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Sensors, Volume 23, Issue 1 (January-1 2023) – 556 articles

Cover Story (view full-size image): The safety risk associated with multilayer materials for food packaging applications mainly relies on the potential formation of primary aromatic amines (PAAs) when polyurethane adhesives are used as tie layers. PAAs can migrate from the packaging material to the food, posing a serious risk to the consumers’ health due to their carcinogenicity. Finding rapid, easy to use, and cheap analytical methods for the in-line quantification of PAAs are of utmost importance, especially at the industrial level. This work discusses how to obtain a new, selective, and sensitive electrochemical sensor for the detection of 4,4’-methylene diphenyl diamine (MDA) through a combined nanotechnology and polymer engineering approach. View this paper
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19 pages, 11921 KiB  
Article
Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network
by Min Khant, Darwin Gouwanda, Alpha A. Gopalai, King Hann Lim and Chee Choong Foong
Sensors 2023, 23(1), 556; https://doi.org/10.3390/s23010556 - 3 Jan 2023
Cited by 7 | Viewed by 4792
Abstract
The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of [...] Read more.
The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of gait quality. Muscle behaviour can be estimated using musculoskeletal modelling or measured using an electromyogram (EMG). However, both methods can be tasking and resource intensive. A combination of IMU and neural networks (NN) has the potential to overcome this limitation. Therefore, this study proposes using NN and IMU data to estimate nine lower extremity muscle activities. Two NN were developed and investigated, namely feedforward neural network (FNN) and long short-term memory neural network (LSTM). The results show that, although both networks were able to predict muscle activities well, LSTM outperformed the conventional FNN. This study confirms the feasibility of estimating muscle activity using IMU data and NN. It also indicates the possibility of this method enabling the gait analysis to be performed outside the laboratory environment with a limited number of devices. Full article
(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)
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30 pages, 2729 KiB  
Review
A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles
by Saroj Kumar Panigrahy and Harika Emany
Sensors 2023, 23(1), 555; https://doi.org/10.3390/s23010555 - 3 Jan 2023
Cited by 16 | Viewed by 5852
Abstract
The Internet of Things (IoT) has risen from ubiquitous computing to the Internet itself. Internet of vehicles (IoV) is the next emerging trend in IoT. We can build intelligent transportation systems (ITS) using IoV. However, overheads are imposed on IoV network due to [...] Read more.
The Internet of Things (IoT) has risen from ubiquitous computing to the Internet itself. Internet of vehicles (IoV) is the next emerging trend in IoT. We can build intelligent transportation systems (ITS) using IoV. However, overheads are imposed on IoV network due to a massive quantity of information being transferred from the devices connected in IoV. One such overhead is the network connection between the units of an IoV. To make an efficient ITS using IoV, optimization of network connectivity is required. A survey on network optimization in IoT and IoV is presented in this study. It also highlights the backdrop of IoT and IoV. This includes the applications, such as ITS with comparison to different advancements, optimization of the network, IoT discussions, along with categorization of algorithms. Some of the simulation tools are also explained which will help the research community to use those tools for pursuing research in IoV. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems Based on Sensor Fusion)
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15 pages, 3212 KiB  
Article
PN Codes Estimation of Binary Phase Shift Keying Signal Based on Sparse Recovery for Radar Jammer
by Bo Peng and Qile Chen
Sensors 2023, 23(1), 554; https://doi.org/10.3390/s23010554 - 3 Jan 2023
Cited by 2 | Viewed by 2114
Abstract
Parameter estimation is extremely important for a radar jammer. With binary phase shift keying (BPSK) signals widely applied in radar systems, estimating the parameters of BPSK signals has attracted increasing attention. However, the BPSK signal is difficult to be processed by traditional time [...] Read more.
Parameter estimation is extremely important for a radar jammer. With binary phase shift keying (BPSK) signals widely applied in radar systems, estimating the parameters of BPSK signals has attracted increasing attention. However, the BPSK signal is difficult to be processed by traditional time frequency analysis methods due to its phase jumping and abrupt discontinuity features which makes it difficult to extract PN (PN) codes of the BPSK signal. To solve this problem, a two-step PN codes estimation method based on sparse recovery is introduced in this paper. The proposed method first pretreats the BPSK signal by estimating its center frequency and converting it to zero intermediate frequency (ZIF). The pretreatment transforms phase jumps of the BPSK signal into the level jumps of the ZIF signal. By nonconvex sparsity promoting regularization, the level jumps of the ZIF signal are extracted through an iterative algorithm. Its effectiveness is verified by numeric simulations and semiphysical tests. The corresponding results demonstrate that the proposed method is able to estimate PN codes from the BPSK signal in serious electromagnetic environments. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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18 pages, 3816 KiB  
Article
Deep Learning Framework for Controlling Work Sequence in Collaborative Human–Robot Assembly Processes
by Pedro P. Garcia, Telmo G. Santos, Miguel A. Machado and Nuno Mendes
Sensors 2023, 23(1), 553; https://doi.org/10.3390/s23010553 - 3 Jan 2023
Cited by 7 | Viewed by 2915
Abstract
The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and [...] Read more.
