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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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18 pages, 1111 KiB  
Article
Control Performance Requirements for Automated Driving Systems
by Trevor Vidano and Francis Assadian
Electronics 2024, 13(5), 902; https://doi.org/10.3390/electronics13050902 - 27 Feb 2024
Cited by 3 | Viewed by 1934
Abstract
This research investigates the development of risk-based performance requirements for the control of an automated driving system (ADS). The proposed method begins by determining the target level of safety for the virtual driver of an ADS. The underlying assumptions are informed by existing [...] Read more.
This research investigates the development of risk-based performance requirements for the control of an automated driving system (ADS). The proposed method begins by determining the target level of safety for the virtual driver of an ADS. The underlying assumptions are informed by existing data. Next, geometric models of the road and vehicle are used to derive deterministic performance levels of the virtual driver. To integrate the risk and performance requirements seamlessly, we propose new definitions for errors associated with the planner, pose, and control modules. These definitions facilitate the derivation of stochastic performance requirements for each module, thus ensuring an overall target level of safety. Notably, these definitions enable real-time controller performance monitoring, thus potentially enabling fault detection linked to the system’s overall safety target. At a high level, this approach argues that the requirements for the virtual driver’s modules should be designed simultaneously. To illustrate this approach, this technique is applied to a research project available in the literature that developed an automated steering system for an articulated bus. This example shows that the method generates achievable performance requirements that are verifiable through experimental testing and highlights the importance in validating the underlying assumptions for effective risk management. Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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14 pages, 1300 KiB  
Article
Hybrid FSO/RF Communications in Space–Air–Ground Integrated Networks: A Reduced Overhead Link Selection Policy
by Petros S. Bithas, Hector E. Nistazakis, Athanassios Katsis and Liang Yang
Electronics 2024, 13(4), 806; https://doi.org/10.3390/electronics13040806 - 19 Feb 2024
Cited by 8 | Viewed by 2404
Abstract
Space–air–ground integrated network (SAGIN) is considered an enabler for sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seems to be a quite promising solution to provide reliable connectivity everywhere and all the time. Its availability can be further enhanced [...] Read more.
Space–air–ground integrated network (SAGIN) is considered an enabler for sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seems to be a quite promising solution to provide reliable connectivity everywhere and all the time. Its availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high-altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors, and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity. Full article
(This article belongs to the Special Issue Emerging Innovative Technologies in 6G Telecommunication: Challenges and Solutions)
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26 pages, 761 KiB  
Article
A Hybrid Group Multi-Criteria Approach Based on SAW, TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems
by Constanta Zoie Radulescu and Marius Radulescu
Electronics 2024, 13(4), 789; https://doi.org/10.3390/electronics13040789 - 17 Feb 2024
Cited by 18 | Viewed by 2482
Abstract
The growth of Internet of Things (IoT) systems is driven by their potential to improve efficiency, enhance decision-making, and create new business opportunities across various domains. In this paper, the main selection problems in IoT-type systems, criteria used in multi-criteria evaluation, and multi-criteria [...] Read more.
The growth of Internet of Things (IoT) systems is driven by their potential to improve efficiency, enhance decision-making, and create new business opportunities across various domains. In this paper, the main selection problems in IoT-type systems, criteria used in multi-criteria evaluation, and multi-criteria methods used for solving IoT selection problems are identified. Then, a Hybrid Group Multi-Criteria Approach for solving selection problems in IoT-type systems is proposed. The approach contains the Best Worst Method (BWM) weighting method, multi-criteria Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Complex Proportional Assessment Method (COPRAS), and a method that combines the solutions obtained using the four considered multi-criteria methods to obtain a single solution. The SAW, TOPSIS, VIKOR, and COPRAS methods were analyzed in relation to their advantages, disadvantages, inputs, outputs, measurement scale, type of normalization, aggregation method, parameters, complexity of implementation, and interactivity. An application of the Hybrid Group Multi-Criteria Approach for IoT platform selection and a comparison between the SAW, TOPSIS, VIKOR, and COPRAS solutions and the solution of the proposed approach is realized. A Spearman correlation analysis is presented. Full article
(This article belongs to the Special Issue Advances in Decision Making for Complex Systems)
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34 pages, 3253 KiB  
Review
Review of Industry 4.0 from the Perspective of Automation and Supervision Systems: Definitions, Architectures and Recent Trends
by Francisco Javier Folgado, David Calderón, Isaías González and Antonio José Calderón
Electronics 2024, 13(4), 782; https://doi.org/10.3390/electronics13040782 - 16 Feb 2024
Cited by 68 | Viewed by 13239
Abstract
Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the [...] Read more.
Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the field of automation and supervision systems. In this paper, a review of the Industry 4.0 concept, with equivalent terms, enabling technologies and reference architectures for its implementation, is presented. It will be shown that this paradigm results from the confluence and integration of both existing and disruptive technologies. Furthermore, the most relevant trends in industrial automation and supervision systems are covered, highlighting the convergence of traditional equipment and those characterized by the Internet of Things (IoT). This paper is intended to serve as a reference document as well as a guide for the design and deployment of automation and supervision systems framed in Industry 4.0. Full article
(This article belongs to the Section Industrial Electronics)
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30 pages, 17457 KiB  
Article
Melanoma Skin Cancer Identification with Explainability Utilizing Mask Guided Technique
by Lahiru Gamage, Uditha Isuranga, Dulani Meedeniya, Senuri De Silva and Pratheepan Yogarajah
Electronics 2024, 13(4), 680; https://doi.org/10.3390/electronics13040680 - 6 Feb 2024
Cited by 26 | Viewed by 3993
Abstract
Melanoma is a highly prevalent and lethal form of skin cancer, which has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the identification of [...] Read more.
Melanoma is a highly prevalent and lethal form of skin cancer, which has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the identification of diseases using medical imaging. The paper introduces a computational model for classifying melanoma skin cancer images using convolutional neural networks (CNNs) and vision transformers (ViT) with the HAM10000 dataset. Both approaches utilize mask-guided techniques, employing a specialized U2-Net segmentation module to generate masks. The CNN-based approach utilizes ResNet50, VGG16, and Xception with transfer learning. The training process is enhanced using a Bayesian hyperparameter tuner. Moreover, this study applies gradient-weighted class activation mapping (Grad-CAM) and Grad-CAM++ to generate heatmaps to explain the classification models. These visual heatmaps elucidate the contribution of each input region to the classification outcome. The CNN-based model approach achieved the highest accuracy at 98.37% in the Xception model with a sensitivity and specificity of 95.92% and 99.01%, respectively. The ViT-based model approach achieved high values for accuracy, sensitivity, and specificity, such as 92.79%, 91.09%, and 93.54%, respectively. Furthermore, the performance of the model was assessed through intersection over union (IOU) and other qualitative evaluations. Finally, we developed the proposed model as a web application that can be used as a support tool for medical practitioners in real-time. The system usability study score of 86.87% is reported, which shows the usefulness of the proposed solution. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence, Machine Learning, Deep Learning, and Explainable AI (XAI))
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55 pages, 1876 KiB  
Review
A Survey on Video Streaming for Next-Generation Vehicular Networks
by Chenn-Jung Huang, Hao-Wen Cheng, Yi-Hung Lien and Mei-En Jian
Electronics 2024, 13(3), 649; https://doi.org/10.3390/electronics13030649 - 4 Feb 2024
Cited by 11 | Viewed by 4314
Abstract
As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In [...] Read more.
As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission. Full article
(This article belongs to the Special Issue Featured Review Papers in Electrical and Autonomous Vehicles)
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26 pages, 352 KiB  
Review
Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases
by Piotr Grzesik and Dariusz Mrozek
Electronics 2024, 13(3), 640; https://doi.org/10.3390/electronics13030640 - 3 Feb 2024
Cited by 27 | Viewed by 10570
Abstract
In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this [...] Read more.
In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this survey, we aim to focus on the combination of machine learning and the edge computing paradigm. The presented research commences with the topic of edge computing, its benefits, such as reduced data transmission, improved scalability, and reduced latency, as well as the challenges associated with this computing paradigm, like energy consumption, constrained devices, security, and device fleet management. It then presents the motivations behind the combination of machine learning and edge computing, such as the availability of more powerful edge devices, improving data privacy, reducing latency, or lowering reliance on centralized services. Then, it describes several edge computing platforms, with a focus on their capability to enable edge intelligence workflows. It also reviews the currently available edge intelligence frameworks and libraries, such as TensorFlow Lite or PyTorch Mobile. Afterward, the paper focuses on the existing use cases for edge intelligence in areas like industrial applications, healthcare applications, smart cities, environmental monitoring, or autonomous vehicles. Full article
(This article belongs to the Special Issue Towards Efficient and Reliable AI at the Edge)
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17 pages, 2087 KiB  
Article
Multi-Channel Graph Convolutional Networks for Graphs with Inconsistent Structures and Features
by Xinglong Chang, Jianrong Wang, Rui Wang, Tao Wang, Yingkui Wang and Weihao Li
Electronics 2024, 13(3), 607; https://doi.org/10.3390/electronics13030607 - 1 Feb 2024
Viewed by 2364
Abstract
Graph convolutional networks (GCNs) have attracted increasing attention in various fields due to their significant capacity to process graph-structured data. Typically, the GCN model and its variants heavily rely on the transmission of node features across the graph structure, which implicitly assumes that [...] Read more.
