Such multiplex point-of-care testing can potentially be used in near-patient settings, providing a more personalized approach to PCa diagnosis, surveillance, and treatment management. View this paper.
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Article
Development of a Smart Leg Splint by Using New Sensor Technologies and New Therapy Possibilities
Sensors 2021, 21(15), 5252; https://doi.org/10.3390/s21155252 - 03 Aug 2021
Viewed by 1036
Abstract
Nowadays, after suffering a fracture in an upper or lower limb, a plaster cast is placed on the affected limb. It is a very old and efficient technique for recovery from an injury that has not had significant changes since its origin. This [...] Read more.
Nowadays, after suffering a fracture in an upper or lower limb, a plaster cast is placed on the affected limb. It is a very old and efficient technique for recovery from an injury that has not had significant changes since its origin. This project aims to develop a new low-cost smart 3D printed splint concept by using new sensing techniques. Two rapidly evolving Advanced Manufacturing (AM) technologies will be used: 3D scanning and 3D printing, thus combining engineering, medicine and materials evolution. The splint will include new small and lightweight sensors to detect any problem during the treatment process. Previous studies have already incorporated this kind of sensor for medical purposes. However, in this study it is implemented with a new concept: the possibility of applying treatments during the immobilization process and obtaining information from the sensors to modify the treatment. Due to this, rehabilitation treatments like infrared, ultrasounds or electroshock may be applied during the treatment, and the sensors (as it is showed in the study) will be able to detect changes during the rehabilitation process. Data of the pressure, temperature, humidity and colour of the skin will be collected in real time and sent to a mobile device so that they can be consulted remotely by a specialist. Moreover, it would be possible to include these data into the Internet of Things movement. This way, all the collected data might be compared and studied in order to find the best treatment for each kind of injury. It will be necessary to use a biocompatible material, submersible and suitable for contact with skin. These materials make it necessary to control the conditions in which the splint is produced, to assure that the properties are maintained. This development, makes it possible to design a new methodology that will help to provide faster and easier treatment. Full article
(This article belongs to the Special Issue Impact of Sensors in Biomechanics, Health Disease and Rehabilitation)
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Article
An Innovative Smart Concrete Anchorage with Self-Stress Sensing Capacity of Prestressing Stress of PS Tendon
Sensors 2021, 21(15), 5251; https://doi.org/10.3390/s21155251 - 03 Aug 2021
Cited by 1 | Viewed by 837
Abstract
An innovative smart concrete anchorage (SCA) has been developed for monitoring the stress of prestressing (PS) tendons by utilizing smart ultra-high-performance concrete (UHPC). The smart UHPC contained 2 vol% steel fibers and fine steel slag aggregates instead of silica sands. The effects of [...] Read more.
An innovative smart concrete anchorage (SCA) has been developed for monitoring the stress of prestressing (PS) tendons by utilizing smart ultra-high-performance concrete (UHPC). The smart UHPC contained 2 vol% steel fibers and fine steel slag aggregates instead of silica sands. The effects of different electrode materials, arrangements, and connectors on the self-stress sensing capacity of the SCA are discussed. A prototype SCA demonstrated its feasibility and sufficient self-stress sensing capacity to be used in monitoring the prestressing loss of the PS tendon. As the tensile stress of the PS tendon increased from 0 to 1488 MPa, the fractional change in resistivity (FCR) of the prototype SCA, with horizontally paired copper wire electrodes and a plug-in type connector, decreased linearly from 0% to −1.53%, whereas the FCR increased linearly from −1.53% to −0.04% as the tensile stress of the PS tendon decreased from 1488 to 331 MPa. Full article
(This article belongs to the Section Intelligent Sensors)
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Article
Complex Environment Path Planning for Unmanned Aerial Vehicles
Sensors 2021, 21(15), 5250; https://doi.org/10.3390/s21155250 - 03 Aug 2021
Cited by 3 | Viewed by 825
Abstract
Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path [...] Read more.
Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments. Full article
(This article belongs to the Section Vehicular Sensing)
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Article
A Vibration Sensor-Based Method for Generating the Precise Rotor Orbit Shape with General Notch Filter Method for New Rotor Seal Design Testing and Diagnostics
Sensors 2021, 21(15), 5249; https://doi.org/10.3390/s21155249 - 03 Aug 2021
Cited by 2 | Viewed by 650
Abstract
Verification of the behaviour of new designs of rotor seals is a crucial phase necessary for their use in rotary machines. Therefore, experimental equipment for the verification of properties that have an effect on rotor dynamics is being developed in the test laboratories [...] Read more.