The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and slowing down the pace of human labor, making such solutions uninteresting. In this study, a different concept of the HRC system is introduced, consisting of an HRC framework for managing assembly processes that are executed simultaneously or individually by humans and robots. This HRC framework based on deep learning models uses only one type of data, RGB camera data, to make predictions about the collaborative workspace and human action, and consequently manage the assembly process. To validate the HRC framework, an industrial HRC demonstrator was built to assemble a mechanical component. Four different HRC frameworks were created based on the convolutional neural network (CNN) model structures: Faster R-CNN ResNet-50 and ResNet-101, YOLOv2 and YOLOv3. The HRC framework with YOLOv3 structure showed the best performance, showing a mean average performance of 72.26% and allowed the HRC industrial demonstrator to successfully complete all assembly tasks within a desired time window. The HRC framework has proven effective for industrial assembly applications. Full article
(This article belongs to the Special Issue Sensors for Robotic Applications in Europe)
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28 pages, 5792 KiB  
Article
Non Linear Control System for Humanoid Robot to Perform Body Language Movements
by Juan Manuel Gomez-Quispe, Gustavo Pérez-Zuñiga, Diego Arce, Fiorella Urbina, Sareli Gibaja, Renato Paredes and Francisco Cuellar
Sensors 2023, 23(1), 552; https://doi.org/10.3390/s23010552 - 3 Jan 2023
Cited by 4 | Viewed by 3396
Abstract
In social robotics, especially with regard to direct interactions between robots and humans, the robotic movements of the body, arms and head must make an adequate displacement to guarantee an adequate interaction, both from a functional and social point of view. To achieve [...] Read more.
In social robotics, especially with regard to direct interactions between robots and humans, the robotic movements of the body, arms and head must make an adequate displacement to guarantee an adequate interaction, both from a functional and social point of view. To achieve this, the use of closed-loop control techniques that consider the complex nonlinear dynamics and disturbances inherent in these systems is required. In this paper, an implementation of a nonlinear controller for the tracking of trajectories and a profile of speeds that execute the movements of the arms and head of a humanoid robot based on the mathematical model is proposed. First, the design and implementation of the arms and head are initially presented, then the mathematical model via kinematic and dynamic analysis was performed. With the above, the design of nonlinear controllers such as nonlinear proportional derivative control with gravity compensation, Backstepping control, Sliding Mode control and the application of each of them to the robotic system are presented. A comparative analysis based on a frequency analysis, the efficiency in polynomial trajectories and the implementation requirements allowed selecting the non-linear Backstepping control technique to be implemented. Then, for the implementation, a centralized control architecture is considered, which uses a central microcontroller in the external loop and an internal microcontroller (as internal loop) for each of the actuators. With the above, the selected controller was validated through experiments performed in real time on the implemented humanoid robot, demonstrating proper path tracking of established trajectories for performing body language movements. Full article
(This article belongs to the Special Issue Social Robots and Applications)
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27 pages, 7387 KiB  
Article
Design and Implementation of a Passive Autoranging Circuit for Hybrid FBG-PZT Photonic Current Transducer
by Burhan Mir, Pawel Niewczas and Grzegorz Fusiek
Sensors 2023, 23(1), 551; https://doi.org/10.3390/s23010551 - 3 Jan 2023
Cited by 2 | Viewed by 2108
Abstract
In this paper, we present a novel technique for passively autoranging a photonic current transducer (PCT) that incorporates a current transformer (CT), piezoelectric transducer (PZT) and fiber Bragg grating (FBG). Due to the usage of single-mode fiber and FBG, multiple PCTs can be [...] Read more.
In this paper, we present a novel technique for passively autoranging a photonic current transducer (PCT) that incorporates a current transformer (CT), piezoelectric transducer (PZT) and fiber Bragg grating (FBG). Due to the usage of single-mode fiber and FBG, multiple PCTs can be interconnected and distributed over a long distance, for example along a power network, greatly reducing the cost of sensor deployment and offering other unique advantages. The autoranging technique relies on the usage of multiple, serially connected CT burden resistors and associated static MOSFET switches to realize instantaneous shortening of the resistors in response to increasing measured current. This functionality is realized passively, utilizing a modular, μW-power comparator circuit that powers itself from the electrical energy supplied by the CT within a small fraction of the 50/60 Hz cycle. The resultant instantaneous changes in sensor gain will be ultimately detected by the central FBG interrogator through real-time analysis of the optical signals and will be used to apply appropriate gain scaling for each sensor. The technique will facilitate the usage of a single PCT to cover an extended dynamic range of the measurement that is required to realize a combined metering- and protection-class current sensor. This paper is limited to the description of the design process, construction, and testing of a prototype passive autoranging circuitry for integration with the PCT. The two-stage circuitry that is based on two burden resistors, 1 Ω and 10 Ω, is used to prove the concept and demonstrate the practically achievable circuit characteristics. It is shown that the circuit correctly reacts to input current threshold breaches of approximately 2 A and 20 A within a 3 ms reaction time. The circuit produces distinct voltage dips across burden resistors that will be used for signal scaling by the FBG interrogator. Full article
(This article belongs to the Special Issue Optical Sensing in Power Systems)
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26 pages, 5980 KiB  
Article
An Imbalanced Generative Adversarial Network-Based Approach for Network Intrusion Detection in an Imbalanced Dataset
by Yamarthi Narasimha Rao and Kunda Suresh Babu
Sensors 2023, 23(1), 550; https://doi.org/10.3390/s23010550 - 3 Jan 2023
Cited by 15 | Viewed by 3209
Abstract
In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device for detecting unauthorized activity. The categorization effectiveness for minority classes is limited by the imbalanced class issues connected with the dataset. We propose an Imbalanced Generative Adversarial Network (IGAN) [...] Read more.
