20 pages, 948 KiB  
Article
Adaptive Fault-Tolerant Formation Control of Heterogeneous Multi-Agent Systems under Directed Communication Topology
by Shangkun Liu 1,2, Bin Jiang 1,2,*, Zehui Mao 1,2 and Yajie Ma 1,2
1 College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2 Jiangsu Key Laboratory of Internet of Things and Control Technologies, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Sensors 2022, 22(16), 6212; https://doi.org/10.3390/s22166212 - 18 Aug 2022
Cited by 12 | Viewed by 2623
Abstract
This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and [...] Read more.
This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the XY plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme. Full article
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22 pages, 996 KiB  
Article
Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection
by Siyuan Yang 1, Mondher Bouazizi 2, Yuwen Cao 2 and Tomoaki Ohtsuki 2,*
1 Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan
2 Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
Sensors 2022, 22(16), 6211; https://doi.org/10.3390/s22166211 - 18 Aug 2022
Viewed by 1954
Abstract
In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight [...] Read more.
In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information. Full article
(This article belongs to the Special Issue Future Trends in Millimeter Wave Communication)
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15 pages, 4986 KiB  
Article
An Efficient Ensemble Deep Learning Approach for Semantic Point Cloud Segmentation Based on 3D Geometric Features and Range Images
by Muhammed Enes Atik * and Zaide Duran
Department of Geomatics Engineering, Istanbul Technical University (ITU), Istanbul 34469, Turkey
Sensors 2022, 22(16), 6210; https://doi.org/10.3390/s22166210 - 18 Aug 2022
Cited by 10 | Viewed by 3618
Abstract
Mobile light detection and ranging (LiDAR) sensor point clouds are used in many fields such as road network management, architecture and urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. Semantic segmentation of mobile point clouds is critical for these [...] Read more.
Mobile light detection and ranging (LiDAR) sensor point clouds are used in many fields such as road network management, architecture and urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. Semantic segmentation of mobile point clouds is critical for these tasks. In this study, we present a robust and effective deep learning-based point cloud semantic segmentation method. Semantic segmentation is applied to range images produced from point cloud with spherical projection. Irregular 3D mobile point clouds are transformed into regular form by projecting the clouds onto the plane to generate 2D representation of the point cloud. This representation is fed to the proposed network that produces semantic segmentation. The local geometric feature vector is calculated for each point. Optimum parameter experiments were also performed to obtain the best results for semantic segmentation. The proposed technique, called SegUNet3D, is an ensemble approach based on the combination of U-Net and SegNet algorithms. SegUNet3D algorithm has been compared with five different segmentation algorithms on two challenging datasets. SemanticPOSS dataset includes the urban area, whereas RELLIS-3D includes the off-road environment. As a result of the study, it was demonstrated that the proposed approach is superior to other methods in terms of mean Intersection over Union (mIoU) in both datasets. The proposed method was able to improve the mIoU metric by up to 15.9% in the SemanticPOSS dataset and up to 5.4% in the RELLIS-3D dataset. Full article
(This article belongs to the Special Issue Sensing and Semantic Perception in Autonomous Driving)
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16 pages, 868 KiB  
Article
Preparing Wi-Fi 7 for Healthcare Internet-of-Things
by Yazdan Ahmad Qadri 1, Zulqarnain 1, Ali Nauman 1, Arslan Musaddiq 2, Eduard Garcia-Villegas 3 and Sung Won Kim 1,*
1 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Korea
2 Department of Computer Science and Media Technology, Linnaeus University, 391 82 Kalmar, Sweden
3 Department of Network Engineering, Universitat Polit’ecnica de Catalunya (UPC), 08034 Barcelona, Spain
Sensors 2022, 22(16), 6209; https://doi.org/10.3390/s22166209 - 18 Aug 2022
Cited by 15 | Viewed by 3664
Abstract
The healthcare Internet of Things (H-IoT) is an interconnection of devices capable of sensing and transmitting information that conveys the status of an individual’s health. The continuous monitoring of an individual’s health for disease diagnosis and early detection is an important application of [...] Read more.