Verification of the behaviour of new designs of rotor seals is a crucial phase necessary for their use in rotary machines. Therefore, experimental equipment for the verification of properties that have an effect on rotor dynamics is being developed in the test laboratories of the manufacturers of these components all over the world. In order to be able to compare the analytically derived and experimentally identified values of the seal parameters, specific requirements for the rotor vibration pattern during experiments are usually set. The rotor vibration signal must contain the specified dominant components, while the others, usually caused by unbalance, must be attenuated. Technological advances have made it possible to use magnetic bearings in test equipment to support the rotor and as a rotor vibration exciter. Active magnetic bearings allow control of the vibrations of the rotor and generate the desired shape of the rotor orbit. This article presents a solution developed for a real test rig equipped with active magnetic bearings and rotor vibration sensors, which is to be used for testing a new design of rotor seals. Generating the exact shape of the orbit is challenging. The exact shape of the rotor orbit is necessary to compare the experimentally and numerically identified properties of the seal. The generalized notch filter method is used to compensate for the undesired harmonic vibrations. In addition, a novel modified generalized notch filter is introduced, which is used for harmonic vibration generation. The excitation of harmonic vibration of the rotor in an AMB system is generally done by injecting the harmonic current into the control loop of each AMB axis. The motion of the rotor in the AMB axis is coupled, therefore adjustment of the amplitudes and phases of the injected signals may be tedious. The novel general notch filter algorithm achieves the desired harmonic vibration of the rotor automatically. At first, the general notch filter algorithm is simulated and the functionality is confirmed. Finally, an experimental test device with an active magnetic bearing is used for verification of the algorithm. The measured data are presented to demonstrate that this approach can be used for precise rotor orbit shape generation by active magnetic bearings. Full article
(This article belongs to the Special Issue Vibration Sensor-Based Diagnosis Technologies and Systems: Part Ⅰ )
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Review
A Systematic Review of Recommender Systems and Their Applications in Cybersecurity
Sensors 2021, 21(15), 5248; https://doi.org/10.3390/s21155248 - 03 Aug 2021
Cited by 4 | Viewed by 1432
Abstract
This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of [...] Read more.
This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others. Full article
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Communication
A Case–Control Study of the Effects of Chronic Low Back Pain in Spatiotemporal Gait Parameters
Sensors 2021, 21(15), 5247; https://doi.org/10.3390/s21155247 - 03 Aug 2021
Cited by 1 | Viewed by 885
Abstract
Chronic low back pain and biomechanical walking imbalances are closely related. It is relevant to identify if there are alterations in spatiotemporal gait patterns in subjects with CLBP (cases) versus healthy subjects (controls) to plan training interventions of motor control gait patterns, and [...] Read more.
Chronic low back pain and biomechanical walking imbalances are closely related. It is relevant to identify if there are alterations in spatiotemporal gait patterns in subjects with CLBP (cases) versus healthy subjects (controls) to plan training interventions of motor control gait patterns, and thus allowing normal physical activity of the individual. This study is intended to identify if spatiotemporal alterations occur in the gait cycle in CLBP subjects (cases) compared with a control group (healthy patients) analyzed with an OptoGait LED sensors gait program. Method: A total of n = 147 participants: n = 75 cases (CLBP) and n = 72 healthy controls subjects were studied with OptoGait gait program. Results: Significant differences were found between the two groups and both feet in foot stride, for the differences of the total stride and contact, for gait cadence and total stride length of the gait cycle (p < 0.05). Conclusions: CLBP may alter some normal gait patterns measured by OptoGait; this finding presents imbalances in gait cycle as an underlying factor. The gait is part of daily life of any individual and it is an important physical activity in relation to the maintenance of an optimal state of health. In addition, future studies are deemed necessary. Full article
(This article belongs to the Collection Sensors for Gait, Posture, and Health Monitoring)
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Article
A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources
Sensors 2021, 21(15), 5246; https://doi.org/10.3390/s21155246 - 03 Aug 2021
Viewed by 925
Abstract
The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and [...] Read more.
The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue. Full article
(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)
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Article
Affordable Embroidered EMG Electrodes for Myoelectric Control of Prostheses: A Pilot Study
Sensors 2021, 21(15), 5245; https://doi.org/10.3390/s21155245 - 03 Aug 2021
Cited by 3 | Viewed by 1224
Abstract
Commercial myoelectric prostheses are costly to purchase and maintain, making their provision challenging for developing countries. Recent research indicates that embroidered EMG electrodes may provide a more affordable alternative to the sensors used in current prostheses. This pilot study investigates the usability of [...] Read more.