In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device for detecting unauthorized activity. The categorization effectiveness for minority classes is limited by the imbalanced class issues connected with the dataset. We propose an Imbalanced Generative Adversarial Network (IGAN) to address the problem of class imbalance by increasing the detection rate of minority classes while maintaining efficiency. To limit the effect of the minimum or maximum value on the overall features, the original data was normalized and one-hot encoded using data preprocessing. To address the issue of the low detection rate of minority attacks caused by the imbalance in the training data, we enrich the minority samples with IGAN. The ensemble of Lenet 5 and Long Short Term Memory (LSTM) is used to classify occurrences that are considered abnormal into various attack categories. The investigational findings demonstrate that the proposed approach outperforms the other deep learning approaches, achieving the best accuracy, precision, recall, TPR, FPR, and F1-score. The findings indicate that IGAN oversampling can enhance the detection rate of minority samples, hence improving overall accuracy. According to the data, the recommended technique valued performance measures far more than alternative approaches. The proposed method is found to achieve above 98% accuracy and classifies various attacks significantly well as compared to other classifiers. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 42118 KiB  
Article
Plant-Wear: A Multi-Sensor Plant Wearable Platform for Growth and Microclimate Monitoring
by Joshua Di Tocco, Daniela Lo Presti, Carlo Massaroni, Stefano Cinti, Sara Cimini, Laura De Gara and Emiliano Schena
Sensors 2023, 23(1), 549; https://doi.org/10.3390/s23010549 - 3 Jan 2023
Cited by 6 | Viewed by 3661
Abstract
Wearable devices are widely spreading in various scenarios for monitoring different parameters related to human and recently plant health. In the context of precision agriculture, wearables have proven to be a valuable alternative to traditional measurement methods for quantitatively monitoring plant development. This [...] Read more.
Wearable devices are widely spreading in various scenarios for monitoring different parameters related to human and recently plant health. In the context of precision agriculture, wearables have proven to be a valuable alternative to traditional measurement methods for quantitatively monitoring plant development. This study proposed a multi-sensor wearable platform for monitoring the growth of plant organs (i.e., stem and fruit) and microclimate (i.e., environmental temperature—T and relative humidity—RH). The platform consists of a custom flexible strain sensor for monitoring growth when mounted on a plant and a commercial sensing unit for monitoring T and RH values of the plant surrounding. A different shape was conferred to the strain sensor according to the plant organs to be engineered. A dumbbell shape was chosen for the stem while a ring shape for the fruit. A metrological characterization was carried out to investigate the strain sensitivity of the proposed flexible sensors and then preliminary tests were performed in both indoor and outdoor scenarios to assess the platform performance. The promising results suggest that the proposed system can be considered one of the first attempts to design wearable and portable systems tailored to the specific plant organ with the potential to be used for future applications in the coming era of digital farms and precision agriculture. Full article
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23 pages, 995 KiB  
Article
Speeding Task Allocation Search for Reconfigurations in Adaptive Distributed Embedded Systems Using Deep Reinforcement Learning
by Ramón Rotaeche, Alberto Ballesteros and Julián Proenza
Sensors 2023, 23(1), 548; https://doi.org/10.3390/s23010548 - 3 Jan 2023
Viewed by 1886
Abstract
A Critical Adaptive Distributed Embedded System (CADES) is a group of interconnected nodes that must carry out a set of tasks to achieve a common goal, while fulfilling several requirements associated with their critical (e.g., hard real-time requirements) and adaptive nature. In these [...] Read more.
A Critical Adaptive Distributed Embedded System (CADES) is a group of interconnected nodes that must carry out a set of tasks to achieve a common goal, while fulfilling several requirements associated with their critical (e.g., hard real-time requirements) and adaptive nature. In these systems, a key challenge is to solve, in a timely manner, the combinatorial optimization problem involved in finding the best way to allocate the tasks to the available nodes (i.e., the task allocation) taking into account aspects such as the computational costs of the tasks and the computational capacity of the nodes. This problem is not trivial and there is no known polynomial time algorithm to find the optimal solution. Several studies have proposed Deep Reinforcement Learning (DRL) approaches to solve combinatorial optimization problems and, in this work, we explore the application of such approaches to the task allocation problem in CADESs. We first discuss the potential advantages of using a DRL-based approach over several heuristic-based approaches to allocate tasks in CADESs and we then demonstrate how a DRL-based approach can achieve similar results for the best performing heuristic in terms of optimality of the allocation, while requiring less time to generate such allocation. Full article
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13 pages, 5627 KiB  
Article
Real-Time LiDAR Point-Cloud Moving Object Segmentation for Autonomous Driving
by Xing Xie, Haowen Wei and Yongjie Yang
Sensors 2023, 23(1), 547; https://doi.org/10.3390/s23010547 - 3 Jan 2023
Cited by 5 | Viewed by 4892
Abstract
The key to autonomous navigation in unmanned systems is the ability to recognize static and moving objects in the environment and to support the task of predicting the future state of the environment, avoiding collisions, and planning. However, because the existing 3D LiDAR [...] Read more.
The key to autonomous navigation in unmanned systems is the ability to recognize static and moving objects in the environment and to support the task of predicting the future state of the environment, avoiding collisions, and planning. However, because the existing 3D LiDAR point-cloud moving object segmentation (MOS) convolutional neural network (CNN) models are very complex and have large computation burden, it is difficult to perform real-time processing on embedded platforms. In this paper, we propose a lightweight MOS network structure based on LiDAR point-cloud sequence range images with only 2.3 M parameters, which is 66% less than the state-of-the-art network. When running on RTX 3090 GPU, the processing time is 35.82 ms per frame and it achieves an intersection-over-union(IoU) score of 51.3% on the SemanticKITTI dataset. In addition, the proposed CNN successfully runs the FPGA platform using an NVDLA-like hardware architecture, and the system achieves efficient and accurate moving-object segmentation of LiDAR point clouds at a speed of 32 fps, meeting the real-time requirements of autonomous vehicles. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding)
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13 pages, 3855 KiB  
Article
Deployment of Smart Specimen Transport System Using RFID and NB-IoT Technologies for Hospital Laboratory
by Ngoc Thien Le, Mya Myet Thwe Chit, Thanh Le Truong, Atchasai Siritantikorn, Narisorn Kongruttanachok, Widhyakorn Asdornwised, Surachai Chaitusaney and Watit Benjapolakul
Sensors 2023, 23(1), 546; https://doi.org/10.3390/s23010546 - 3 Jan 2023
Cited by 4 | Viewed by 2761
Abstract
In this study, we propose a specimen tube prototype and smart specimen transport box using radio frequency identification (RFID) and narrow band–Internet of Things (NB-IoT) technology to use in the Department of Laboratory Medicine, King Chulalongkorn Memorial Hospital. Our proposed method replaces the [...] Read more.