The healthcare Internet of Things (H-IoT) is an interconnection of devices capable of sensing and transmitting information that conveys the status of an individual’s health. The continuous monitoring of an individual’s health for disease diagnosis and early detection is an important application of H-IoT. Ambient assisted living (AAL) entails monitoring a patient’s health to ensure their well-being. However, ensuring a limit on transmission delays is an essential requirement of such monitoring systems. The uplink (UL) transmission during the orthogonal frequency division multiple access (OFDMA) in the wireless local area networks (WLANs) can incur a delay which may not be acceptable for delay-sensitive applications such as H-IoT due to their random nature. Therefore, we propose a UL OFDMA scheduler for the next Wireless Fidelity (Wi-Fi) standard, the IEEE 802.11be, that is compliant with the latency requirements for healthcare applications. The scheduler allocates the channel resources for UL transmission taking into consideration the traffic class or access category. The results demonstrate that the proposed scheduler can achieve the required latency for H-IoT applications. Additionally, the performance in terms of fairness and throughput is also superior to state-of-the-art schedulers. Full article
(This article belongs to the Special Issue Recent Advances in Mobile and Wireless Communication Networks)
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25 pages, 15281 KiB  
Article
GR-ConvNet v2: A Real-Time Multi-Grasp Detection Network for Robotic Grasping
by Sulabh Kumra 1,2,*, Shirin Joshi 1,3 and Ferat Sahin 1
1 The Department of Electrical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
2 eBots Inc., Fremont, CA 94539, USA
3 Siemens Corporation, Berkeley, CA 94704, USA
Sensors 2022, 22(16), 6208; https://doi.org/10.3390/s22166208 - 18 Aug 2022
Cited by 36 | Viewed by 7115
Abstract
We propose a dual-module robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from the n-channel image of the scene. We present an improved version of the Generative Residual Convolutional Neural Network (GR-ConvNet v2) model that [...] Read more.
We propose a dual-module robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from the n-channel image of the scene. We present an improved version of the Generative Residual Convolutional Neural Network (GR-ConvNet v2) model that can generate robust antipodal grasps from n-channel image input at real-time speeds (20 ms). We evaluated the proposed model architecture on three standard datasets and achieved a new state-of-the-art accuracy of 98.8%, 95.1%, and 97.4% on Cornell, Jacquard and Graspnet grasping datasets, respectively. Empirical results show that our model significantly outperformed the prior work with a stricter IoU-based grasp detection metric. We conducted a suite of tests in simulation and the real world on a diverse set of previously unseen objects with adversarial geometry and household items. We demonstrate the adaptability of our approach by directly transferring the trained model to a 7 DoF robotic manipulator with a grasp success rate of 95.4% and 93.0% on novel household and adversarial objects, respectively. Furthermore, we validate the generalization capability of our pixel-wise grasp prediction model by validating it on complex Ravens-10 benchmark tasks, some of which require closed-loop visual feedback for multi-step sequencing. Full article
(This article belongs to the Special Issue AI Based Autonomous Robots)
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19 pages, 8244 KiB  
Article
Comprehensive Engineering Frequency Domain Analysis and Vibration Suppression of Flexible Aircraft Based on Active Disturbance Rejection Controller
by Litao Liu 1,2 and Bingwei Tian 1,*
1 Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu 610207, China
2 Pittsburgh Institute, Sichuan University, Chengdu 610207, China
Sensors 2022, 22(16), 6207; https://doi.org/10.3390/s22166207 - 18 Aug 2022
Cited by 7 | Viewed by 2902
Abstract
The crash of an aircraft with an almost vertical attitude in Wuzhou, Guangxi, China, on 21 March 2022, has caused a robust discussion in the civil aviation community. We propose an active disturbance rejection controller (ADRC) for suppressing aeroelastic vibrations of a flexible [...] Read more.