Commercial myoelectric prostheses are costly to purchase and maintain, making their provision challenging for developing countries. Recent research indicates that embroidered EMG electrodes may provide a more affordable alternative to the sensors used in current prostheses. This pilot study investigates the usability of such electrodes for myoelectric control by comparing online and offline performance against conventional gel electrodes. Offline performance is evaluated through the classification of nine different hand and wrist gestures. Online performance is assessed with a crossover two-degree-of-freedom real-time experiment using Fitts’ Law. Two performance metrics (Throughput and Completion Rate) are used to quantify usability. The mean classification accuracy of the nine gestures is approximately 98% for subject-specific models trained on both gel and embroidered electrode offline data from individual subjects, and 97% and 96% for general models trained on gel and embroidered offline data, respectively, from all subjects. Throughput (0.3 bits/s) and completion rate (95–97%) are similar in the online test. Results indicate that embroidered electrodes can achieve similar performance to gel electrodes paving the way for low-cost myoelectric prostheses. Full article
(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)
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Article
The Algorithm of a Game-Based System in the Relation between an Operator and a Technical Object in Management of E-Commerce Logistics Processes with the Use of Machine Learning
Sensors 2021, 21(15), 5244; https://doi.org/10.3390/s21155244 - 03 Aug 2021
Viewed by 792
Abstract
Machine learning (ML) is applied in various logistic processes utilizing innovative techniques (e.g., the use of drones for automated delivery in e-commerce). Early challenges showed the insufficient drones’ steering capacity and cognitive gap related to the lack of theoretical foundation for controlling algorithms. [...] Read more.
Machine learning (ML) is applied in various logistic processes utilizing innovative techniques (e.g., the use of drones for automated delivery in e-commerce). Early challenges showed the insufficient drones’ steering capacity and cognitive gap related to the lack of theoretical foundation for controlling algorithms. The aim of this paper is to present a game-based algorithm of controlling behaviours in the relation between an operator (OP) and a technical object (TO), based on the assumption that the game is logistics-oriented and the algorithm is to support ML applied in e-commerce optimization management. Algebraic methods, including matrices, Lagrange functions, systems of differential equations, and set-theoretic notation, have been used as the main tools. The outcome is a model of a game-based optimization process in a two-element logistics system and an algorithm applied to find optimal steering strategies. The algorithm has been initially verified with the use of simulation based on a Bayesian network (BN) and a structured set of possible strategies (OP/TO) calculated with the use of QGeNie Modeller, finally prepared for Python. It has been proved the algorithm at this stage has no deadlocks and unforeseen loops and is ready to be challenged with the original big set of learning data from a drone-operating company (as the next stage of the planned research). Full article
(This article belongs to the Section Internet of Things)
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Article
Discovering Vegetation Recovery and Landslide Activities in the Wenchuan Earthquake Area with Landsat Imagery
Sensors 2021, 21(15), 5243; https://doi.org/10.3390/s21155243 - 03 Aug 2021
Cited by 3 | Viewed by 765
Abstract
Post-seismic vegetation recovery is critical to local ecosystem recovery and slope stability, especially in the Wenchuan earthquake area where tens of thousands of landslides were triggered. This study executed a decadal monitoring of post-seismic landslide activities all over the region by investigating landslide [...] Read more.
Post-seismic vegetation recovery is critical to local ecosystem recovery and slope stability, especially in the Wenchuan earthquake area where tens of thousands of landslides were triggered. This study executed a decadal monitoring of post-seismic landslide activities all over the region by investigating landslide vegetation recovery rate (VRR) with Landsat images and a (nearly) complete landslide inventory. Thirty thousand landslides that were larger than nine pixels were chosen for VRR analysis, to reduce the influence of mixed pixels and support detailed investigation within landslides. The study indicates that about 60% of landslide vegetation gets close to the pre-earthquake level in ten years and is expected to recover to the pre-earthquake level within 20 years. The vegetation recovery is significantly influenced by topographic factors, especially elevation and slope, while it is barely related to the distance to epicenter, fault ruptures, and rivers. This study checked and improved the knowledge of vegetation recovery and landslide stability in the area, based on a detailed investigation. Full article
(This article belongs to the Section Remote Sensors)
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Review
Wearable Technologies in Field Hockey Competitions: A Scoping Review
Sensors 2021, 21(15), 5242; https://doi.org/10.3390/s21155242 - 03 Aug 2021
Cited by 1 | Viewed by 1505
Abstract
The aim of this review is to investigate the common wearable devices currently used in field hockey competitions, and to understand the hockey-specific parameters these devices measure. A systematic search was conducted by using three electronic databases and search terms that included field [...] Read more.