Graph convolutional networks (GCNs) have attracted increasing attention in various fields due to their significant capacity to process graph-structured data. Typically, the GCN model and its variants heavily rely on the transmission of node features across the graph structure, which implicitly assumes that the graph structure and node features are consistent, i.e., they carry related information. However, in many real-world networks, node features may unexpectedly mismatch with the structural information. Existing GCNs fail to generalize to inconsistent scenarios and are even outperformed by models that ignore the graph structure or node features. To address this problem, we investigate how to extract representations from both the graph structure and node features. Consequently, we propose the multi-channel graph convolutional network (MCGCN) for graphs with inconsistent structures and features. Specifically, the MCGCN encodes the graph structure and node features using two specific convolution channels to extract two separate specific representations. Additionally, two joint convolution channels are constructed to extract the common information shared by the graph structure and node features. Finally, an attention mechanism is utilized to adaptively learn the importance weights of these channels under the guidance of the node classification task. In this way, our model can handle both consistent and inconsistent scenarios. Extensive experiments on both synthetic and real-world datasets for node classification and recommendation tasks show that our methods, MCGCN-A and MCGCN-I, achieve the best performance on seven out of eight datasets and the second-best performance on the remaining dataset. For simpler graph structures or tasks where the overhead of multiple convolution channels is not justified, traditional single-channel GCN models might be more efficient. Full article
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21 pages, 7312 KiB  
Article
Cyber-Resilient Converter Control System for Doubly Fed Induction Generator-Based Wind Turbine Generators
by Nathan Farrar and Mohd. Hasan Ali
Electronics 2024, 13(3), 492; https://doi.org/10.3390/electronics13030492 - 24 Jan 2024
Cited by 4 | Viewed by 2110
Abstract
As wind turbine generator systems become more common in the modern power grid, the question of how to adequately protect them from cyber criminals has become a major theme in the development of new control systems. As such, artificial intelligence (AI) and machine [...] Read more.
As wind turbine generator systems become more common in the modern power grid, the question of how to adequately protect them from cyber criminals has become a major theme in the development of new control systems. As such, artificial intelligence (AI) and machine learning (ML) algorithms have become major contributors to preventing, detecting, and mitigating cyber-attacks in the power system. In their current state, wind turbine generator systems are woefully unprepared for a coordinated and sophisticated cyber attack. With the implementation of the internet-of-things (IoT) devices in the power control network, cyber risks have increased exponentially. The literature shows the impact analysis and exploring detection techniques for cyber attacks on the wind turbine generator systems; however, almost no work on the mitigation of the adverse effects of cyber attacks on the wind turbine control systems has been reported. To overcome these limitations, this paper proposes implementing an AI-based converter controller, i.e., a multi-agent deep deterministic policy gradient (DDPG) method that can mitigate any adverse effects that communication delays or bad data could have on a grid-connected doubly fed induction generator (DFIG)-based wind turbine generator or wind farm. The performance of the proposed DDPG controller has been compared with that of a variable proportional–integral (VPI) control-based mitigation method. The proposed technique has been simulated and validated utilizing the MATLAB/Simulink software, version R2023A, to demonstrate the effectiveness of the proposed method. Also, the performance of the proposed DDPG method is better than that of the VPI method in mitigating the adverse impacts of cyber attacks on wind generator systems, which is validated by the plots and the root mean square error table found in the results section. Full article
(This article belongs to the Special Issue Advances in Renewable Energy and Electricity Generation)
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15 pages, 4767 KiB  
Article
FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape
by Geeta Joshi, Aditi Jain, Shalini Reddy Araveeti, Sabina Adhikari, Harshit Garg and Mukund Bhandari
Electronics 2024, 13(3), 498; https://doi.org/10.3390/electronics13030498 - 24 Jan 2024
Cited by 105 | Viewed by 33234
Abstract
As artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML)-enabled medical devices are increasingly used in healthcare. In this study, we collected publicly available information on AI/ML-enabled medical devices approved by the FDA in the United States, as [...] Read more.
As artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML)-enabled medical devices are increasingly used in healthcare. In this study, we collected publicly available information on AI/ML-enabled medical devices approved by the FDA in the United States, as of the latest update on 19 October 2023. We performed comprehensive analysis of a total of 691 FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices and offer an in-depth analysis of clearance pathways, approval timeline, regulation type, medical specialty, decision type, recall history, etc. We found a significant surge in approvals since 2018, with clear dominance of the radiology specialty in the application of machine learning tools, attributed to the abundant data from routine clinical data. The study also reveals a reliance on the 510(k)-clearance pathway, emphasizing its basis on substantial equivalence and often bypassing the need for new clinical trials. Also, it notes an underrepresentation of pediatric-focused devices and trials, suggesting an opportunity for expansion in this demographic. Moreover, the geographical limitation of clinical trials, primarily within the United States, points to a need for more globally inclusive trials to encompass diverse patient demographics. This analysis not only maps the current landscape of AI/ML-enabled medical devices but also pinpoints trends, potential gaps, and areas for future exploration, clinical trial practices, and regulatory approaches. In conclusion, our analysis sheds light on the current state of FDA-approved AI/ML-enabled medical devices and prevailing trends, contributing to a wider comprehension. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)
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17 pages, 6140 KiB  
Article
Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks
by Stamatis Apeiranthitis, Paraskevi Zacharia, Avraam Chatzopoulos and Michail Papoutsidakis
Electronics 2024, 13(2), 460; https://doi.org/10.3390/electronics13020460 - 22 Jan 2024
Cited by 10 | Viewed by 4704
Abstract
All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in harsh environments with excess temperature, humidity, vibration, fatigue, and load. A breakdown or malfunction in one of these machineries can significantly impact a [...] Read more.
All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in harsh environments with excess temperature, humidity, vibration, fatigue, and load. A breakdown or malfunction in one of these machineries can significantly impact a vessel’s operation and safety and, consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This study presents the economic and technical benefits of predictive maintenance over traditional preventive maintenance and repair by replacement approaches in the maritime domain. By leveraging modern technology and artificial intelligence, we can analyze the operating conditions of machinery by obtaining measurements either from sensors permanently installed on the machinery or by utilizing portable measuring instruments. This facilitates the early identification of potential damage, thereby enabling efficient strategizing for future maintenance and repair endeavors. In this paper, we propose and develop a convolutional neural network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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20 pages, 10060 KiB  
Article
Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems
by Umer Farooq, Moses Ademola and Abdu Shaalan
Electronics 2024, 13(2), 438; https://doi.org/10.3390/electronics13020438 - 21 Jan 2024
Cited by 19 | Viewed by 4373
Abstract
In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses. Although conventional preventive maintenance mechanisms like time-based maintenance, routine inspections, [...] Read more.
In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses. Although conventional preventive maintenance mechanisms like time-based maintenance, routine inspections, and manual data analysis provide a certain level of fault prevention, they are often reactive, time-consuming, and imprecise. On the other hand, machine learning algorithms can detect anomalies early, process vast amounts of data, continuously improve in almost real time, and, in turn, significantly enhance the efficiency of modern industrial systems. In this work, we compare different machine learning and deep learning techniques to optimise the predictive maintenance of ball bearing systems, which, in turn, will reduce the downtime and improve the efficiency of current and future industrial systems. For this purpose, we evaluate and compare classification algorithms like Logistic Regression and Support Vector Machine, as well as ensemble algorithms like Random Forest and Extreme Gradient Boost. We also explore and evaluate long short-term memory, which is a type of recurrent neural network. We assess and compare these models in terms of their accuracy, precision, recall, F1 scores, and computation requirement. Our comparison results indicate that Extreme Gradient Boost gives the best trade-off in terms of overall performance and computation time. For a dataset of 2155 vibration signals, Extreme Gradient Boost gives an accuracy of 96.61% while requiring a training time of only 0.76 s. Moreover, among the techniques that give an accuracy greater than 80%, Extreme Gradient Boost also gives the best accuracy-to-computation-time ratio. Full article
(This article belongs to the Section Systems & Control Engineering)
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14 pages, 8860 KiB  
Article
An Effective Spherical NF/FF Transformation Suitable for Characterising an Antenna under Test in Presence of an Infinite Perfectly Conducting Ground Plane
by Flaminio Ferrara, Claudio Gennarelli, Rocco Guerriero and Giovanni Riccio
Electronics 2024, 13(2), 397; https://doi.org/10.3390/electronics13020397 - 18 Jan 2024
Viewed by 1280
Abstract
An effective near-field to far-field transformation using a reduced number of near-field measurements collected via a spherical scan over the upper hemisphere, due to the presence of a flat metallic ground, is devised in this paper. Such a transformation relies on the non-redundant [...] Read more.