The crash of an aircraft with an almost vertical attitude in Wuzhou, Guangxi, China, on 21 March 2022, has caused a robust discussion in the civil aviation community. We propose an active disturbance rejection controller (ADRC) for suppressing aeroelastic vibrations of a flexible aircraft at the simulation level. The ADRC has a relatively simple structure and it has been proved in several fields to provide better control than the classical proportional-integral-derivative (PID) control theory and is easier to translate from theory to practice compared with other modern control theories. In this paper, the vibration model of the flexible aircraft was built, based on the first elastic vibration mode of the aircraft. In addition, the principle of ADRC is explained in detail, a second-order ADRC was designed to control the vibration model, and the system’s closed-loop frequency domain characteristics, tracking effect and sensitivity were comprehensively analyzed. The estimation error of the extended state observer (ESO) and the anti-disturbance effect were analyzed, while the robustness of the closed-loop system was verified using the Monte Carlo method, which was used for the first time in this field. Simulation results showed that the ADRC suppressed aircraft elastic vibration better than PID controllers and that the closed-loop system was robust in the face of dynamic parameters. Full article
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22 pages, 1890 KiB  
Article
Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception
by Effat Jalaeian Zaferani 1, Mohammad Teshnehlab 1, Amirreza Khodadadian 2,*, Clemens Heitzinger 3,4, Mansour Vali 1, Nima Noii 5 and Thomas Wick 2
1 Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
2 Institute of Applied Mathematics, Leibniz University of Hannover, 30167 Hannover, Germany
3 Institute of Analysis and Scientific Computing, TU Wien, 1040 Vienna, Austria
4 Center for Artificial Intelligence and Machine Learning (CAIML), TU Wien, 1040 Vienna, Austria
5 Institute of Continuum Mechanics, Leibniz University of Hannover, 30823 Garbsen, Germany
Sensors 2022, 22(16), 6206; https://doi.org/10.3390/s22166206 - 18 Aug 2022
Cited by 10 | Viewed by 2218
Abstract
In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires [...] Read more.
In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning for Intelligent Sensing Systems)
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17 pages, 2924 KiB  
Article
Facile Conversion of the Quinone-Semicarbazone Chromophore of Naftazone into a Fluorescent Quinol-Semicarbazide: Kinetic Study and Analysis of Naftazone in Pharmaceuticals and Human Serum
by Mohammed Gamal 1,2, Hazim M. Ali 3, Rania El-Shaheny 4, Ibrahim A. Naguib 5, Izzeddin Alsalahat 6,* and Mahmoud El-Maghrabey 4
1 Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, P.O. Box 2014, Sakaka 72388, Aljouf, Saudi Arabia
2 Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Beni-Suef University, Alshaheed Shehata Ahmed Hegazy St., Beni-Suef 62574, Egypt
3 Department of Chemistry, College of Science, Jouf University, P.O. Box 2014, Sakaka 72388, Aljouf, Saudi Arabia
4 Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
5 Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Mecca, Saudi Arabia
6 UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Cardiff CF24 1TP, UK
Sensors 2022, 22(16), 6205; https://doi.org/10.3390/s22166205 - 18 Aug 2022
Cited by 4 | Viewed by 2239
Abstract
Naftazone is a quinone-semi carbazone drug that possesses a strong orange color, and hence it was usually analyzed colorimetrically or by HPLC-UV. However, these methods are not sensitive enough to determine naftazone in biological samples. Naftazone lacks intrinsic fluorescence and does not possess [...] Read more.
Naftazone is a quinone-semi carbazone drug that possesses a strong orange color, and hence it was usually analyzed colorimetrically or by HPLC-UV. However, these methods are not sensitive enough to determine naftazone in biological samples. Naftazone lacks intrinsic fluorescence and does not possess easily derivatizable functional groups. In this contribution, we introduced the first spectrofluorimetric method for naftazone assay through reduction-elicited fluorogenic derivatization through the reduction of its quinone-semicarbazone moiety to the corresponding quinol-semicarbazide derivative by potassium borohydride as a reduction probe. The solvent-dependent fluorescence of the reaction product was studied in various protic and aprotic solvents. Eventually, the fluorescence of the reduced naftazone was measured in 2-propanol at λemission of 350 nm after excitation at λecxitation of 295 nm. The relative fluorescence intensity was linearly correlated to the drug concentration (r = 0.9995) from 10.0 to 500 ng/mL with high sensitivity, where the lower detection limit was 2.9 ng/mL. Hence, the method was effectively applied for naftazone tablets quality control with a mean %recovery of 100.3 ± 1.5, and the results agreed with those of the comparison HPLC-UV method. Furthermore, a new salting-out assisted liquid-liquid extraction (SALLE) method was established for naftazone extraction from human serum, followed by its determination using the developed reduction-based fluorogenic method. The developed SALLE method showed excellent recovery for naftazone from human serum (92.3–106.5%) with good precision (RSD ≤ 6.8%). Additionally, the reaction of naftazone with potassium borohydride was kinetically monitored, and it was found to follow pseudo-first-order kinetics with an activation energy of 43.8 kcal/mol. The developed method’s greenness was approved using three green analytical chemistry metrics. Full article
(This article belongs to the Section Chemical Sensors)
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15 pages, 6560 KiB  
Article
Potentiometric Electronic Tongue for Quantitative Ion Analysis in Natural Mineral Waters
by María Cuartero 1,*, Alberto Ruiz 2, Manuel Galián 3 and Joaquín A. Ortuño 3,*
1 Department of Chemistry, School of Engineering Science in Chemistry, Biochemistry and Health, KTH Royal Institute of Technology, Teknikringen 30, SE-100 44 Stockholm, Sweden
2 Department of Informatics and Systems, University of Murcia, 30100 Murcia, Spain
3 Department of Analytical Chemistry, University of Murcia, 30100 Murcia, Spain
Sensors 2022, 22(16), 6204; https://doi.org/10.3390/s22166204 - 18 Aug 2022
Cited by 9 | Viewed by 2423
Abstract
The present paper addresses the development and use of a new potentiometric electronic tongue for both qualitative and quantitative characterization of natural mineral waters. The electronic tongue is particularly related to the conductivity and ion content of/in the water sample. The analytical system [...] Read more.