In this study, we propose a specimen tube prototype and smart specimen transport box using radio frequency identification (RFID) and narrow band–Internet of Things (NB-IoT) technology to use in the Department of Laboratory Medicine, King Chulalongkorn Memorial Hospital. Our proposed method replaces the existing system, based on barcode technology, with shortage usage and low reliability. In addition, tube-tagged barcode has not eliminated the lost or incorrect delivery issues in many laboratories. In this solution, the passive RFID tag is attached to the surface of the specimen tube and stores information such as patient records, required tests, and receiver laboratory location. This information can be written and read multiple times using an RFID device. While delivering the specimen tubes via our proposed smart specimen transport box from one clinical laboratory to another, the NB-IoT attached to the box monitors the temperature and humidity values inside the box and tracks the box’s GPS location to check whether the box arrives at the destination. The environmental condition inside the specimen transport box is sent to the cloud and can be monitored by doctors. The experimental results have proven the innovation of our solution and opened a new dimension for integrating RFID and IoT technologies into the specimen logistic system in the hospital. Full article
(This article belongs to the Special Issue RFID Technology for Sensing, Biosensing, and the Internet of Things)
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18 pages, 1934 KiB  
Article
Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal
by Carina S. González-González, Vanesa Muñoz-Cruz, Pedro Antonio Toledo-Delgado and Eduardo Nacimiento-García
Sensors 2023, 23(1), 545; https://doi.org/10.3390/s23010545 - 3 Jan 2023
Cited by 13 | Viewed by 4838
Abstract
A key factor for successfully implementing gamified learning platforms is making students interact with the system from multiple digital platforms. Learning platforms that try to accomplish all their objectives by concentrating all the interactions from users with them are less effective than initially [...] Read more.
A key factor for successfully implementing gamified learning platforms is making students interact with the system from multiple digital platforms. Learning platforms that try to accomplish all their objectives by concentrating all the interactions from users with them are less effective than initially believed. Conversational bots are ideal solutions for cross-platform user interaction. In this paper, an open student–player model is presented. The model includes the use of machine learning techniques for online adaptation. Then, an architecture for the solution is described, including the open model. Finally, the chatbot design is addressed. The chatbot architecture ensures that its reactive nature fits into our defined architecture. The approach’s implementation and validation aim to create a tool to encourage kids to practice multiplication tables playfully. Full article
(This article belongs to the Special Issue Cognitive Architectures for Robots Learning of In-Hand Manipulation)
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18 pages, 4272 KiB  
Article
DSTEELNet: A Real-Time Parallel Dilated CNN with Atrous Spatial Pyramid Pooling for Detecting and Classifying Defects in Surface Steel Strips
by Khaled R. Ahmed
Sensors 2023, 23(1), 544; https://doi.org/10.3390/s23010544 - 3 Jan 2023
Cited by 6 | Viewed by 2867
Abstract
Automatic defects inspection and classification demonstrate significant importance in improving quality in the steel industry. This paper proposed and developed DSTEELNet convolution neural network (CNN) architecture to improve detection accuracy and the required time to detect defects in surface steel strips. DSTEELNet includes [...] Read more.
Automatic defects inspection and classification demonstrate significant importance in improving quality in the steel industry. This paper proposed and developed DSTEELNet convolution neural network (CNN) architecture to improve detection accuracy and the required time to detect defects in surface steel strips. DSTEELNet includes three parallel stacks of convolution blocks with atrous spatial pyramid pooling. Each convolution block used a different dilation rate that expands the receptive fields, increases the feature resolutions and covers square regions of input 2D image without any holes or missing edges and without increases in computations. This work illustrates the performance of DSTEELNet with a different number of parallel stacks and a different order of dilation rates. The experimental results indicate significant improvements in accuracy and illustrate that the DSTEELNet achieves of 97% mAP in detecting defects in surface steel strips on the augmented dataset GNEU and Severstal datasets and is able to detect defects in a single image in 23ms. Full article
(This article belongs to the Section Sensing and Imaging)
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20 pages, 4300 KiB  
Review
Piezoelectric Materials and Sensors for Structural Health Monitoring: Fundamental Aspects, Current Status, and Future Perspectives
by Min Ju, Zhongshang Dou, Jia-Wang Li, Xuting Qiu, Binglin Shen, Dawei Zhang, Fang-Zhou Yao, Wen Gong and Ke Wang
Sensors 2023, 23(1), 543; https://doi.org/10.3390/s23010543 - 3 Jan 2023
Cited by 42 | Viewed by 9430
Abstract
Structural health monitoring technology can assess the status and integrity of structures in real time by advanced sensors, evaluate the remaining life of structure, and make the maintenance decisions on the structures. Piezoelectric materials, which can yield electrical output in response to mechanical [...] Read more.
Structural health monitoring technology can assess the status and integrity of structures in real time by advanced sensors, evaluate the remaining life of structure, and make the maintenance decisions on the structures. Piezoelectric materials, which can yield electrical output in response to mechanical strain/stress, are at the heart of structural health monitoring. Here, we present an overview of the recent progress in piezoelectric materials and sensors for structural health monitoring. The article commences with a brief introduction of the fundamental physical science of piezoelectric effect. Emphases are placed on the piezoelectric materials engineered by various strategies and the applications of piezoelectric sensors for structural health monitoring. Finally, challenges along with opportunities for future research and development of high-performance piezoelectric materials and sensors for structural health monitoring are highlighted. Full article
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23 pages, 5220 KiB  
Review
Long-Period Fiber Grating Sensors for Chemical and Biomedical Applications
by Jintao Cai, Yulei Liu and Xuewen Shu
Sensors 2023, 23(1), 542; https://doi.org/10.3390/s23010542 - 3 Jan 2023
Cited by 12 | Viewed by 2979
Abstract
Optical fiber biosensors (OFBS) are being increasingly proposed due to their intrinsic advantages over conventional sensors, including their compactness, potential remote control and immunity to electromagnetic interference. This review systematically introduces the advances of OFBS based on long-period fiber gratings (LPFGs) for chemical [...] Read more.