An effective near-field to far-field transformation using a reduced number of near-field measurements collected via a spherical scan over the upper hemisphere, due to the presence of a flat metallic ground, is devised in this paper. Such a transformation relies on the non-redundant sampling representations of electromagnetic fields and exploits the image principle to properly account for the metallic ground, supposed to be of infinite extent and realised by perfectly conducting material. The sampling representation of the probe voltage over the upper hemisphere is developed by modelling the antenna under test and its image by a very adaptable convex surface, which is able to fit as much as possible the geometry of any kind of antenna, thus minimising the volumetric redundancy and, accordingly, the number of required samples as well as the measurement time. Then, the use of a two-dimensional optimal sampling interpolation algorithm allows the reconstruction of the voltage value at each sampling point of the spherical grid required by the classical near-field-to-far-field transformation developed by Hansen. Numerical examples proving the effectiveness of the developed sampling representation and related near-field-to-far-field transformation techniques are reported. Full article
(This article belongs to the Special Issue Feature Papers in Microwave and Wireless Communications Section)
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18 pages, 2413 KiB  
Article
A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies
by Ioannis A. Bartsiokas, Panagiotis K. Gkonis, Dimitra I. Kaklamani and Iakovos S. Venieris
Electronics 2024, 13(2), 390; https://doi.org/10.3390/electronics13020390 - 17 Jan 2024
Cited by 7 | Viewed by 1995
Abstract
Growing and diverse user needs, along with the need for continuous access with minimal delay in densely populated machine-type networks, have led to a significant overhaul of modern mobile communication systems. Within this realm, the integration of advanced physical layer techniques such as [...] Read more.
Growing and diverse user needs, along with the need for continuous access with minimal delay in densely populated machine-type networks, have led to a significant overhaul of modern mobile communication systems. Within this realm, the integration of advanced physical layer techniques such as relaying-assisted transmission in beyond fifth-generation (B5G) networks aims to not only enhance network performance but also extend coverage across multicellular orientations. However, in cellular environments, the increased interference levels and the complex channel representations introduce a notable rise in the computational complexity associated with radio resource management (RRM) tasks. Machine and deep learning (ML/DL) have been proposed as an efficient way to support the enhanced user demands in densely populated environments since ML/DL models can relax the traffic load that is associated with RRM tasks. There is, however, in these solutions the need for distributed execution of training tasks to accelerate the decision-making process in RRM tasks. For this purpose, federated learning (FL) schemes are considered a promising field of research for next-generation (NG) networks’ RRM. This paper proposes an FL approach to tackle the joint relay node (RN) selection and resource allocation problem subject to power management constraints when in B5G networks. The optimization objective of this approach is to jointly elevate energy (EE) and spectral efficiency (SE) levels. The performance of the proposed approach is evaluated for various relaying-assisted transmission topologies and through comparison with other state-of-the-art ones (both ML and non-ML). In particular, the total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to approximately 10–20% compared to a state-of-the-art centralized ML scheme. Moreover, achieved accuracy can be improved by up to 10% compared to state-of-the-art non-ML solutions, while training time is reduced by approximately 50%. Full article
(This article belongs to the Special Issue Feature Papers in Microwave and Wireless Communications Section)
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15 pages, 3885 KiB  
Article
A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles
by Keon Yun, Heesun Yun, Sangmin Lee, Jinhyeok Oh, Minchul Kim, Myongcheol Lim, Juntaek Lee, Chanmin Kim, Jiwon Seo and Jinyoung Choi
Electronics 2024, 13(2), 288; https://doi.org/10.3390/electronics13020288 - 8 Jan 2024
Cited by 12 | Viewed by 2922
Abstract
Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our [...] Read more.
Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our system, including the RSU, is designed to identify vehicles exhibiting abnormal driving. Abnormal driving can arise from various causes, such as communication delays, sensor errors, navigation system malfunctions, environmental challenges, and cybersecurity threats. We simulated exploring three primary scenarios of abnormal driving: sensor errors, overlapping vehicles, and counterflow driving. The applicability of machine learning algorithms for detecting these anomalies was evaluated. The Minisom algorithm, in particular, demonstrated high accuracy, recall, and precision in identifying sensor errors, vehicle overlaps, and counterflow situations. Notably, changes in the vehicle’s direction and its characteristics proved to be significant indicators in the Basic Safety Messages (BSM). We propose adding a new element called linePosition to BSM Part 2, enhancing our ability to promptly detect and address vehicle abnormalities. This addition underpins the technical capabilities of RSU systems equipped with edge computing, enabling real-time analysis of vehicle data and appropriate responsive measures. In this paper, we emphasize the effectiveness of machine learning in identifying and responding to the abnormal behavior of autonomous vehicles, offering new ways to enhance vehicle safety and facilitate smoother road traffic flow. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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18 pages, 2334 KiB  
Article
How to Design and Evaluate mHealth Apps? A Case Study of a Mobile Personal Health Record App
by Guyeop Kim, Dongwook Hwang, Jaehyun Park, Hyun K. Kim and Eui-Seok Hwang
Electronics 2024, 13(1), 213; https://doi.org/10.3390/electronics13010213 - 3 Jan 2024
Cited by 6 | Viewed by 4427
Abstract
The rapid growth of the mHealth market has led to the development of several tools to evaluate user experience. However, there is a lack of universal tools specifically designed for this emerging technology. This study was conducted with the aim of developing and [...] Read more.
The rapid growth of the mHealth market has led to the development of several tools to evaluate user experience. However, there is a lack of universal tools specifically designed for this emerging technology. This study was conducted with the aim of developing and verifying a user experience evaluation scale for mHealth apps based on factors proposed in previous research. The initial draft of the tool was created following a comprehensive review of existing questionnaires related to mHealth app evaluation. The validity of this scale was then tested through exploratory and confirmatory factor analysis. The results of the factor analysis led to the derivation of 16 items, which were conceptually mapped to five factors: ease of use and satisfaction, information architecture, usefulness, ease of information, and aesthetics. A case study was also conducted to improve mHealth apps concerning personal health records using this scale. In conclusion, the developed user experience evaluation scale for mHealth apps can provide comprehensive user feedback and contribute to the improvement of these apps. Full article
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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27 pages, 5536 KiB  
Article
Multi-Modal Contrastive Learning for LiDAR Point Cloud Rail-Obstacle Detection in Complex Weather
by Lu Wen, Yongliang Peng, Miao Lin, Nan Gan and Rongqing Tan
Electronics 2024, 13(1), 220; https://doi.org/10.3390/electronics13010220 - 3 Jan 2024
Cited by 12 | Viewed by 3432
Abstract
Obstacle intrusion is a serious threat to the safety of railway traffic. LiDAR point cloud 3D semantic segmentation (3DSS) provides a new method for unmanned rail-obstacle detection. However, the inevitable degradation of model performance occurs in complex weather and hinders its practical application. [...] Read more.
Obstacle intrusion is a serious threat to the safety of railway traffic. LiDAR point cloud 3D semantic segmentation (3DSS) provides a new method for unmanned rail-obstacle detection. However, the inevitable degradation of model performance occurs in complex weather and hinders its practical application. In this paper, a multi-modal contrastive learning (CL) strategy, named DHT-CL, is proposed to improve point cloud 3DSS in complex weather for rail-obstacle detection. DHT-CL is a camera and LiDAR sensor fusion strategy specifically designed for complex weather and obstacle detection tasks, without the need for image input during the inference stage. We first demonstrate how the sensor fusion method is more robust under rainy and snowy conditions, and then we design a Dual-Helix Transformer (DHT) to extract deeper cross-modal information through a neighborhood attention mechanism. Then, an obstacle anomaly-aware cross-modal discrimination loss is constructed for collaborative optimization that adapts to the anomaly identification task. Experimental results on a complex weather railway dataset show that with an mIoU of 87.38%, the proposed DHT-CL strategy achieves better performance compared to other high-performance models from the autonomous driving dataset, SemanticKITTI. The qualitative results show that DHT-CL achieves higher accuracy in clear weather and reduces false alarms in rainy and snowy weather. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Transportation Systems)
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15 pages, 5731 KiB  
Article
Design of a Ka-Band Heterogeneous Integrated T/R Module of Phased Array Antenna
by Qinghua Zeng, Zhengtian Chen, Mengyun He, Song Wang, Xiao Liu and Haitao Xu
Electronics 2024, 13(1), 204; https://doi.org/10.3390/electronics13010204 - 2 Jan 2024
Cited by 4 | Viewed by 2883
Abstract
The central element of a phased array antenna that performs beam electrical scanning, as well as signal transmission and reception, is the transceiver (T/R) module. Higher standards have been set for the integration, volume, power consumption, stability, and environmental adaptability of T/R modules [...] Read more.
The central element of a phased array antenna that performs beam electrical scanning, as well as signal transmission and reception, is the transceiver (T/R) module. Higher standards have been set for the integration, volume, power consumption, stability, and environmental adaptability of T/R modules due to the increased operating frequency of phased array antennas, the variability of application platforms, and the diversified development of system functions. Device-based multichannel T/R modules are the key to realizing low-profile Ka-band phased array antenna microsystem architecture. The design and implementation of a low-profile, high-performance, and highly integrated Ka-band phased array antenna T/R module are examined in this paper. Additionally, a dependable Ka-band four-channel T/R module based on Si/GaAs/Low Temperature Co-fired Ceramic (LTCC), applying multi-material heterogeneous integration architecture, is proposed and fabricated. The chip architecture, transceiver link, LTCC substrates, interconnect interface, and packaging are all taken into consideration when designing the T/R module. When compared to a standard phased array antenna, the module’s profile shrunk from 40 mm to 8 mm, and its overall dimensions are only 10.8 mm × 10 mm × 3 mm. It weighs 1 g, and with the same specs, the single channel volume was reduced by 95%. The T/R module has an output power of ≥26 dBm for single-channel transmission, an efficiency of ≥25%, and a noise factor of ≤4.4 dB. When compared to T/R modules based on System-on-Chip (SOC) devices, the RF performance has significantly improved, as seen by an increase in single channel output power and a decrease in the receiving noise factor. This work lays a foundation for the devitalization and engineering application of T/R modules in highly reliable application scenarios. Full article
(This article belongs to the Special Issue Recent Advances in Antennas and Millimeter-Wave Applications for Mobile Communication Systems)
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26 pages, 1235 KiB  
Article
Event-Triggered Adaptive Control for a Class of Nonlinear Systems with Dead-Zone Input
by Congli Mei, Dong Guo, Gang Chen, Jianping Cai and Jianning Li
Electronics 2024, 13(1), 210; https://doi.org/10.3390/electronics13010210 - 2 Jan 2024
Cited by 27 | Viewed by 1866
Abstract
In this paper, the event-triggered control problem is investigated using backstepping techniques for nonlinear systems with dead-zone input. The external disturbance and unknown parameters are also considered in the controller’s design. It is well known that errors in input signal measurements are inevitable. [...] Read more.