The present paper addresses the development and use of a new potentiometric electronic tongue for both qualitative and quantitative characterization of natural mineral waters. The electronic tongue is particularly related to the conductivity and ion content of/in the water sample. The analytical system is based on six ion-selective electrodes whose membranes are formulated to provide either cationic or anionic response and considering plasticizers with different dielectric constants (bis(2-ethylhexyl) sebacate, 2-nitrophenyl octyl ether or tricresylphosphate), while keeping the polymeric matrix, i.e., poly(vinyl chloride). Notably, the absence of any ionophore in the membrane provides a general response profile, i.e., no selectivity toward any special ion, which is convenient for the realization of an effective electronic tongue. The dynamic response of the tongue toward water samples of different chemical compositions and geographical locations has been obtained. At the optimized experimental conditions, the tongue presents acceptable repeatability and reproducibility (absence of hysteresis). The principal component analysis of the final potential values observed with the six electrodes allows for the differentiation and classification of the samples according to their conductivity, which is somehow related to the mineralization. Moreover, quantitative determination of the six main ions in the water samples (i.e., chloride, nitrate, hydrogen carbonate, sulfate, sodium, calcium, and magnesium) is possible by means of a simple linear calibration (and cross-validation) model. Full article
(This article belongs to the Special Issue Ion-Selective Sensors and Their Applications)
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14 pages, 32738 KiB  
Article
Design of a Reconfigurable Crop Scouting Vehicle for Row Crop Navigation: A Proof-of-Concept Study
by Austin Schmitz 1, Chetan Badgujar 1,*, Hasib Mansur 1, Daniel Flippo 1, Brian McCornack 2 and Ajay Sharda 1
1 Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66502, USA
2 Department of Entomology, Kansas State University, Manhattan, KS 66502, USA
Sensors 2022, 22(16), 6203; https://doi.org/10.3390/s22166203 - 18 Aug 2022
Cited by 12 | Viewed by 3039
Abstract
Pest infestation causes significant crop damage during crop production, which reduces the crop yield in terms of quality and quantity. Accurate, precise, and timely information on pest infestation is a crucial aspect of integrated pest management practices. The current manual scouting methods are [...] Read more.
Pest infestation causes significant crop damage during crop production, which reduces the crop yield in terms of quality and quantity. Accurate, precise, and timely information on pest infestation is a crucial aspect of integrated pest management practices. The current manual scouting methods are time-consuming and laborious, particularly for large fields. Therefore, a fleet of scouting vehicles is proposed to monitor and collect crop information at the sub-canopy level. These vehicles would traverse large fields and collect real-time information on pest type, concentration, and infestation level. In addition to this, the developed vehicle platform would assist in collecting information on soil moisture, nutrient deficiency, and disease severity during crop growth stages. This study established a proof-of-concept of a crop scouting vehicle that can navigate through the row crops. A reconfigurable ground vehicle (RGV) was designed and fabricated. The developed prototype was tested in the laboratory and an actual field environment. Moreover, the concept of corn row detection was established by utilizing an array of low-cost ultrasonic sensors. The RGV was successful in navigating through the corn field. The RGV’s reconfigurable characteristic provides the ability to move anywhere in the field without damaging the crops. This research shows the promise of using reconfigurable robots for row crop navigation for crop scouting and monitoring which could be modular and scalable, and can be mass-produced in quick time. A fleet of these RGVs would empower the farmers to make meaningful and timely decisions for their cropping system. Full article
(This article belongs to the Special Issue Ground and Aerial Robots in Smart Agriculture)
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10 pages, 480 KiB  
Article
Glucometer Usability for 65+ Type 2 Diabetes Patients: Insights on Physical and Cognitive Issues
by Maria Pinelli 1,*, Emanuele Lettieri 1, Andrea Boaretto 2, Carlo Casile 3, Giuseppe Citro 4, Bernardino Zazzaro 5 and Adriana Ravazzoni 5
1 Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini 4/B, 20156 Milan, Italy
2 Personalive S.r.l., Via Durando 38, 20158 Milan, Italy
3 Azienda Ospedaliera Papardo, Contrada Papardo, 98158 Messina, Italy
4 ASP Basilicata, Via Torraca 2, 85100 Potenza, Italy
5 Presidio Ospedaliero Umberto I° UOS Endocrinologia, Via Testaferrata 1, 96100 Siracusa, Italy
Sensors 2022, 22(16), 6202; https://doi.org/10.3390/s22166202 - 18 Aug 2022
Cited by 1 | Viewed by 3319
Abstract
Background: Self-monitoring of blood glucose (SMBG) is of paramount relevance for type 2 diabetes mellitus (T2DM) patients. However, past evidence shows that there are physical and cognitive issues that might limit the usage of glucometers by T2DM patients aged 65 years and [...] Read more.