Optical fiber biosensors (OFBS) are being increasingly proposed due to their intrinsic advantages over conventional sensors, including their compactness, potential remote control and immunity to electromagnetic interference. This review systematically introduces the advances of OFBS based on long-period fiber gratings (LPFGs) for chemical and biomedical applications from the perspective of design and functionalization. The sensitivity of such a sensor can be enhanced by designing the device working at or near the dispersion turning point, or working around the mode transition, or their combination. In addition, several common functionalization methods are summarized in detail, such as the covalent immobilization of 3-aminopropyltriethoxysilane (APTES) silanization and graphene oxide (GO) functionalization, and the noncovalent immobilization of the layer-by-layer assembly method. Moreover, reflective LPFG-based sensors with different configurations have also been introduced. This work aims to provide a comprehensive understanding of LPFG-based biosensors and to suggest some future directions for exploration. Full article
(This article belongs to the Special Issue Optical Fiber Sensors for Chemical and Biomedical Applications)
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23 pages, 514 KiB  
Article
Continuous and Secure Integration Framework for Smart Contracts
by Alvaro Reyes, Miguel Jimeno and Ricardo Villanueva-Polanco
Sensors 2023, 23(1), 541; https://doi.org/10.3390/s23010541 - 3 Jan 2023
Cited by 1 | Viewed by 2633
Abstract
As part of agile methodologies seen in the past few years, IT organizations have continuously adopted new practices in their software delivery life-cycle to improve both efficiency and effectiveness of development teams. Two of these practices are continuous integration and continuous deployment, which [...] Read more.
As part of agile methodologies seen in the past few years, IT organizations have continuously adopted new practices in their software delivery life-cycle to improve both efficiency and effectiveness of development teams. Two of these practices are continuous integration and continuous deployment, which are part of the DevOps cycle which has helped organizations build software effectively and efficiently. These practices must be considered for new technologies such as smart contracts, where security concerns and bugs might cost more once deployed than traditional software. This paper states the importance of using a proper DevOps routine and how it is possible to apply this practice to a smart contract build. Specifically, this paper introduces a framework to implement DevOps for smart contracts development by describing multiple DevOps tools and their applicability to smart contract development. Full article
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28 pages, 8590 KiB  
Article
Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems
by Shifat Hossain and Ki-Doo Kim
Sensors 2023, 23(1), 540; https://doi.org/10.3390/s23010540 - 3 Jan 2023
Cited by 4 | Viewed by 2204
Abstract
The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with [...] Read more.
The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with pricking the fingertip or using minimally invasive patches. In this study, we devise a noninvasive method to estimate the percentage of the in vivo glycated hemoglobin (HbA1c) values from Monte Carlo photon propagation simulations, based on models of the wrist using 3D magnetic resonance (MR) image data. The MR image slices are first segmented for several different tissue types, and the proposed Monte Carlo photon propagation system with complex composite tissue support is then used to derive several models for the fingertip and wrist sections with different wavelengths of light sources and photodetector arrangements. The Pearson r values for the estimated percent HbA1c values are 0.94 and 0.96 for the fingertip transmission- and reflection-type measurements, respectively. This is found to be the best among the related studies. Furthermore, a single-detector multiple-source arrangement resulted in a Pearson r value of 0.97 for the wrist. The Bland–Altman bias values were found to be −0.003 ± 0.36, 0.01 ± 0.25, and 0.01 ± 0.21, for the two fingertip and wrist models, respectively, which conform to the standards of the current state-of-the-art invasive point-of-care devices. The implementation of these algorithms will be a suitable alternative to the invasive state-of-the-art methods. Full article
(This article belongs to the Special Issue Optical Biosensors for Healthcare Monitoring)
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22 pages, 2693 KiB  
Article
Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test
by Ryota Shin, Yukihiko Okada and Kyosuke Yamamoto
Sensors 2023, 23(1), 539; https://doi.org/10.3390/s23010539 - 3 Jan 2023
Cited by 2 | Viewed by 2914
Abstract
For infrastructures to be sustainable, it is essential to improve maintenance and management efficiency. Vibration-based monitoring methods are being investigated to improve the efficiency of infrastructure maintenance and management. In this paper, signals from acceleration sensors attached to vehicles traveling on bridges are [...] Read more.
For infrastructures to be sustainable, it is essential to improve maintenance and management efficiency. Vibration-based monitoring methods are being investigated to improve the efficiency of infrastructure maintenance and management. In this paper, signals from acceleration sensors attached to vehicles traveling on bridges are processed. Methods have been proposed to individually estimate the modal parameters of bridges and road unevenness from vehicle vibrations. This study proposes a method to simultaneously estimate the mechanical parameters of the vehicle, bridge, and road unevenness with only a few constraints. Numerical validation examined the effect of introducing the Kalman filter on the accuracy of estimating the mechanical parameters of vehicles and bridges. In field tests, vehicle vibration, bridge vibration, and road unevenness were measured and verified, respectively. The road surface irregularities estimated by the proposed method were compared with the measured values, which were somewhat smaller than the measured values. Future studies are needed to improve the efficiency of vehicle vibration preprocessing and optimization methods and to establish a methodology for evaluating accuracy. Full article
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22 pages, 5922 KiB  
Article
Experimental Procedure for the Metrological Characterization of Time-of-Flight Cameras for Human Body 3D Measurements
by Simone Pasinetti, Cristina Nuzzi, Alessandro Luchetti, Matteo Zanetti, Matteo Lancini and Mariolino De Cecco
Sensors 2023, 23(1), 538; https://doi.org/10.3390/s23010538 - 3 Jan 2023
Cited by 3 | Viewed by 2322
Abstract
Time-of-flight cameras are widely adopted in a variety of indoor applications ranging from industrial object measurement to human activity recognition. However, the available products may differ in terms of the quality of the acquired point cloud, and the datasheet provided by the constructors [...] Read more.