In this paper, the event-triggered control problem is investigated using backstepping techniques for nonlinear systems with dead-zone input. The external disturbance and unknown parameters are also considered in the controller’s design. It is well known that errors in input signal measurements are inevitable. In event-triggered control, such errors will directly affect whether the control signal is updated. This measurement error can be seen in the form of interference to the threshold. Therefore, unlike traditional event-triggered control, the existence of threshold disturbance is considered in the controller’s design. The proposed controller can not only compensate for the uncertainties caused by external disturbance and unknown parameters but can also suppress the unknown effects caused by threshold interference. In addition, to obtain a continuous controller, a smooth function is constructed to approximate the discontinuous sign function. In this way, Zeno behavior is successfully avoided. The boundedness of all signals and the tracking performance of the system can be guaranteed by the proposed control scheme. Numerical simulation and actual system simulation demonstrate the effectiveness of the proposed control scheme. The comparative simulation results also verify this event-triggered controller’s advantages, including better tracking performance and fewer trigger times. Full article
(This article belongs to the Section Systems & Control Engineering)
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15 pages, 2049 KiB  
Article
A Multimodal Late Fusion Framework for Physiological Sensor and Audio-Signal-Based Stress Detection: An Experimental Study and Public Dataset
by Vasileios-Rafail Xefteris, Monica Dominguez, Jens Grivolla, Athina Tsanousa, Francesco Zaffanela, Martina Monego, Spyridon Symeonidis, Sotiris Diplaris, Leo Wanner, Stefanos Vrochidis and Ioannis Kompatsiaris
Electronics 2023, 12(23), 4871; https://doi.org/10.3390/electronics12234871 - 2 Dec 2023
Cited by 6 | Viewed by 4054
Abstract
Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities [...] Read more.
Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time. Full article
(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)
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14 pages, 1680 KiB  
Article
AI to Train AI: Using ChatGPT to Improve the Accuracy of a Therapeutic Dialogue System
by Karolina Gabor-Siatkowska, Marcin Sowański, Rafał Rzatkiewicz, Izabela Stefaniak, Marek Kozłowski and Artur Janicki
Electronics 2023, 12(22), 4694; https://doi.org/10.3390/electronics12224694 - 18 Nov 2023
Cited by 9 | Viewed by 5641
Abstract
In this work, we present the use of one artificial intelligence (AI) application (ChatGPT) to train another AI-based application. As the latter one, we show a dialogue system named Terabot, which was used in the therapy of psychiatric patients. Our study was motivated [...] Read more.
In this work, we present the use of one artificial intelligence (AI) application (ChatGPT) to train another AI-based application. As the latter one, we show a dialogue system named Terabot, which was used in the therapy of psychiatric patients. Our study was motivated by the fact that for such a domain-specific system, it was difficult to acquire large real-life data samples to increase the training database: this would require recruiting more patients, which is both time-consuming and costly. To address this gap, we have employed a neural large language model: ChatGPT version 3.5, to generate data solely for training our dialogue system. During initial experiments, we identified intents that were most often misrecognized. Next, we fed ChatGPT with a series of prompts, which triggered the language model to generate numerous additional training entries, e.g., alternatives to the phrases that had been collected during initial experiments with healthy users. This way, we have enlarged the training dataset by 112%. In our case study, for testing, we used 2802 speech recordings originating from 32 psychiatric patients. As an evaluation metric, we used the accuracy of intent recognition. The speech samples were converted into text using automatic speech recognition (ASR). The analysis showed that the patients’ speech challenged the ASR module significantly, resulting in deteriorated speech recognition and, consequently, low accuracy of intent recognition. However, thanks to the augmentation of the training data with ChatGPT-generated data, the intent recognition accuracy increased by 13% relatively, reaching 86% in total. We also emulated the case of an error-free ASR and showed the impact of ASR misrecognitions on the intent recognition accuracy. Our study showcased the potential of using generative language models to develop other AI-based tools, such as dialogue systems. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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33 pages, 1227 KiB  
Review
A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing
by Rudolf Hoffmann and Christoph Reich
Electronics 2023, 12(22), 4572; https://doi.org/10.3390/electronics12224572 - 8 Nov 2023
Cited by 13 | Viewed by 11128
Abstract
Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) [...] Read more.
Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries. Full article
(This article belongs to the Special Issue Convolutional Neural Networks and Vision Applications, 3rd Edition)
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6 pages, 359 KiB  
Editorial
Wearable Electronic Systems Based on Smart Wireless Sensors for Multimodal Physiological Monitoring in Health Applications: Challenges, Opportunities, and Future Directions
by Cristiano De Marchis, Giovanni Crupi, Nicola Donato and Sergio Baldari
Electronics 2023, 12(20), 4284; https://doi.org/10.3390/electronics12204284 - 16 Oct 2023
Cited by 1 | Viewed by 2037
Abstract
Driven by the fast-expanding market, wearable technologies have rapidly evolved [...] Full article
(This article belongs to the Section Microwave and Wireless Communications)
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16 pages, 5443 KiB  
Article
Design of High-Gain and Low-Mutual-Coupling Multiple-Input–Multiple-Output Antennas Based on PRS for 28 GHz Applications
by Jinkyu Jung, Wahaj Abbas Awan, Domin Choi, Jaemin Lee, Niamat Hussain and Nam Kim
Electronics 2023, 12(20), 4286; https://doi.org/10.3390/electronics12204286 - 16 Oct 2023
Cited by 15 | Viewed by 2543
Abstract
In this paper, a high-gain and low-mutual-coupling four-port Multiple Input Multiple Output (MIMO) antenna based on a Partially Reflective Surface (PRS) for 28 GHz applications is proposed. The antenna radiator is a circular-shaped patch with a circular slot and a pair of vias [...] Read more.
In this paper, a high-gain and low-mutual-coupling four-port Multiple Input Multiple Output (MIMO) antenna based on a Partially Reflective Surface (PRS) for 28 GHz applications is proposed. The antenna radiator is a circular-shaped patch with a circular slot and a pair of vias to secure a wide bandwidth ranging from 24.29 GHz to 28.45 GHz (15.77%). The targeted band has been allocated for several countries such as Korea, Europe, the United States, China, and Japan. The optimized antenna offers a peak gain of 8.77 dBi at 24.29 GHz with a gain of 6.78 dBi. A novel PRS is designed and loaded on the antenna for broadband and high-gain characteristics. With the PRS, the antenna offers a wide bandwidth from 23.67 GHz to 29 GHz (21%), and the gain is improved up to 11.4 dBi, showing an overall increase of about 3 dBi. A 2 × 2 MIMO system is designed using the single-element antenna, which offers a bandwidth of 23.5 to 29 GHz (20%), and a maximum gain of 11.4 dBi. The MIMO antenna also exhibits a low mutual coupling of −35 dB along with a low Envelope Correlation Coefficient and Channel Capacity Loss, making it a suitable candidate for future compact-sized mmWave MIMO systems. Full article
(This article belongs to the Special Issue Recent Advances in Antennas and Millimeter-Wave Applications for Mobile Communication Systems)
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14 pages, 840 KiB  
Article
Reconfigurable Intelligent Surface-Assisted Millimeter Wave Networks: Cell Association and Coverage Analysis
by Donglai Zhao, Gang Wang, Jinlong Wang and Zhiquan Zhou
Electronics 2023, 12(20), 4270; https://doi.org/10.3390/electronics12204270 - 16 Oct 2023
Cited by 3 | Viewed by 2843
Abstract
Reconfigurable intelligent surface (RIS) is emerging as a promising technology to achieve coverage enhancement. This paper develops a tractable analytical framework based on stochastic geometry for performance analysis of RIS-assisted millimeter wave networks. Based on the framework, a two-step cell association criterion is [...] Read more.