Background: Self-monitoring of blood glucose (SMBG) is of paramount relevance for type 2 diabetes mellitus (T2DM) patients. However, past evidence shows that there are physical and cognitive issues that might limit the usage of glucometers by T2DM patients aged 65 years and over. Objective: Our aim was to investigate the physical and cognitive issues related to the usage of glucometers by T2DM patients aged 65 years and over. Materials and Methods: The extant literature was analysed to define an original framework showing the logical nexus between physical and cognitive issues and quality of life. Then we collected evidence addressing the specific case of the Accu-Chek® Instant glucometer produced by Roche Diabetes Care GmbH, which implements new features claiming to improve usability. We conducted 30 interviews with T2DM patients aged 65 years and over, three interviews with senior nurses, and a focus group with three senior physicians and three senior nurses. Results: From the interviews, both patients and nurses declared that they were generally satisfied with the Accu-Chek® Instant glucometer’s characteristics. In the focus group, the results were commented on and, in the light of some diverging answers, improvements have been set up for future implementation. Conclusions: Our study produces evidence and future suggestions about the usage of glucometers by type 2 diabetes patients aged 65 years and over. Full article
(This article belongs to the Section Biomedical Sensors)
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21 pages, 7925 KiB  
Article
Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection
by Oleg O. Kartashov *, Andrey V. Chernov, Alexander A. Alexandrov, Dmitry S. Polyanichenko, Vladislav S. Ierusalimov, Semyon A. Petrov and Maria A. Butakova
The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia
Sensors 2022, 22(16), 6201; https://doi.org/10.3390/s22166201 - 18 Aug 2022
Cited by 14 | Viewed by 4503
Abstract
During the steel pipeline installation, special attention is paid to the butt weld control performed by fusion welding. The operation of the currently popular automated X-ray and ultrasonic testing complexes is associated with high resource and monetary costs. In this regard, this work [...] Read more.
During the steel pipeline installation, special attention is paid to the butt weld control performed by fusion welding. The operation of the currently popular automated X-ray and ultrasonic testing complexes is associated with high resource and monetary costs. In this regard, this work is devoted to the development of alternative and cost-effective means of preliminary quality control of the work performed based on the visual testing method. To achieve this goal, a hardware platform based on a single board Raspberry Pi4 minicomputer and a set of available modules and expansion cards is proposed, and software whose main functionality is implemented based on the systemic application of computer vision algorithms and machine learning methods. The YOLOv5 object detection algorithm and the random forest machine learning model were used as a defect detection and classification system. The mean average precision (mAP) of the trained YOLOv5 algorithm based on extracted weld contours is 86.9%. A copy of YOLOv5 trained on the images of control objects showed a mAP result of 96.8%. Random forest identifying of the defect precursor based on the point clouds of the weld surface achieved a mAP of 87.5%. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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19 pages, 5370 KiB  
Article
SWIPT-Pairing Mechanism for Channel-Aware Cooperative H-NOMA in 6G Terahertz Communications
by Haider W. Oleiwi * and Hamed Al-Raweshidy
Department of Electronic and Electrical Engineering, Brunel University, London UB8 3PH, UK
Sensors 2022, 22(16), 6200; https://doi.org/10.3390/s22166200 - 18 Aug 2022
Cited by 23 | Viewed by 2464
Abstract
The constraints of 5G communication systems compel further improvements to be compatible with 6G candidate technologies, especially to cope with the limited wavelengths of blockage-sensitive terahertz (THz) frequencies. In this paper integrating cooperative simultaneous wireless information and power transfer (SWIPT) and hybrid-non-orthogonal multiple [...] Read more.