Time-of-flight cameras are widely adopted in a variety of indoor applications ranging from industrial object measurement to human activity recognition. However, the available products may differ in terms of the quality of the acquired point cloud, and the datasheet provided by the constructors may not be enough to guide researchers in the choice of the perfect device for their application. Hence, this work details the experimental procedure to assess time-of-flight cameras’ error sources that should be considered when designing an application involving time-of-flight technology, such as the bias correction and the temperature influence on the point cloud stability. This is the first step towards a standardization of the metrological characterization procedure that could ensure the robustness and comparability of the results among tests and different devices. The procedure was conducted on Kinect Azure, Basler Blaze 101, and Basler ToF 640 cameras. Moreover, we compared the devices in the task of 3D reconstruction following a procedure involving the measure of both an object and a human upper-body-shaped mannequin. The experiment highlighted that, despite the results of the previously conducted metrological characterization, some devices showed evident difficulties in reconstructing the target objects. Thus, we proved that performing a rigorous evaluation procedure similar to the one proposed in this paper is always necessary when choosing the right device. Full article
(This article belongs to the Section Optical Sensors)
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14 pages, 10422 KiB  
Article
Comparison of a Wearable Accelerometer/Gyroscopic, Portable Gait Analysis System (LEGSYS+TM) to the Laboratory Standard of Static Motion Capture Camera Analysis
by Ryan Homes, Devon Clark, Sina Moridzadeh, Danijel Tosovic, Wolbert Van den Hoorn, Kylie Tucker and Mark Midwinter
Sensors 2023, 23(1), 537; https://doi.org/10.3390/s23010537 - 3 Jan 2023
Cited by 1 | Viewed by 2260
Abstract
Examination of gait patterns has been used to determine severity, intervention triage and prognostic measures for many health conditions. Methods that generate detailed gait data for clinical use are typically logistically constrained to a formal gait laboratory setting. This has led to an [...] Read more.
Examination of gait patterns has been used to determine severity, intervention triage and prognostic measures for many health conditions. Methods that generate detailed gait data for clinical use are typically logistically constrained to a formal gait laboratory setting. This has led to an interest in portable analysis systems for near clinical or community-based assessments. The following study assessed with the wearable accelerometer/gyroscopic, gait analysis system (LEGSYS+TM) and the standard of static motion capture camera (MOCAP) analysis during a treadmill walk at three different walking speeds in healthy participants (n = 15). To compare each speed, 20 strides were selected from the MOCAP data and compared with the LEGSYS+ strides at the same time point. Both scatter and bland-Altman plots with accompanying linear regression analysis for each of the parameters. Each stride parameter showed minimal or a consistent difference between the LEGSYS+ and MOCAP, with the phase parameters showing inconsistencies between the systems. Overall, LEGSYS+ stride parameters can be used in the clinical setting, with the utility of phase parameters needing to be taken with caution. Full article
(This article belongs to the Section Wearables)
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21 pages, 1189 KiB  
Review
A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review
by Jia Min Yen and Jeong Hoon Lim
Sensors 2023, 23(1), 536; https://doi.org/10.3390/s23010536 - 3 Jan 2023
Cited by 7 | Viewed by 2159
Abstract
Neurological diseases including stroke and neurodegenerative disorders cause a hefty burden on the healthcare system. Survivors experience significant impairment in mobility and daily activities, which requires extensive rehabilitative interventions to assist them to regain lost skills and restore independence. The advent of remote [...] Read more.
Neurological diseases including stroke and neurodegenerative disorders cause a hefty burden on the healthcare system. Survivors experience significant impairment in mobility and daily activities, which requires extensive rehabilitative interventions to assist them to regain lost skills and restore independence. The advent of remote rehabilitation architecture and enabling technology mandates the elaboration of sensing mechanisms tailored to individual clinical needs. This study aims to review current trends in the application of sensing mechanisms in remote monitoring and rehabilitation in neurological diseases, and to provide clinical insights to develop bespoke sensing mechanisms. A systematic search was performed using the PubMED database to identify 16 papers published for the period between 2018 to 2022. Teleceptive sensors (56%) were utilized more often than wearable proximate sensors (50%). The most commonly used modality was infrared (38%) and acceleration force (38%), followed by RGB color, EMG, light and temperature, and radio signal. The strategy adopted to improve the sensing mechanism included a multimodal sensor, the application of multiple sensors, sensor fusion, and machine learning. Most of the stroke studies utilized biofeedback control systems (78%) while the majority of studies for neurodegenerative disorders used sensors for remote monitoring (57%). Functional assessment tools that the sensing mechanism may emulate to produce clinically valid information were proposed and factors affecting user adoption were described. Lastly, the limitations and directions for further development were discussed. Full article
(This article belongs to the Special Issue Wearable Sensors for Neurological Diseases Remote Monitoring)
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19 pages, 2649 KiB  
Article
Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly
by Kanta Matsumoto, Tomokazu Matsui, Hirohiko Suwa and Keiichi Yasumoto
Sensors 2023, 23(1), 535; https://doi.org/10.3390/s23010535 - 3 Jan 2023
Cited by 1 | Viewed by 2374
Abstract
It is essential to estimate the stress state of the elderly to improve their QoL. Stress states change every day and hour, depending on the activities performed and the duration/intensity. However, most existing studies estimate stress states using only biometric information or specific [...] Read more.