Reconfigurable intelligent surface (RIS) is emerging as a promising technology to achieve coverage enhancement. This paper develops a tractable analytical framework based on stochastic geometry for performance analysis of RIS-assisted millimeter wave networks. Based on the framework, a two-step cell association criterion is proposed, and the analytical expressions of the user association probability and the coverage probability in general scenarios are derived. In addition, the closed-form expressions of the two performance metrics in special cases are also provided. The simulation results verify the accuracy of the theoretically derived analytical expressions, and reveal the superiority of deploying RISs in millimeter wave networks and the effectiveness of the proposed cell association scheme to improve coverage. Furthermore, the effects of the RIS parameters and the BS density on coverage performance are also investigated. Full article
(This article belongs to the Special Issue Applications, Trends and Development for 5G/6G and beyond Wireless Communication Systems)
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31 pages, 5489 KiB  
Article
Explicit Representation of Mechanical Functions for Maintenance Decision Support
by Mengchu Song, Ilmar F. Santos, Xinxin Zhang, Jing Wu and Morten Lind
Electronics 2023, 12(20), 4267; https://doi.org/10.3390/electronics12204267 - 15 Oct 2023
Cited by 1 | Viewed by 2361
Abstract
Artificial intelligence (AI) has been increasingly applied to condition-based maintenance (CBM), a knowledge-based method taking advantage of human expertise and other system knowledge that can serve as an alternative in cases in which machine learning is inapplicable due to a lack of training [...] Read more.
Artificial intelligence (AI) has been increasingly applied to condition-based maintenance (CBM), a knowledge-based method taking advantage of human expertise and other system knowledge that can serve as an alternative in cases in which machine learning is inapplicable due to a lack of training data. Functional information is seen as the most fundamental and important knowledge in maintenance decision making. This paper first proposes a mechanical functional modeling approach based on a functional modeling and reasoning methodology called multilevel flow modeling (MFM). The approach actually bridges the modeling gap between the mechanical level and the process level, which potentially extends the existing capability of MFM in rule-based diagnostics and prognostics from operation support to maintenance support. Based on this extension, a framework of optimized CBM is proposed, which can be used to diagnose potential mechanical failures from condition monitoring data and predict their future impacts in a qualitative way. More importantly, the framework uses MFM-based reliability-centered maintenance (RCM) to determine the importance of a detected potential failure, which can ensure the cost-effectiveness of CBM by adapting the maintenance requirements to specific operational contexts. This ability cannot be offered by existing CBM methods. An application to a mechanical test apparatus and hypothetical coupling with a process plant are used to demonstrate the proposed framework. Full article
(This article belongs to the Special Issue Advances in Immersive Technologies, Knowledge Representation, and AI for Human Centered Digital Experiences)
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26 pages, 2948 KiB  
Article
Real-Time AI-Driven Fall Detection Method for Occupational Health and Safety
by Anastasiya Danilenka, Piotr Sowiński, Kajetan Rachwał, Karolina Bogacka, Anna Dąbrowska, Monika Kobus, Krzysztof Baszczyński, Małgorzata Okrasa, Witold Olczak, Piotr Dymarski, Ignacio Lacalle, Maria Ganzha and Marcin Paprzycki
Electronics 2023, 12(20), 4257; https://doi.org/10.3390/electronics12204257 - 14 Oct 2023
Cited by 8 | Viewed by 4673
Abstract
Fall accidents in industrial and construction environments require an immediate reaction, to provide first aid. Shortening the time between the fall and the relevant personnel being notified can significantly improve the safety and health of workers. Therefore, in this work, an IoT system [...] Read more.
Fall accidents in industrial and construction environments require an immediate reaction, to provide first aid. Shortening the time between the fall and the relevant personnel being notified can significantly improve the safety and health of workers. Therefore, in this work, an IoT system for real-time fall detection is proposed, using the ASSIST-IoT reference architecture. Empowered with a machine learning model, the system can detect fall accidents and swiftly notify the occupational health and safety manager. To train the model, a novel multimodal fall detection dataset was collected from ten human participants and an anthropomorphic dummy, covering multiple types of fall, including falls from a height. The dataset includes absolute location and acceleration measurements from several IoT devices. Furthermore, a lightweight long short-term memory model is proposed for fall detection, capable of operating in an IoT environment with limited network bandwidth and hardware resources. The accuracy and F1-score of the model on the collected dataset were shown to exceed 0.95 and 0.9, respectively. The collected multimodal dataset was published under an open license, to facilitate future research on fall detection methods in occupational health and safety. Full article
(This article belongs to the Special Issue Artificial Intelligence Empowered Internet of Things)
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12 pages, 7248 KiB  
Article
Optimal Camera Placement to Generate 3D Reconstruction of a Mixed-Reality Human in Real Environments
by Juhwan Kim and Dongsik Jo
Electronics 2023, 12(20), 4244; https://doi.org/10.3390/electronics12204244 - 13 Oct 2023
Viewed by 3957
Abstract
Virtual reality and augmented reality are increasingly used for immersive engagement by utilizing information from real environments. In particular, three-dimensional model data, which is the basis for creating virtual places, can be manually developed using commercial modeling toolkits, but with the advancement of [...] Read more.
Virtual reality and augmented reality are increasingly used for immersive engagement by utilizing information from real environments. In particular, three-dimensional model data, which is the basis for creating virtual places, can be manually developed using commercial modeling toolkits, but with the advancement of sensing technology, computer vision technology can also be used to create virtual environments. Specifically, a 3D reconstruction approach can generate a single 3D model from image information obtained from various scenes in real environments using several cameras (multi-cameras). The goal is to generate a 3D model with excellent precision. However, the rules for choosing the optimal number of cameras and settings to capture information from in real environments (e.g., actual people) employing several cameras in unconventional positions are lacking. In this study, we propose an optimal camera placement strategy for acquiring high-quality 3D data using an irregular camera placement, essential for organizing image information while acquiring human data in a three-dimensional real space, using multiple irregular cameras in real environments. Our results show that installation costs can be lowered by arranging a minimum number of multi-camera cameras in an arbitrary space, and automated virtual human manufacturing with high accuracy can be conducted using optimal irregular camera location. Full article
(This article belongs to the Special Issue Perception and Interaction in Mixed, Augmented, and Virtual Reality)
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16 pages, 4373 KiB  
Article
Computer Vision Algorithms for 3D Object Recognition and Orientation: A Bibliometric Study
by Youssef Yahia, Júlio Castro Lopes and Rui Pedro Lopes
Electronics 2023, 12(20), 4218; https://doi.org/10.3390/electronics12204218 - 12 Oct 2023
Cited by 2 | Viewed by 2627
Abstract
This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main [...] Read more.
This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few. Full article
(This article belongs to the Special Issue Applications of Deep Learning Techniques)
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15 pages, 5638 KiB  
Article
Underwater Biomimetic Covert Acoustic Communications Mimicking Multiple Dolphin Whistles
by Yongcheol Kim, Hojun Lee, Seunghwan Seol, Bonggyu Park and Jaehak Chung
Electronics 2023, 12(19), 3999; https://doi.org/10.3390/electronics12193999 - 22 Sep 2023
Cited by 4 | Viewed by 1619
Abstract
This paper presents an underwater biomimetic covert acoustic communication system that achieves high covertness and a high data rate by mimicking dolphin group whistles. The proposed method uses combined time–frequency shift keying modulation with continuous varying carrier frequency modulation, which mitigates the interference [...] Read more.
This paper presents an underwater biomimetic covert acoustic communication system that achieves high covertness and a high data rate by mimicking dolphin group whistles. The proposed method uses combined time–frequency shift keying modulation with continuous varying carrier frequency modulation, which mitigates the interference between two overlapping multiple whistles while maintaining a high data rate. The data rate and bit error rate (BER) performance of the proposed method were compared with conventional underwater covert communication through an additive white Gaussian noise channel, a modeled underwater channel, and practical ocean experiments. For the covertness test, the similarity of the proposed multiple whistles was compared with the real dolphin group whistles using the mean opinion score test. As a result, the proposed method demonstrated a higher data rate, better BER performance, and large covertness to the real dolphin group whistles. Full article
(This article belongs to the Special Issue New Advances in Underwater Communication Systems)
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18 pages, 12864 KiB  
Article
A CMA-Based Electronically Reconfigurable Dual-Mode and Dual-Band Antenna
by Nicholas E. Russo, Constantinos L. Zekios and Stavros V. Georgakopoulos
Electronics 2023, 12(18), 3915; https://doi.org/10.3390/electronics12183915 - 17 Sep 2023
Cited by 2 | Viewed by 3405
Abstract
In this work, an electronically reconfigurable dual-band dual-mode microstrip ring antenna with high isolation is proposed. Using characteristic mode analysis (CMA), the physical characteristics of the ring antenna are revealed, and two modes are appropriately chosen for operation in two sub-6 GHz “legacy” [...] Read more.
In this work, an electronically reconfigurable dual-band dual-mode microstrip ring antenna with high isolation is proposed. Using characteristic mode analysis (CMA), the physical characteristics of the ring antenna are revealed, and two modes are appropriately chosen for operation in two sub-6 GHz “legacy” bands. Due to the inherent orthogonality of the characteristic modes, measured isolation larger than 37 dB was achieved in both bands without requiring complicated decoupling approaches. An integrated electronically reconfigurable matching network (comprising PIN diodes and varactors) was designed to switch between the two modes of operation. The simulated and measured results were in excellent agreement, showing a peak gain of 4.7 dB for both modes and radiation efficiency values of 44.3% and 64%, respectively. Using CMA to gain physical insights into the radiative orthogonal modes of under-researched and non-conventional antennas (e.g., antennas of arbitrary shapes) opens the door to developing highly compact radiators, which enable next-generation communication systems. Full article
(This article belongs to the Special Issue Recent Advances in Antenna Arrays and Millimeter-Wave Components)
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15 pages, 4402 KiB  
Article
DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n
by Qiang Liu, Wei Huang, Xiaoqiu Duan, Jianghao Wei, Tao Hu, Jie Yu and Jiahuan Huang
Electronics 2023, 12(18), 3892; https://doi.org/10.3390/electronics12183892 - 15 Sep 2023
Cited by 35 | Viewed by 4336
Abstract
Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. [...] Read more.
Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region’s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method. Full article
(This article belongs to the Special Issue Advances and Applications of Computer Vision in Electronics)
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21 pages, 7872 KiB  
Article
YOLO-Drone: An Optimized YOLOv8 Network for Tiny UAV Object Detection
by Xianxu Zhai, Zhihua Huang, Tao Li, Hanzheng Liu and Siyuan Wang
Electronics 2023, 12(17), 3664; https://doi.org/10.3390/electronics12173664 - 30 Aug 2023
Cited by 105 | Viewed by 23382
Abstract
With the widespread use of UAVs in commercial and industrial applications, UAV detection is receiving increasing attention in areas such as public safety. As a result, object detection techniques for UAVs are also developing rapidly. However, the small size of drones, complex airspace [...] Read more.
With the widespread use of UAVs in commercial and industrial applications, UAV detection is receiving increasing attention in areas such as public safety. As a result, object detection techniques for UAVs are also developing rapidly. However, the small size of drones, complex airspace backgrounds, and changing light conditions still pose significant challenges for research in this area. Based on the above problems, this paper proposes a tiny UAV detection method based on the optimized YOLOv8. First, in the detection head component, a high-resolution detection head is added to improve the device’s detection capability for small targets, while the large target detection head and redundant network layers are cut off to effectively reduce the number of network parameters and improve the detection speed of UAV; second, in the feature extraction stage, SPD-Conv is used to extract multi-scale features instead of Conv to reduce the loss of fine-grained information and enhance the model’s feature extraction capability for small targets. Finally, the GAM attention mechanism is introduced in the neck to enhance the model’s fusion of target features and improve the model’s overall performance in detecting UAVs. Relative to the baseline model, our method improves performance by 11.9%, 15.2%, and 9% in terms of P (precision), R (recall), and mAP (mean average precision), respectively. Meanwhile, it reduces the number of parameters and model size by 59.9% and 57.9%, respectively. In addition, our method demonstrates clear advantages in comparison experiments and self-built dataset experiments and is more suitable for engineering deployment and the practical applications of UAV object detection systems. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)
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19 pages, 4263 KiB  
Article
Integration of Wearables and Wireless Technologies to Improve the Interaction between Disabled Vulnerable Road Users and Self-Driving Cars
by Antonio Guerrero-Ibañez, Ismael Amezcua-Valdovinos and Juan Contreras-Castillo
Electronics 2023, 12(17), 3587; https://doi.org/10.3390/electronics12173587 - 25 Aug 2023
Cited by 4 | Viewed by 3056
Abstract
The auto industry is accelerating, and self-driving cars are becoming a reality. However, the acceptance of such cars will depend on their social and environmental integration into a road traffic ecosystem comprising vehicles, motorcycles, bicycles, and pedestrians. One of the most vulnerable groups [...] Read more.
The auto industry is accelerating, and self-driving cars are becoming a reality. However, the acceptance of such cars will depend on their social and environmental integration into a road traffic ecosystem comprising vehicles, motorcycles, bicycles, and pedestrians. One of the most vulnerable groups within the road ecosystem is pedestrians. Assistive technology focuses on ensuring functional independence for people with disabilities. However, little effort has been devoted to exploring possible interaction mechanisms between pedestrians with disabilities and self-driving cars. This paper analyzes how self-driving cars and disabled pedestrians should interact in a traffic ecosystem supported by wearable devices for pedestrians to feel safer and more comfortable. We define the concept of an Assistive Self-driving Car (ASC). We describe a set of procedures to identify people with disabilities using an IEEE 802.11p-based device and a group of messages to express the intentions of disabled pedestrians to self-driving cars. This interaction provides disabled pedestrians with increased safety and confidence in performing tasks such as crossing the street. Finally, we discuss strategies for alerting disabled pedestrians to potential hazards within the road ecosystem. Full article
(This article belongs to the Special Issue Advanced Research in Wireless and Wearable Technology and Applications)
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16 pages, 5254 KiB  
Article
A Deep Learning Framework for Adaptive Beamforming in Massive MIMO Millimeter Wave 5G Multicellular Networks
by Spyros Lavdas, Panagiotis K. Gkonis, Efthalia Tsaknaki, Lambros Sarakis, Panagiotis Trakadas and Konstantinos Papadopoulos
Electronics 2023, 12(17), 3555; https://doi.org/10.3390/electronics12173555 - 23 Aug 2023
Cited by 9 | Viewed by 4215
Abstract
The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according [...] Read more.
The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network has the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system-level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, the EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required with respect to the non-ML framework. Full article
(This article belongs to the Special Issue Recent Advances in Antenna Arrays and Millimeter-Wave Components)
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16 pages, 13408 KiB  
Article
A 220 GHz to 325 GHz Grounded Coplanar Waveguide Based Periodic Leaky-Wave Beam-Steering Antenna in Indium Phosphide Process
by Akanksha Bhutani, Marius Kretschmann, Joel Dittmer, Peng Lu, Andreas Stöhr and Thomas Zwick
Electronics 2023, 12(16), 3482; https://doi.org/10.3390/electronics12163482 - 17 Aug 2023
Cited by 7 | Viewed by 3402
Abstract
This paper presents a novel periodic grounded coplanar waveguide (GCPW) leaky-wave antenna implemented in an Indium Phosphide (InP) process. The antenna is designed to operate in the 220 GHz–325 GHz frequency range, with the goal of integrating it with an InP uni-traveling-carrier photodiode [...] Read more.
This paper presents a novel periodic grounded coplanar waveguide (GCPW) leaky-wave antenna implemented in an Indium Phosphide (InP) process. The antenna is designed to operate in the 220 GHz–325 GHz frequency range, with the goal of integrating it with an InP uni-traveling-carrier photodiode to realize a wireless transmitter module. Future wireless communication systems must deliver a high data rate to multiple users in different locations. Therefore, wireless transmitters need to have a broadband nature, high gain, and beam-steering capability. Leaky-wave antennas offer a simple and cost-effective way to achieve beam-steering by sweeping frequency in the THz range. In this paper, the first periodic GCPW leaky-wave antenna in the 220 GHz–325 GHz frequency range is demonstrated. The antenna design is based on a novel GCPW leaky-wave unit cell (UC) that incorporates mirrored L-slots in the lateral ground planes. These mirrored L-slots effectively mitigate the open stopband phenomenon of a periodic leaky-wave antenna. The leakage rate, phase constant, and Bloch impedance of the novel GCPW leaky-wave UC are analyzed using Floquet’s theory. After optimizing the UC, a periodic GCPW leaky-wave antenna is constructed by cascading 16 UCs. Electromagnetic simulation results of the leaky-wave antenna are compared with an ideal model derived from a single UC. The two design approaches show excellent agreement in terms of their reflection coefficient and beam-steering range. Therefore, the ideal model presented in this paper demonstrates, for the first time, a rapid method for developing periodic leaky-wave antennas. To validate the simulation results, probe-based antenna measurements are conducted, showing close agreement in terms of the reflection coefficient, peak antenna gain, beam-steering angle, and far-field radiation patterns. The periodic GCPW leaky-wave antenna presented in this paper exhibits a high gain of up to 13.5 dBi and a wide beam-steering range from −60° to 35° over the 220 GHz–325 GHz frequency range. Full article
(This article belongs to the Special Issue Advanced Antenna Technologies for B5G and 6G Applications)
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15 pages, 2160 KiB  
Article
Safe and Trustful AI for Closed-Loop Control Systems
by Julius Schöning and Hans-Jürgen Pfisterer
Electronics 2023, 12(16), 3489; https://doi.org/10.3390/electronics12163489 - 17 Aug 2023
Cited by 10 | Viewed by 5724
Abstract
In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with [...] Read more.
In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Engineering)
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26 pages, 1389 KiB  
Article
MAGNETO and DeepInsight: Extended Image Translation with Semantic Relationships for Classifying Attack Data with Machine Learning Models
by Aeryn Dunmore, Adam Dunning, Julian Jang-Jaccard, Fariza Sabrina and Jin Kwak
Electronics 2023, 12(16), 3463; https://doi.org/10.3390/electronics12163463 - 15 Aug 2023
Cited by 5 | Viewed by 3850
Abstract
The translation of traffic flow data into images for the purposes of classification in machine learning tasks has been extensively explored in recent years. However, the method of translation has a significant impact on the success of such attempts. In 2019, a method [...] Read more.