The constraints of 5G communication systems compel further improvements to be compatible with 6G candidate technologies, especially to cope with the limited wavelengths of blockage-sensitive terahertz (THz) frequencies. In this paper integrating cooperative simultaneous wireless information and power transfer (SWIPT) and hybrid-non-orthogonal multiple access (H-NOMA) using THz frequency bands are suggested. We investigated and developed an optimal SWIPT-pairing mechanism for the multilateral proposed system that represents a considerable enhancement in energy/spectral efficiencies while improving the significant system specifications. Given the system performance investigation and the gains achieved, in this paper, wireless communication systems were optimized and upgraded, making use of promising technologies including H-NOMA and THz communications. This process aimed to alleviate the THz transmission challenges and improve wireless connectivity, resource availability, processing, robustness, capacity, user-fairness, and overall performance of communication networks. It thoroughly optimized the best H-NOMA pairing scheme for cell users. The conducted results showed how the proposed technique managed to improve energy and spectral efficiencies compared to the related work by more than 75%, in addition to the dynamism of the introduced mechanism. This system reduces the transceivers’ hardware and computational complexity while improving reliability and transmission rates, without the need for complex technologies, e.g., multi-input multi-output or reflecting services. Full article
(This article belongs to the Special Issue 6G Wireless Communication Systems)
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21 pages, 2689 KiB  
Article
Robust Handover Optimization Technique with Fuzzy Logic Controller for Beyond 5G Mobile Networks
by Saddam Alraih 1, Rosdiadee Nordin 1, Asma Abu-Samah 1,*, Ibraheem Shayea 2,3, Nor Fadzilah Abdullah 1 and Abdulraqeb Alhammadi 4
1 Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
2 Electronics and Communication Engineering Department, Istanbul Technical University, Istanbul 34467, Turkey
3 Wireless Communication Centre, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
4 Communication Systems and Networks Research Lab, Malaysia-Japan International Institute of Technology, University Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Sensors 2022, 22(16), 6199; https://doi.org/10.3390/s22166199 - 18 Aug 2022
Cited by 31 | Viewed by 3636
Abstract
Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key [...] Read more.
Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE’s mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics. Full article
(This article belongs to the Special Issue Towards Next Generation beyond 5G (B5G) Networks)
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13 pages, 16065 KiB  
Article
Improved A* Path Planning Method Based on the Grid Map
by Yangqi Ou 1,*, Yuexin Fan 2,*, Xinglan Zhang 2, Yanhua Lin 2 and Weijing Yang 2
1 College of Automation, Chongqing University, Chongqing 400044, China
2 College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China
Sensors 2022, 22(16), 6198; https://doi.org/10.3390/s22166198 - 18 Aug 2022
Cited by 54 | Viewed by 5881
Abstract
In obstacle spatial path planning, the traditional A* algorithm has the problem of too many turning points and slow search speed. With this in mind, a path planning method that improves the A* (A-Star) algorithm is proposed. The mobile robot platform was equipped [...] Read more.
In obstacle spatial path planning, the traditional A* algorithm has the problem of too many turning points and slow search speed. With this in mind, a path planning method that improves the A* (A-Star) algorithm is proposed. The mobile robot platform was equipped with a lidar and inertial measurement unit (IMU). The Hdl_graph_slam mapping algorithm was used to construct a two-dimensional grid map, and the improved A* algorithm was used for path planning of the mobile robot. The algorithm introduced the path smoothing strategy and safety protection mechanism, and it eliminated redundant points and minimal corner points by judging whether there were obstacles in the connection of two path nodes. The algorithm effectively improved the smoothness of the path and facilitated the robot to move in the actual operation. It could avoid the wear of the robot by expanding obstacles and improving the safety performance of the robot. Subsequently, the algorithm introduced the steering cost model and the adaptive cost function to improve the search efficiency, making the search purposeful and effective. Lastly, the effectiveness of the proposed algorithm was verified by experiments. The average path search time was reduced by 13%. The average search extension node was reduced by 11%. The problems of too many turning points and slow search speed of traditional A* algorithm in path planning were improved. Full article
(This article belongs to the Section Sensing and Imaging)
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