The aim of this review is to investigate the common wearable devices currently used in field hockey competitions, and to understand the hockey-specific parameters these devices measure. A systematic search was conducted by using three electronic databases and search terms that included field hockey, wearables, accelerometers, inertial sensors, global positioning system (GPS), heart rate monitors, load, performance analysis, player activity profiles, and competitions from the earliest record. The review included 39 studies that used wearable devices during competitions. GPS units were found to be the most common wearable in elite field hockey competitions, followed by heart rate monitors. Wearables in field hockey are mostly used to measure player activity profiles and physiological demands. Inconsistencies in sampling rates and performance bands make comparisons between studies challenging. Nonetheless, this review demonstrated that wearable devices are being used for various applications in field hockey. Researchers, engineers, coaches, and sport scientists can consider using GPS units of higher sampling rates, as well as including additional variables such as skin temperatures and injury associations, to provide a more thorough evaluation of players’ physical and physiological performances. Future work should include goalkeepers and non-elite players who are less studied in the current literature. Full article
(This article belongs to the Collection Sensor Technology for Sports Science)
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Article
Multibody-Based Input and State Observers Using Adaptive Extended Kalman Filter
Sensors 2021, 21(15), 5241; https://doi.org/10.3390/s21155241 - 03 Aug 2021
Cited by 2 | Viewed by 999
Abstract
The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to [...] Read more.
The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to obtain. This challenge can be overcome by the use of adaptive techniques. Based on an error-extended Kalman filter with force estimation (errorEKF-FE), the adaptive method known as maximum likelihood is adjusted to fulfill the multibody requirements. This new filter is called adaptive error-extended Kalman filter (AerrorEKF-FE). In order to present a general approach, the method is tested on two different mechanisms in a simulation environment. In addition, different sensor configurations are also studied. Results show that, in spite of the maneuver conditions and initial statistics, the AerrorEKF-FE provides estimations with accuracy and robustness. The AerrorEKF-FE proves that adaptive techniques can be applied to multibody-based state estimators, increasing, therefore, their fields of application. Full article
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Article
Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis
Sensors 2021, 21(15), 5240; https://doi.org/10.3390/s21155240 - 03 Aug 2021
Cited by 4 | Viewed by 1353
Abstract
Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for [...] Read more.
Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Falls Monitoring)
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Article
Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data
Sensors 2021, 21(15), 5239; https://doi.org/10.3390/s21155239 - 03 Aug 2021
Cited by 5 | Viewed by 5647
Abstract
The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods as well as manual paper based [...] Read more.
The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods as well as manual paper based methods. Furthermore, due to the impact of the advancement of mobile smart devices imbedding several sensors and deep learning algorithms, the necessity of a standardized, qualitative, and automatic scoring system for CDT has been increased. This study presents a mobile phone application, mCDT, for the CDT and suggests a novel, automatic and qualitative scoring method using mobile sensor data and deep learning algorithms: CNN, a convolutional network, U-Net, a convolutional network for biomedical image segmentation, and the MNIST (Modified National Institute of Standards and Technology) database. To obtain DeepC, a trained model for segmenting a contour image from a hand drawn clock image, U-Net was trained with 159 CDT hand-drawn images at 128 × 128 resolution, obtained via mCDT. To construct DeepH, a trained model for segmenting the hands in a clock image, U-Net was trained with the same 159 CDT 128 × 128 resolution images. For obtaining DeepN, a trained model for classifying the digit images from a hand drawn clock image, CNN was trained with the MNIST database. Using DeepC, DeepH and DeepN with the sensor data, parameters of contour (0–3 points), numbers (0–4 points), hands (0–5 points), and the center (0–1 points) were scored for a total of 13 points. From 219 subjects, performance testing was completed with images and sensor data obtained via mCDT. For an objective performance analysis, all the images were scored and crosschecked by two clinical experts in CDT scaling. Performance test analysis derived a sensitivity, specificity, accuracy and precision for the contour parameter of 89.33, 92.68, 89.95 and 98.15%, for the hands parameter of 80.21, 95.93, 89.04 and 93.90%, for the numbers parameter of 83.87, 95.31, 87.21 and 97.74%, and for the center parameter of 98.42, 86.21, 96.80 and 97.91%, respectively. From these results, the mCDT application and its scoring system provide utility in differentiating dementia disease subtypes, being valuable in clinical practice and for studies in the field. Full article
(This article belongs to the Special Issue Image Sensing and Processing with Convolutional Neural Networks)
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Article
Convolutional Neural Networks for Challenges in Automated Nuclide Identification
Sensors 2021, 21(15), 5238; https://doi.org/10.3390/s21155238 - 03 Aug 2021
Cited by 2 | Viewed by 924
Abstract
Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods [...] Read more.
Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed. The implementation of 1D multi-class, multi-label CNNs demonstrated good generalisation to real spectra with poor statistics and significant gain shifts. It is also shown that even basic CNN architectures prove reliable for RIID under the challenging conditions of heavy shielding and close source geometries, and may be extended to generalised solutions for pragmatic RIID. Full article
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Article
A Semi-Linear Elliptic Model for a Circular Membrane MEMS Device Considering the Effect of the Fringing Field
Sensors 2021, 21(15), 5237; https://doi.org/10.3390/s21155237 - 02 Aug 2021
Cited by 11 | Viewed by 1074
Abstract
In this study, an accurate analytic semi-linear elliptic differential model for a circular membrane MEMS device, which considers the effect of the fringing field on the membrane curvature recovering, is presented. A novel algebraic condition, related to the membrane electromechanical properties, able to [...] Read more.
In this study, an accurate analytic semi-linear elliptic differential model for a circular membrane MEMS device, which considers the effect of the fringing field on the membrane curvature recovering, is presented. A novel algebraic condition, related to the membrane electromechanical properties, able to govern the uniqueness of the solution, is also demonstrated. Numerical results for the membrane profile, obtained by using the Shooting techniques, the Keller–Box scheme, and the III/IV Stage Lobatto IIIa formulas, have been carried out, and their performances have been compared. The convergence conditions, and the possible presence of ghost solutions, have been evaluated and discussed. Finally, a practical criterion for choosing the membrane material as a function of the MEMS specific application is presented. Full article
(This article belongs to the Section Physical Sensors)
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Article
A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders
Sensors 2021, 21(15), 5236; https://doi.org/10.3390/s21155236 - 02 Aug 2021
Viewed by 1679
Abstract
The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the [...] Read more.
The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the necessary characteristics for the worker to be able to position himself/herself comfortably with the correct posture in front of their computer. Furthermore, from the point of view of the medical personnel in charge of occupational risk prevention, an automated tool able to quantify the degree of incorrectness of a postural habit in a worker is needed. For this purpose, in this work, a system based on the postural detection of the worker is designed, implemented and tested, using a specialized hardware system that processes video in real time through convolutional neural networks. This system is capable of detecting the posture of the neck, shoulders and arms, providing recommendations to the worker in order to prevent possible health problems, due to poor posture. The results of the proposed system show that this video processing can be carried out in real time (up to 25 processed frames/sec) with a low power consumption (less than 10 watts) using specialized hardware, obtaining an accuracy of over 80% in terms of the pattern detected. Full article
(This article belongs to the Special Issue Adaptive and Intelligent Sensors for Mobile Health)
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Article
Passive Measurement of Three Optical Beacon Coordinates Using a Simultaneous Method
Sensors 2021, 21(15), 5235; https://doi.org/10.3390/s21155235 - 02 Aug 2021
Viewed by 624
Abstract
Among other things, passive methods based on the processing of images of feature points or beacons captured by an image sensor are used to measure the relative position of objects. At least two cameras usually have to be used to obtain the required [...] Read more.
Among other things, passive methods based on the processing of images of feature points or beacons captured by an image sensor are used to measure the relative position of objects. At least two cameras usually have to be used to obtain the required information, or the cameras are combined with other sensors working on different physical principles. This paper describes the principle of passively measuring three position coordinates of an optical beacon using a simultaneous method and presents the results of corresponding experimental tests. The beacon is represented by an artificial geometric structure, consisting of several semiconductor light sources. The sources are suitably arranged to allow, all from one camera, passive measurement of the distance, two position angles, the azimuth, and the beacon elevation. The mathematical model of this method consists of working equations containing measured coordinates, geometric parameters of the beacon, and geometric parameters of the beacon image captured by the camera. All the results of these experimental tests are presented. Full article
(This article belongs to the Section Navigation and Positioning)
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Article
Forecasting of Typhoon-Induced Wind-Wave by Using Convolutional Deep Learning on Fused Data of Remote Sensing and Ground Measurements
Sensors 2021, 21(15), 5234; https://doi.org/10.3390/s21155234 - 02 Aug 2021
Cited by 4 | Viewed by 882
Abstract
Taiwan is an island, and its economic activities are primarily dependent on maritime transport and international trade. However, Taiwan is also located in the region of typhoon development in the Northwestern Pacific Basin. Thus, it frequently receives strong winds and large waves brought [...] Read more.