The translation of traffic flow data into images for the purposes of classification in machine learning tasks has been extensively explored in recent years. However, the method of translation has a significant impact on the success of such attempts. In 2019, a method called DeepInsight was developed to translate genetic information into images. It was then adopted in 2021 for the purpose of translating network traffic into images, allowing the retention of semantic data about the relationships between features, in a model called MAGNETO. In this paper, we explore and extend this research, using the MAGNETO algorithm on three new intrusion detection datasets—CICDDoS2019, 5G-NIDD, and BOT-IoT—and also extend this method into the realm of multiclass classification tasks using first a One versus Rest model, followed by a full multiclass classification task, using multiple new classifiers for comparison against the CNNs implemented by the original MAGNETO model. We have also undertaken comparative experiments on the original MAGNETO datasets, CICIDS17, KDD99, and UNSW-NB15, as well as a comparison for other state-of-the-art models using the NSL-KDD dataset. The results show that the MAGNETO algorithm and the DeepInsight translation method, without the use of data augmentation, offer a significant boost to accuracy when classifying network traffic data. Our research also shows the effectiveness of Decision Tree and Random Forest classifiers on this type of data. Further research into the potential for real-time execution is needed to explore the possibilities for extending this method of translation into real-world scenarios. Full article
(This article belongs to the Special Issue Application Research Using AI, IoT, HCI, and Big Data Technologies)
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23 pages, 4527 KiB  
Article
Self-Regulated Learning and Active Feedback of MOOC Learners Supported by the Intervention Strategy of a Learning Analytics System
by Ruth Cobos
Electronics 2023, 12(15), 3368; https://doi.org/10.3390/electronics12153368 - 7 Aug 2023
Cited by 8 | Viewed by 3341
Abstract
MOOCs offer great learning opportunities, but they also present several challenges for learners that hinder them from successfully completing MOOCs. To address these challenges, edX-LIMS (System for Learning Intervention and its Monitoring for edX MOOCs) was developed. It is a learning analytics system [...] Read more.
MOOCs offer great learning opportunities, but they also present several challenges for learners that hinder them from successfully completing MOOCs. To address these challenges, edX-LIMS (System for Learning Intervention and its Monitoring for edX MOOCs) was developed. It is a learning analytics system that supports an intervention strategy (based on learners’ interactions with the MOOC) to provide feedback to learners through web-based Learner Dashboards. Additionally, edX-LIMS provides a web-based Instructor Dashboard for instructors to monitor their learners. In this article, an enhanced version of the aforementioned system called edX-LIMS+ is presented. This upgrade introduces new services that enhance both the learners’ and instructors’ dashboards with a particular focus on self-regulated learning. Moreover, the system detects learners’ problems to guide them and assist instructors in better monitoring learners and providing necessary support. The results obtained from the use of this new version (through learners’ interactions and opinions about their dashboards) demonstrate that the feedback provided has been significantly improved, offering more valuable information to learners and enhancing their perception of both the dashboard and the intervention strategy supported by the system. Additionally, the majority of learners agreed with their detected problems, thereby enabling instructors to enhance interventions and support learners’ learning processes. Full article
(This article belongs to the Special Issue Continuing and Emerging Research Trends in Technology-Enhanced Learning (Invited papers from ICWL-2022 and SETE-2022 Joint International Conferences))
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18 pages, 513 KiB  
Article
Cascading and Ensemble Techniques in Deep Learning
by I. de Zarzà, J. de Curtò, Enrique Hernández-Orallo and Carlos T. Calafate
Electronics 2023, 12(15), 3354; https://doi.org/10.3390/electronics12153354 - 5 Aug 2023
Cited by 18 | Viewed by 8075
Abstract
In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions [...] Read more.
In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions into another set of NN. Our exploration starts from an initial preliminary study and extends to various ensemble techniques including bagging, stacking, and finally cascading. The cascading ensemble involves training a second layer of models on the predictions of the first. This cascading structure, combined with ensemble voting for the final prediction, aims to exploit the strengths of multiple models while mitigating their individual weaknesses. Our results demonstrate significant improvement in prediction accuracy, providing a compelling case for the potential utility of these techniques in healthcare applications, specifically for prediction of diabetes where we achieve compelling model accuracy of 91.5% on the test set on a particular challenging dataset, where we compare thoroughly against many other methodologies. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies and Applications)
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29 pages, 6436 KiB  
Article
Fish Monitoring from Low-Contrast Underwater Images
by Nikos Petrellis, Georgios Keramidas, Christos P. Antonopoulos and Nikolaos Voros
Electronics 2023, 12(15), 3338; https://doi.org/10.3390/electronics12153338 - 4 Aug 2023
Cited by 7 | Viewed by 3287
Abstract
A toolset supporting fish detection, orientation, tracking and especially morphological feature estimation with high speed and accuracy, is presented in this paper. It can be exploited in fish farms to automate everyday procedures including size measurement and optimal harvest time estimation, fish health [...] Read more.
A toolset supporting fish detection, orientation, tracking and especially morphological feature estimation with high speed and accuracy, is presented in this paper. It can be exploited in fish farms to automate everyday procedures including size measurement and optimal harvest time estimation, fish health assessment, quantification of feeding needs, etc. It can also be used in an open sea environment to monitor fish size, behavior and the population of various species. An efficient deep learning technique for fish detection is employed and adapted, while methods for fish tracking are also proposed. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height) and the localization of fish body parts of particular interest such as the eyes and gills. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low-contrast underwater images where the fish can be hardly distinguished from its background. Hardware and software acceleration techniques have been applied at the shape alignment process reducing the frame processing latency to less than 0.5 us on a general purpose computer and less than 16 ms on an embedded platform. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream. A large public dataset with low-resolution underwater videos and images has also been developed to test the proposed system under worst case conditions. Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence (AI)/Machine Learning (ML) and IoT in the Primary Sector)
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18 pages, 8039 KiB  
Article
A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
by Gianni Bosi, Antonio Raffo, Valeria Vadalà, Rocco Giofrè, Giovanni Crupi and Giorgio Vannini
Electronics 2023, 12(13), 2939; https://doi.org/10.3390/electronics12132939 - 4 Jul 2023
Cited by 7 | Viewed by 2816
Abstract
In this paper, we experimentally investigate the effects of degradation observed on 0.15-µm GaN HEMT devices when operating under realistic power amplifier conditions. The latter will be applied to the devices under test (DUT) by exploiting a low-frequency load-pull characterization technique that provides [...] Read more.
In this paper, we experimentally investigate the effects of degradation observed on 0.15-µm GaN HEMT devices when operating under realistic power amplifier conditions. The latter will be applied to the devices under test (DUT) by exploiting a low-frequency load-pull characterization technique that provides information consistent with RF operation, with the advantage of revealing electrical quantities not directly detectable at high frequency. Quantities such as the resistive gate current, play a fundamental role in the analysis of technology reliability. The experiments will be carried out on DUTs of the same periphery considering two different power amplifier operations: a saturated class-AB condition, that emphasizes the degradation effects produced by high temperatures due to power dissipation, and a class-E condition, that enhances the effects of high electric fields. The experiments will be carried out at 30 °C and 100 °C, and the results will be compared to evaluate how a specific RF condition can impact on the device degradation. Such a kind of comparison, to the authors’ knowledge, has never been carried out and represents the main novelty of the present study. Full article
(This article belongs to the Special Issue Emerging Intelligent and Green Communications: Systems, Devices and Applications)
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15 pages, 1940 KiB  
Article
Predicting High-Frequency Stock Movement with Differential Transformer Neural Network
by Shijie Lai, Mingxian Wang, Shengjie Zhao and Gonzalo R. Arce
Electronics 2023, 12(13), 2943; https://doi.org/10.3390/electronics12132943 - 4 Jul 2023
Cited by 8 | Viewed by 6672
Abstract
Predicting stock prices has long been the holy grail for providing guidance to investors. Extracting effective information from Limit Order Books (LOBs) is a key point in high-frequency trading based on stock-movement forecasting. LOBs offer many details, but at the same time, they [...] Read more.
Predicting stock prices has long been the holy grail for providing guidance to investors. Extracting effective information from Limit Order Books (LOBs) is a key point in high-frequency trading based on stock-movement forecasting. LOBs offer many details, but at the same time, they are very noisy. This paper proposes a differential transformer neural network model, dubbed DTNN, to predict stock movement according to LOB data. The model utilizes a temporal attention-augmented bilinear layer (TABL) and a temporal convolutional network (TCN) to denoise the data. In addition, a prediction transformer module captures the dependency between time series. A differential layer is proposed and incorporated into the model to extract information from the messy and chaotic high-frequency LOB time series. This layer can identify the fine distinction between adjacent slices in the series. We evaluate the proposed model on several datasets. On the open LOB benchmark FI-2010, our model outperforms other comparative state-of-the-art methods in accuracy and F1 score. In the experiments using actual stock data, our model also shows great stock-movement forecasting capability and generalization performance. Full article
(This article belongs to the Section Artificial Intelligence)
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20 pages, 921 KiB  
Article
Engaging Learners in Educational Robotics: Uncovering Students’ Expectations for an Ideal Robotic Platform
by Georgios Kyprianou, Alexandra Karousou, Nikolaos Makris, Ilias Sarafis, Angelos Amanatiadis and Savvas A. Chatzichristofis
Electronics 2023, 12(13), 2865; https://doi.org/10.3390/electronics12132865 - 28 Jun 2023
Cited by 8 | Viewed by 5151
Abstract
Extensive research has been conducted on educational robotics (ER) platforms to explore their usage across different educational levels and assess their effectiveness in achieving desired learning outcomes. However, the existing literature has a limitation in regard to addressing learners’ specific preferences and characteristics [...] Read more.