It is essential to estimate the stress state of the elderly to improve their QoL. Stress states change every day and hour, depending on the activities performed and the duration/intensity. However, most existing studies estimate stress states using only biometric information or specific activities (e.g., sleep duration, exercise duration/amount, etc.) as explanatory variables and do not consider all daily living activities. It is necessary to link various daily living activities and biometric information in order to estimate the stress state more accurately. Specifically, we construct a stress estimation model using machine learning with the answers to a stress status questionnaire obtained every morning and evening as the ground truth and the biometric data during each of the performed activities and the new proposed indicator including biological and activity perspectives as the features. We used the following methods: Baseline Method 1, in which the RRI variance and Lorenz plot area for 4 h after waking and 24 h before the questionnaire were used as features; Baseline Method 2, in which sleep time was added as a feature to Baseline Method 1; the proposed method, in which the Lorenz plot area per activity and total time per activity were added. We compared the results with the proposed method, which added the new indicators as the features. The results of the evaluation experiments using the one-month data collected from five elderly households showed that the proposed method had an average estimation accuracy of 59%, 7% better than Baseline Method 1 (52%) and 4% better than Baseline Method 2 (55%). Full article
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17 pages, 3069 KiB  
Article
Use of a Thermodynamic Sensor in Monitoring Fermentation Processes in Gluten-Free Dough Proofing
by Martin Adamek, Magdalena Zvonkova, Iva Buresova, Martin Buran, Veronika Sevcikova, Romana Sebestikova, Anna Adamkova, Nela Skowronkova and Jiri Mlcek
Sensors 2023, 23(1), 534; https://doi.org/10.3390/s23010534 - 3 Jan 2023
Cited by 2 | Viewed by 1732
Abstract
Dough fermentation in gluten-free bakery products is problematic due to the absence of gluten, which provides advantageous rheological properties. A thermodynamic sensor (TDS) system combined with an electronic nose was tested as an alternative to conventional methods monitoring dough development based on mechanical [...] Read more.
Dough fermentation in gluten-free bakery products is problematic due to the absence of gluten, which provides advantageous rheological properties. A thermodynamic sensor (TDS) system combined with an electronic nose was tested as an alternative to conventional methods monitoring dough development based on mechanical properties. In the first part, the configuration of the sensors in the thermodynamic system and their response to different heat-source positions, which significantly affect the output signal from the measurement system, were investigated. The practical contribution lies in the application of the measurements to the example of gluten-free doughs with and without edible insect enrichment. An optimized configuration of the thermodynamic system (one sensor on the inner wall of the container at the bottom and another in the middle of the container closer to the top of the dough) in combination with an experimental electronic nose was used for the aforementioned measurement. In some cases, up to 87% correlation between the signal from the TDS and the signals from a professional rheofermentometer Rheo F-4 (Chopin) was demonstrated. The differences between the results can be explained by the use of different techniques. Using a combination of sensor systems in one place, one time and one sample can lead to more comprehensive and robust results. Furthermore, it was shown that the fermentation activity increased in corn dough with the addition of insects compared to dough without the addition. In rice flour dough with the addition of edible insects, fermentation activity was similar to that of the flour without the addition. Full article
(This article belongs to the Section Smart Agriculture)
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12 pages, 616 KiB  
Article
Passive Fingerprinting of Same-Model Electrical Devices by Current Consumption
by Mikhail Ronkin and Dima Bykhovsky
Sensors 2023, 23(1), 533; https://doi.org/10.3390/s23010533 - 3 Jan 2023
Cited by 2 | Viewed by 1693
Abstract
One possible device authentication method is based on device fingerprints, such as software- or hardware-based unique characteristics. In this paper, we propose a fingerprinting technique based on passive externally measured information, i.e., current consumption from the electrical network. The key insight is that [...] Read more.
One possible device authentication method is based on device fingerprints, such as software- or hardware-based unique characteristics. In this paper, we propose a fingerprinting technique based on passive externally measured information, i.e., current consumption from the electrical network. The key insight is that small hardware discrepancies naturally exist even between same-electrical-circuit devices, making it feasible to identify slight variations in the consumed current under steady-state conditions. An experimental database of current consumption signals of two similar groups containing 20 same-model computer displays was collected. The resulting signals were classified using various state-of-the-art time-series classification (TSC) methods. We successfully identified 40 similar (same-model) electrical devices with about 94% precision, while most errors were concentrated in confusion between a small number of devices. A simplified empirical wavelet transform (EWT) paired with a linear discriminant analysis (LDA) classifier was shown to be the recommended classification method. Full article
(This article belongs to the Special Issue Machine Learning Engineering in Sensors Applications)
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21 pages, 6123 KiB  
Article
Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle
by Su Myat Noe, Thi Thi Zin, Pyke Tin and Ikuo Kobayashi
Sensors 2023, 23(1), 532; https://doi.org/10.3390/s23010532 - 3 Jan 2023
Cited by 11 | Viewed by 4310
Abstract
Effective livestock management is critical for cattle farms in today’s competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not feasible in today’s farming systems. Fortunately, automatic tracking [...] Read more.