Taiwan is an island, and its economic activities are primarily dependent on maritime transport and international trade. However, Taiwan is also located in the region of typhoon development in the Northwestern Pacific Basin. Thus, it frequently receives strong winds and large waves brought by typhoons, which pose a considerable threat to port operations. To determine the real-time status of winds and waves brought by typhoons near the coasts of major ports in Taiwan, this study developed models for predicting the wind speed and wave height near the coasts of ports during typhoon periods. The forecasting horizons range from 1 to 6 h. In this study, the gated recurrent unit (GRU) neural networks and convolutional neural networks (CNNs) were combined and adopted to formulate the typhoon-induced wind and wave height prediction models. This work designed two wind speed prediction models (WIND-1 and WIND-2) and four wave height prediction models (WAVE-1 to WAVE-4), which are based on the WIND-1 and WIND-2 model outcomes. The Longdong and Liuqiu Buoys were the experiment locations. The observatory data from the ground stations and buoys, as well as radar reflectivity images, were adopted. The results indicated that, first, WIND-2 has a superior wind speed prediction performance to WIND-1, where WIND-2 can be used to identify the temporal and spatial changes in wind speeds using ground station data and reflectivity images. Second, WAVE-4 has the optimal wave height prediction performance, followed by WAVE-3, WAVE-2, and WAVE-1. The results of WAVE-4 revealed using the designed models with in-situ and reflectivity data directly yielded optimal predictions of the wind-based wave heights. Overall, the results indicated that the presented combination models were able to extract the spatial image features using multiple convolutional and pooling layers and provide useful information from time-series data using the GRU memory cell units. Overall, the presented models could exhibit promising results. Full article
(This article belongs to the Special Issue Image Sensing and Processing with Convolutional Neural Networks)
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Article
ESCOVE: Energy-SLA-Aware Edge–Cloud Computation Offloading in Vehicular Networks
Sensors 2021, 21(15), 5233; https://doi.org/10.3390/s21155233 - 02 Aug 2021
Cited by 4 | Viewed by 785
Abstract
The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the [...] Read more.
The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state-of-the-art approach. Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications)
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Article
Sea Fog Dissipation Prediction in Incheon Port and Haeundae Beach Using Machine Learning and Deep Learning
Sensors 2021, 21(15), 5232; https://doi.org/10.3390/s21155232 - 02 Aug 2021
Cited by 1 | Viewed by 910
Abstract
Sea fog is a natural phenomenon that reduces the visibility of manned vehicles and vessels that rely on the visual interpretation of traffic. Fog clearance, also known as fog dissipation, is a relatively under-researched area when compared with fog prediction. In this work, [...] Read more.
Sea fog is a natural phenomenon that reduces the visibility of manned vehicles and vessels that rely on the visual interpretation of traffic. Fog clearance, also known as fog dissipation, is a relatively under-researched area when compared with fog prediction. In this work, we first analyzed meteorological observations that relate to fog dissipation in Incheon port (one of the most important ports for the South Korean economy) and Haeundae beach (the most populated and famous resort beach near Busan port). Next, we modeled fog dissipation using two separate algorithms, classification and regression, and a model with nine machine learning and three deep learning techniques. In general, the applied methods demonstrated high prediction accuracy, with extra trees and recurrent neural nets performing best in the classification task and feed-forward neural nets in the regression task. Full article
(This article belongs to the Collection Multi-Sensor Information Fusion)
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Article
Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition
Sensors 2021, 21(15), 5231; https://doi.org/10.3390/s21155231 - 02 Aug 2021
Cited by 2 | Viewed by 928
Abstract
Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively [...] Read more.
Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4–0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement. Full article
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Article
Performance Analysis of Non-Interferometry Based Surface Plasmon Resonance Microscopes
Sensors 2021, 21(15), 5230; https://doi.org/10.3390/s21155230 - 02 Aug 2021
Cited by 2 | Viewed by 710
Abstract
Surface plasmon microscopy has been of interest to the science and engineering community and has been utilized in broad aspects of applications and studies, including biochemical sensing and biomolecular binding kinetics. The benefits of surface plasmon microscopy include label-free detection, high sensitivity, and [...] Read more.