Extensive research has been conducted on educational robotics (ER) platforms to explore their usage across different educational levels and assess their effectiveness in achieving desired learning outcomes. However, the existing literature has a limitation in regard to addressing learners’ specific preferences and characteristics regarding these platforms. To address this gap, it is crucial to encourage learners’ active participation in the design process of robotic platforms. By incorporating their valuable feedback and preferences and providing them with platforms that align with their interests, we can create a motivating environment that leads to increased engagement in science, technology, engineering and mathematics (STEM) courses and improved learning outcomes. Furthermore, this approach fosters a sense of absorption and full engagement among peers as they collaborate on assigned activities. To bridge the existing research gap, our study aimed to investigate the current trends in the morphology of educational robotics platforms. We surveyed students from multiple schools in Greece who had no prior exposure to robotic platforms. Our study aimed to understand students’ expectations of an ideal robotic companion. We examined the desired characteristics, modes of interaction, and socialization that students anticipate from such a companion. By uncovering these attributes and standards, we aimed to inform the development of an optimal model that effectively fulfills students’ educational aspirations while keeping them motivated and engaged. Full article
(This article belongs to the Special Issue Recent Advances in Educational Robotics, Volume II)
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22 pages, 671 KiB  
Article
LLM-Informed Multi-Armed Bandit Strategies for Non-Stationary Environments
by J. de Curtò, I. de Zarzà, Gemma Roig, Juan Carlos Cano, Pietro Manzoni and Carlos T. Calafate
Electronics 2023, 12(13), 2814; https://doi.org/10.3390/electronics12132814 - 25 Jun 2023
Cited by 19 | Viewed by 8967
Abstract
In this paper, we introduce an innovative approach to handling the multi-armed bandit (MAB) problem in non-stationary environments, harnessing the predictive power of large language models (LLMs). With the realization that traditional bandit strategies, including epsilon-greedy and upper confidence bound (UCB), may struggle [...] Read more.
In this paper, we introduce an innovative approach to handling the multi-armed bandit (MAB) problem in non-stationary environments, harnessing the predictive power of large language models (LLMs). With the realization that traditional bandit strategies, including epsilon-greedy and upper confidence bound (UCB), may struggle in the face of dynamic changes, we propose a strategy informed by LLMs that offers dynamic guidance on exploration versus exploitation, contingent on the current state of the bandits. We bring forward a new non-stationary bandit model with fluctuating reward distributions and illustrate how LLMs can be employed to guide the choice of bandit amid this variability. Experimental outcomes illustrate the potential of our LLM-informed strategy, demonstrating its adaptability to the fluctuating nature of the bandit problem, while maintaining competitive performance against conventional strategies. This study provides key insights into the capabilities of LLMs in enhancing decision-making processes in dynamic and uncertain scenarios. Full article
(This article belongs to the Special Issue Zero-Shot Learning in Natural Language Processing and Its Applications)
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12 pages, 1986 KiB  
Communication
Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach
by Sajjad Ahmad Khan, Hyun Jun Lee and Huhnkuk Lim
Electronics 2023, 12(13), 2768; https://doi.org/10.3390/electronics12132768 - 21 Jun 2023
Cited by 15 | Viewed by 11594
Abstract
Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid [...] Read more.
Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid approach that incorporates the features of two state-of-the-art object detection models: You Only Look Once (YOLO) and Faster Region CNN (Faster R-CNN). The proposed hybrid approach combines the detection and boundary box selection capabilities of YOLO with the region of interest (RoI) pooling from Faster R-CNN, resulting in improved segmentation and classification accuracy. Furthermore, we skip the Region Proposal Network (RPN) from the Faster R-CNN architecture to optimize processing time. The hybrid model is trained on a local dataset of 10,000 labeled traffic images collected during driving scenarios, further enhancing its accuracy. The results demonstrate that our proposed hybrid approach outperforms existing state-of-the-art models, providing both high accuracy and practical real-time object detection for autonomous vehicles. It is observed that the proposed hybrid model achieves a significant increase in accuracy, with improvements ranging from 5 to 7 percent compared to the standalone YOLO models. The findings of this research have practical implications for the integration of AI technologies in autonomous driving systems. Full article
(This article belongs to the Section Artificial Intelligence)
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18 pages, 15432 KiB  
Article
An AR Application for the Efficient Construction of Water Pipes Buried Underground
by Koki Inoue, Shuichiro Ogake, Kazuma Kobayashi, Toyoaki Tomura, Satoshi Mitsui, Toshifumi Satake and Naoki Igo
Electronics 2023, 12(12), 2634; https://doi.org/10.3390/electronics12122634 - 12 Jun 2023
Cited by 1 | Viewed by 2561
Abstract
Unlike other civil engineering works, water pipe works require digging out before construction because the construction site is buried. The AR application is a system that displays buried objects in the ground in three dimensions when users hold a device such as a [...] Read more.
Unlike other civil engineering works, water pipe works require digging out before construction because the construction site is buried. The AR application is a system that displays buried objects in the ground in three dimensions when users hold a device such as a smartphone over the ground, using images from the smartphone. The system also registers new buried objects when they are updated. The target of this project is water pipes, which are the most familiar of all buried structures. The system has the following functions: “registration and display of new water pipe information” and “acquisition and display of current location coordinate information.” By applying the plane detection function to data acquired from a camera mounted on a smartphone, the system can easily register and display a water pipe model horizontally to the ground. The system does not require a reference marker because it uses GPS and the plane detection function. In the future, the system will support the visualization and registration of not only water pipes but also other underground infrastructures and will play an active role in the rapid restoration of infrastructure after a large-scale disaster through the realization of a buried-object 3D MAP platform. Full article
(This article belongs to the Special Issue Science and Technology of Advanced Electronics, Sensing Systems and AI Applied to Society: Including Collections from the Latest Papers of KRIS 2023)
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19 pages, 1251 KiB  
Review
Immersive Virtual Reality Enabled Interventions for Autism Spectrum Disorder: A Systematic Review and Meta-Analysis
by Chen Li, Meike Belter, Jing Liu and Heide Lukosch
Electronics 2023, 12(11), 2497; https://doi.org/10.3390/electronics12112497 - 1 Jun 2023
Cited by 11 | Viewed by 6638
Abstract
Autism spectrum disorder (ASD) is characterized by persistent deficits in social communication and interaction, which can have significant impacts on daily life, education, and work. Limited performance in learning and working, as well as exclusion from social activities, are common challenges faced by [...] Read more.
Autism spectrum disorder (ASD) is characterized by persistent deficits in social communication and interaction, which can have significant impacts on daily life, education, and work. Limited performance in learning and working, as well as exclusion from social activities, are common challenges faced by individuals with ASD. Virtual reality (VR) technology has emerged as a promising medium for delivering interventions for ASD. To address five major research questions and understand the latest trends and challenges in this area, a systematic review of 21 journal articles published between 1 January 2010 and 31 December 2022 was conducted using the PRISMA approach. A meta-analysis of 15 articles was further conducted to assess interventional effectiveness. The results showed that most studies focused on social and affective skill training and relied on existing theories and practices with limited adaptations for VR. Furthermore, the enabling technologies’ affordances for the interventional needs of individuals with ASD were not thoroughly investigated. We suggest that future studies should propose and design interventions with solid theoretical foundations, explore more interventional areas besides social and affective skill training, and employ more rigorous experimental designs to investigate the effectiveness of VR-enabled ASD interventions. Full article
(This article belongs to the Topic Technology-Mediated Agile Blended Learning)
(This article belongs to the Section Artificial Intelligence)
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14 pages, 2173 KiB  
Article
Improving Norwegian Translation of Bicycle Terminology Using Custom Named-Entity Recognition and Neural Machine Translation
by Daniel Hellebust and Isah A. Lawal
Electronics 2023, 12(10), 2334; https://doi.org/10.3390/electronics12102334 - 22 May 2023
Viewed by 3660
Abstract
The Norwegian business-to-business (B2B) market for bicycles consists mainly of international brands, such as Shimano, Trek, Cannondale, and Specialized. The product descriptions for these brands are usually in English and need local translation. However, these product descriptions include bicycle-specific terminologies that are challenging [...] Read more.
The Norwegian business-to-business (B2B) market for bicycles consists mainly of international brands, such as Shimano, Trek, Cannondale, and Specialized. The product descriptions for these brands are usually in English and need local translation. However, these product descriptions include bicycle-specific terminologies that are challenging for online translators, such as Google. For this reason, local companies outsource translation or translate product descriptions manually, which is cumbersome. In light of the Norwegian B2B bicycle industry, this paper explores transfer learning to improve the machine translation of bicycle-specific terminology from English to Norwegian, including generic text. Firstly, we trained a custom Named-Entity Recognition (NER) model to identify cycling-specific terminology and then adapted a MarianMT neural machine translation model for the translation process. Due to the lack of publicly available bicycle-terminology-related datasets to train the proposed models, we created our dataset by collecting a corpus of cycling-related texts. We evaluated the performance of our proposed model and compared its performance with that of Google Translate. Our model outperformed Google Translate on the test set, with a SacreBleu score of 45.099 against 36.615 for Google Translate on average. We also created a web application where the user can input English text with related bicycle terminologies, and it will return the detected cycling-specific words in addition to a Norwegian translation. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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14 pages, 1644 KiB  
Article
Deep Learning-Based Context-Aware Recommender System Considering Change in Preference
by Soo-Yeon Jeong and Young-Kuk Kim
Electronics 2023, 12(10), 2337; https://doi.org/10.3390/electronics12102337 - 22 May 2023
Cited by 6 | Viewed by 3888
Abstract
In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems [...] Read more.
In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods. Full article
(This article belongs to the Special Issue Application Research Using AI, IoT, HCI, and Big Data Technologies)
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