Effective livestock management is critical for cattle farms in today’s competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not feasible in today’s farming systems. Fortunately, automatic tracking and identification systems have greatly improved in recent years. Moreover, correctly identifying individual cows is an integral part of predicting behavior during estrus. By doing so, we can monitor a cow’s behavior, and pinpoint the right time for artificial insemination. However, most previous techniques have relied on direct observation, increasing the human workload. To overcome this problem, this paper proposes the use of state-of-the-art deep learning-based Multi-Object Tracking (MOT) algorithms for a complete system that can automatically and continuously detect and track cattle using an RGB camera. This study compares state-of-the-art MOTs, such as Deep-SORT, Strong-SORT, and customized light-weight tracking algorithms. To improve the tracking accuracy of these deep learning methods, this paper presents an enhanced re-identification approach for a black cattle dataset in Strong-SORT. For evaluating MOT by detection, the system used the YOLO v5 and v7, as a comparison with the instance segmentation model Detectron-2, to detect and classify the cattle. The high cattle-tracking accuracy with a Multi-Object Tracking Accuracy (MOTA) was 96.88%. Using these methods, the findings demonstrate a highly accurate and robust cattle tracking system, which can be applied to innovative monitoring systems for agricultural applications. The effectiveness and efficiency of the proposed system were demonstrated by analyzing a sample of video footage. The proposed method was developed to balance the trade-off between costs and management, thereby improving the productivity and profitability of dairy farms; however, this method can be adapted to other domestic species. Full article
(This article belongs to the Section Smart Agriculture)
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38 pages, 2117 KiB  
Article
A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing
by Messaoud Babaghayou, Noureddine Chaib, Nasreddine Lagraa, Mohamed Amine Ferrag and Leandros Maglaras
Sensors 2023, 23(1), 531; https://doi.org/10.3390/s23010531 - 3 Jan 2023
Cited by 9 | Viewed by 2314
Abstract
By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard [...] Read more.
By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary’s exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy “Give up a Pawn to Save a Chariot”. Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use. Full article
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17 pages, 523 KiB  
Article
MALS-Net: A Multi-Head Attention-Based LSTM Sequence-to-Sequence Network for Socio-Temporal Interaction Modelling and Trajectory Prediction
by Fuad Hasan and Hailong Huang
Sensors 2023, 23(1), 530; https://doi.org/10.3390/s23010530 - 3 Jan 2023
Cited by 12 | Viewed by 4148
Abstract
Predicting the trajectories of surrounding vehicles is an essential task in autonomous driving, especially in a highway setting, where minor deviations in motion can cause serious road accidents. The future trajectory prediction is often not only based on historical trajectories but also on [...] Read more.
Predicting the trajectories of surrounding vehicles is an essential task in autonomous driving, especially in a highway setting, where minor deviations in motion can cause serious road accidents. The future trajectory prediction is often not only based on historical trajectories but also on a representation of the interaction between neighbouring vehicles. Current state-of-the-art methods have extensively utilized RNNs, CNNs and GNNs to model this interaction and predict future trajectories, relying on a very popular dataset known as NGSIM, which, however, has been criticized for being noisy and prone to overfitting issues. Moreover, transformers, which gained popularity from their benchmark performance in various NLP tasks, have hardly been explored in this problem, presumably due to the accumulative errors in their autoregressive decoding nature of time-series forecasting. Therefore, we propose MALS-Net, a Multi-Head Attention-based LSTM Sequence-to-Sequence model that makes use of the transformer’s mechanism without suffering from accumulative errors by utilizing an attention-based LSTM encoder-decoder architecture. The proposed model was then evaluated in BLVD, a more practical dataset without the overfitting issue of NGSIM. Compared to other relevant approaches, our model exhibits state-of-the-art performance for both short and long-term prediction. Full article
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18 pages, 1526 KiB  
Article
User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe
by Vera Pospelova, Inés López-Baldominos, Luis Fernández-Sanz, Ana Castillo-Martínez and Sanjay Misra
Sensors 2023, 23(1), 529; https://doi.org/10.3390/s23010529 - 3 Jan 2023
Cited by 3 | Viewed by 2703
Abstract
The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users’ support as an essential part of the social side of sustainability. IoT represents a disruption in the general scenario of [...] Read more.
The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users’ support as an essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals. The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for the implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., privacy, legal, IPR, and security issues. Our participation in several EU-funded projects with a focus on this area has enabled the collection of information on both sides of IoT sustainability through surveys but also by collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects of the sustainability of the Internet of Things in practice. Full article
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20 pages, 9638 KiB  
Article
Grass Cutting Robot for Inclined Surfaces in Hilly and Mountainous Areas
by Yuki Nishimura and Tomoyuki Yamaguchi
Sensors 2023, 23(1), 528; https://doi.org/10.3390/s23010528 - 3 Jan 2023
Cited by 2 | Viewed by 4080
Abstract
Grass cutting is necessary to prevent grass from diverting essential nutrients and water from crops. Usually, in hilly and mountainous areas, grass cutting is performed on steep slopes with an inclination angle of up to 60° (inclination gradient of 173%). However, such grass [...] Read more.
Grass cutting is necessary to prevent grass from diverting essential nutrients and water from crops. Usually, in hilly and mountainous areas, grass cutting is performed on steep slopes with an inclination angle of up to 60° (inclination gradient of 173%). However, such grass cutting tasks are dangerous owing to the unstable positioning of workers. For robots to perform these grass cutting tasks, slipping and falling must be prevented on inclined surfaces. In this study, a robot based on stable propeller control and four-wheel steering was developed to provide stable locomotion during grass cutting tasks. The robot was evaluated in terms of locomotion for different steering methods, straight motion on steep slopes, climbing ability, and coverage area. The results revealed that the robot was capable of navigating uneven terrains with steep slope angles. Moreover, no slipping actions that could have affected the grass cutting operations were observed. We confirmed that the proposed robot is able to cover 99.95% and 98.45% of an area on a rubber and grass slope, respectively. Finally, the robot was tested on different slopes with different angles in hilly and mountainous areas. The developed robot was able to perform the grass cutting task as expected. Full article
(This article belongs to the Special Issue Sensors and Robotic Systems for Agriculture Applications)
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35 pages, 1074 KiB  
Review
Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment
by Md. Mahadi Hasan, Muhammad Usama Islam, Muhammad Jafar Sadeq, Wai-Keung Fung and Jasim Uddin
Sensors 2023, 23(1), 527; https://doi.org/10.3390/s23010527 - 3 Jan 2023
Cited by 29 | Viewed by 6904
Abstract
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, [...] Read more.
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients’ mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster. Full article
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