Surface plasmon microscopy has been of interest to the science and engineering community and has been utilized in broad aspects of applications and studies, including biochemical sensing and biomolecular binding kinetics. The benefits of surface plasmon microscopy include label-free detection, high sensitivity, and quantitative measurements. Here, a theoretical framework to analyze and compare several non-interferometric surface plasmon microscopes is proposed. The scope of the study is to (1) identify the strengths and weaknesses in each surface plasmon microscopes reported in the literature; (2) quantify their performance in terms of spatial imaging resolution, imaging contrast, sensitivity, and measurement accuracy for quantitative and non-quantitative imaging modes of the microscopes. Six types of non-interferometric microscopes were included in this study: annulus aperture scanning, half annulus aperture scanning, single-point scanning, double-point scanning, single-point scanning, at 45 degrees azimuthal angle, and double-point scanning at 45 degrees azimuthal angle. For non-quantitative imaging, there is a substantial tradeoff between the image contrast and the spatial resolution. For the quantitative imaging, the half annulus aperture provided the highest sensitivity of 127.058 rad/μm2 RIU−1, followed by the full annulus aperture of 126.318 rad/μm2 RIU−1. There is a clear tradeoff between spatial resolution and sensitivity. The annulus aperture and half annulus aperture had an optimal resolution, sensitivity, and crosstalk compared to the other non-interferometric surface plasmon resonance microscopes. The resolution depends strongly on the propagation length of the surface plasmons rather than the numerical aperture of the objective lens. For imaging and sensing purposes, the recommended microfluidic channel size and protein stamping size for surface plasmon resonance experiments is at least 25 μm for accurate plasmonic measurements. Full article
(This article belongs to the Section Sensing and Imaging)
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Article
DAMAC: A Delay-Aware MAC Protocol for Ad Hoc Underwater Acoustic Sensor Networks
Sensors 2021, 21(15), 5229; https://doi.org/10.3390/s21155229 - 02 Aug 2021
Viewed by 644
Abstract
In a channel shared by several nodes, the scheduling algorithm is a key factor to avoiding collisions in the random access-based approach. Commonly, scheduling algorithms can be used to enhance network performance to meet certain requirements. Therefore, in this paper we propose a [...] Read more.
In a channel shared by several nodes, the scheduling algorithm is a key factor to avoiding collisions in the random access-based approach. Commonly, scheduling algorithms can be used to enhance network performance to meet certain requirements. Therefore, in this paper we propose a Delay-Aware Media Access Control (DAMAC) protocol for monitoring time-sensitive applications over multi-hop in Underwater Acoustic Sensor Networks (UASNs), which relies on the random access-based approach where each node uses Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) to determine channel status, switches nodes on and off to conserve energy, and allows concurrent transmissions to improve the underwater communication in the UASNs. In addition, DAMAC does not require any handshaking packets prior to data transmission, which helps to improve network performance in several metrics. The proposed protocol considers the long propagation delay to allow concurrent transmissions, meaning nodes are scheduled to transmit their data packets concurrently to exploit the long propagation delay between underwater nodes. The simulation results show that DAMAC protocol outperforms Aloha, BroadcastMAC, RMAC, Tu-MAC, and OPMAC protocols under varying network loads in terms of energy efficiency, communication overhead, and fairness of the network by up to 65%, 45%, and 726%, respectively. Full article
(This article belongs to the Section Sensor Networks)
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Article
Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System
Sensors 2021, 21(15), 5228; https://doi.org/10.3390/s21155228 - 02 Aug 2021
Cited by 2 | Viewed by 956
Abstract
In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data [...] Read more.
In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training. Full article
(This article belongs to the Section Radar Sensors)
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Article
Sequence Set Design for a New LFM-PC Hybrid Modulated Radar Signal
Sensors 2021, 21(15), 5227; https://doi.org/10.3390/s21155227 - 02 Aug 2021
Viewed by 591
Abstract
In this paper we study the code design problem of a new form of linear frequency modulation phase-coded (LFM-PC) hybrid signal with wide Doppler tolerance based on a range-Doppler discrete ambiguity function (DAF) to get better detection performance and anti-jamming capability. The DAF [...] Read more.
In this paper we study the code design problem of a new form of linear frequency modulation phase-coded (LFM-PC) hybrid signal with wide Doppler tolerance based on a range-Doppler discrete ambiguity function (DAF) to get better detection performance and anti-jamming capability. The DAF of the LFM-PC inter pulse signal is derived within the Doppler tolerance. Two optimization models are established. One is single pulse sequence design (SSD) for Doppler tolerance extension based on minimum integral normalized sidelobe level (INSL); the other is multi pulse sequence set design (MSSD) for signal orthogonality based on the minimizing sum of the normalized DAF sidelobe (NDAFSL) and discrete cross ambiguity function (DCAF). Two low-complexity signal optimization methods based on alternating direction method of multiplier (ADMM) are proposed, respectively. The simulation results show that the optimized signals have either wide Doppler tolerance or good orthogonal performance, and the optimization methods (i.e., SSD-ADMM and MSSD-ADMM) have the characteristics of fast convergence speed and low operation amount. Full article
(This article belongs to the Section Radar Sensors)
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Article
Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes
Sensors 2021, 21(15), 5226; https://doi.org/10.3390/s21155226 - 02 Aug 2021
Viewed by 959
Abstract
Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several [...] Read more.
Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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Article
Investigation and Optimization of a Line-Locked Quartz Enhanced Spectrophone for Rapid Carbon Dioxide Measurement
Sensors 2021, 21(15), 5225; https://doi.org/10.3390/s21155225 - 02 Aug 2021
Cited by 4 | Viewed by 751
Abstract
We have developed a rapid quartz enhanced spectrophone for carbon dioxide (CO2) measurement, in which the laser wavelength was tightly locked to a CO2 absorption line and a custom quartz tuning fork (QTF) operating at 12.5 kHz was employed. The [...] Read more.
We have developed a rapid quartz enhanced spectrophone for carbon dioxide (CO2) measurement, in which the laser wavelength was tightly locked to a CO2 absorption line and a custom quartz tuning fork (QTF) operating at 12.5 kHz was employed. The intrinsic QTF oscillation-limited response time, as well as the optimal feedback interval, was experimentally investigated. By tightly locking the laser to the R(16) transition of CO2, we obtained a stable laser operation with its center wavelength variation kept within 0.0002 cm−1, merely three times the laser linewidth. The reported CO2 sensor achieved a detection limit of 7 ppm, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 4.7 × 10−9 W·cm−1·Hz−1/2, at a response time of 0.5 s. The detection limit can be further improved to 0.45 ppm at an integration time of 270 s, illustrating a good system stability. This spectrophone enables the realization of compact and fast-response gas sensors for many scenarios, where CO2 concentration from sub-ppm to hundreds of thousands of ppm is expected. Full article
(This article belongs to the Special Issue State-of-the-Art Optical Sensors Technology in China 2021-2022)
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Article
Texture Recognition Based on Perception Data from a Bionic Tactile Sensor
Sensors 2021, 21(15), 5224; https://doi.org/10.3390/s21155224 - 02 Aug 2021
Cited by 3 | Viewed by 1008
Abstract
Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, [...] Read more.
Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, we adopt a bionic tactile sensor to collect vibration data while sliding against materials of interest. Under a fixed contact pressure and speed, a total of 1000 sets of vibration data from ten different materials were collected. With the tactile perception data, four types of texture recognition algorithms are proposed. Three machine learning algorithms, including support vector machine, random forest, and K-nearest neighbor, are established for texture recognition. The test accuracy of those three methods are 95%, 94%, 94%, respectively. In the detection process of machine learning algorithms, the asamoto and polyester are easy to be confused with each other. A convolutional neural network is established to further increase the test accuracy to 98.5%. The three machine learning models and convolutional neural network demonstrate high accuracy and excellent robustness. Full article
(This article belongs to the Special Issue Micro/Nano Energy and Flexible Sensors)
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Article
Ultra-Wideband Positioning Sensor with Application to an Autonomous Ultraviolet-C Disinfection Vehicle
Sensors 2021, 21(15), 5223; https://doi.org/10.3390/s21155223 - 01 Aug 2021
Cited by 2 | Viewed by 1101
Abstract
Due to the COVID-19 virus being highly transmittable, frequently cleaning and disinfecting facilities is common guidance in public places. However, the more often the environment is cleaned, the higher the risk of cleaning staff getting infected. Therefore, strong demand for sanitizing areas in [...] Read more.
Due to the COVID-19 virus being highly transmittable, frequently cleaning and disinfecting facilities is common guidance in public places. However, the more often the environment is cleaned, the higher the risk of cleaning staff getting infected. Therefore, strong demand for sanitizing areas in automatic modes is undoubtedly expected. In this paper, an autonomous disinfection vehicle with an Ultraviolet-C (UVC) lamp is designed and implemented using an ultra-wideband (UWB) positioning sensor. The UVC dose for 90% inactivation of the reproductive ability of COVID-19 is 41.7 J/m2, which a 40 W UVC lamp can achieve within a 1.6 m distance for an exposure time of 30 s. With this UVC lamp, the disinfection vehicle can effectively sterilize in various scenarios. In addition, the high-accuracy UWB positioning system, with the time difference of arrival (TDOA) algorithm, is also studied for autonomous vehicle navigation in indoor environments. The number of UWB tags that use a synchronization protocol between UWB anchors can be unlimited. Moreover, this proposed Gradient Descent (GD), which uses Taylor method, is a high-efficient algorithm for finding the optimal position for real-time computation due to its low error and short calculating time. The generalized traversal path planning procedure, with the edge searching method, is presented to improve the efficiency of autonomous navigation. The average error of the practical navigation demonstrated in the meeting room is 0.10 m. The scalability of the designed system to different application scenarios is also discussed and experimentally demonstrated. Hence, the usefulness of the proposed UWB sensor applied to UVC disinfection vehicles to prevent COVID-19 infection is verified by employing it to sterilize indoor environments without human operation. Full article
(This article belongs to the Section Navigation and Positioning)
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