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Volume 13
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Electronics, Volume 13, Issue 13 (July-1 2024) – 254 articles

Cover Story (view full-size image): Future beyond-5G and 6G communication systems require a higher quality of service along with meeting multiple objectives. This paper delves into the concepts that form the basis for the design of two potential future mMIMO networks: cell-free and radio stripe systems. Their key aspects are addressed, including details of the channel estimation, uplink and downlink transmission and reception phases. The optimization concepts that underpin resource allocation algorithms are also explored, specifically those applied in power allocation and access point selection schemes. This serves as a foundation for addressing the challenges of achieving the conflicting major key performance indicators, such as enhancements on spectral efficiency, power efficiency, computational complexity or load balance. View this paper
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15 pages, 2207 KiB  
Article
Supraharmonics Reconstruction Method Based on Blackman Window and Compressed Sensing
by Fei Zhong, Xiao Zhang, Yangyang Zhu, Lining Guan, Zhihong Jiang and Zhe Chen
Electronics 2024, 13(13), 2679; https://doi.org/10.3390/electronics13132679 - 8 Jul 2024
Cited by 4 | Viewed by 1103
Abstract
This paper proposes a method that combines window functions with compressed sensing for the detection of ultra-high harmonics in the frequency range of 2–150 kHz. By analyzing the sparsity of the signal, the Bruckmann window function, which is most suitable for the compressed [...] Read more.
This paper proposes a method that combines window functions with compressed sensing for the detection of ultra-high harmonics in the frequency range of 2–150 kHz. By analyzing the sparsity of the signal, the Bruckmann window function, which is most suitable for the compressed sensing reconstruction process and the characteristics of ultra-high harmonics, is selected. Simulations indicate that, compared to existing methods, the proposed algorithm based on the fusion of the Bruckmann window and compressed sensing achieves a sparser post-observation signal with reduced fluctuations. The robustness and anti-interference capabilities are enhanced, while the harmonic detection accuracy and signal reconstruction performance are significantly improved. The reconstruction error reaches 4.15 × 10−6, the mean squared error percentage (MSE) reaches 4.13 × 10−6, and the signal-to-noise ratio (SNR) is as high as 97.69 dB, marking an increase of 54.11%. This study provides a new theoretical and methodological approach for the analysis and processing of ultra-high harmonics caused by a high proportion of power electronic devices. Full article
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18 pages, 3741 KiB  
Article
Adaptive Speed Control Scheme Based on Congestion Level and Inter-Vehicle Distance
by Jicheng Yin and Seung-Hoon Hwang
Electronics 2024, 13(13), 2678; https://doi.org/10.3390/electronics13132678 - 8 Jul 2024
Cited by 2 | Viewed by 1033
Abstract
Cellular vehicle-to-everything (C-V2X) enables short-distance communication between vehicles and other users to improve road safety through data sharing. Conventional research on C-V2X typically assumes that vehicles travel at the same speed with a fixed inter-vehicle distance ( [...] Read more.
Cellular vehicle-to-everything (C-V2X) enables short-distance communication between vehicles and other users to improve road safety through data sharing. Conventional research on C-V2X typically assumes that vehicles travel at the same speed with a fixed inter-vehicle distance (Disinter). However, this assumption does not reflect the real driving environment or promote road traffic efficiency. Conversely, assigning different speeds to vehicles without a structured approach inevitably increases the collision risk. Therefore, determining appropriate speeds for each vehicle in the C-V2X framework is crucial. To this end, considering the road environment and mobility, this study introduces an adaptive speed mechanism based on the congestion level of a zone and Disinter. First, the given scenario is divided into several zones. Subsequently, based on the congestion level of a zone and the Disinter level, an adaptive speed is defined for each vehicle. This approach ensured that vehicles adopt lower speeds in congested situations to reduce the collision probability and higher speeds in sparse traffic cases to improve traffic efficiency. The performance of the proposed adaptive speed scheme is compared with that of conventional fixed-speed settings. The results show that the adaptive speed control scheme outperforms conventional fixed-speed schemes in terms of the packet reception ratio (PRR) and collision ratio (CR). Specifically, the proposed mechanism can reduce the CR to 0 and ensure that the PRR is higher than 0.98 in low-density scenarios. Full article
(This article belongs to the Special Issue Emerging Innovative Technologies in 6G Telecommunication: Challenges and Solutions)
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19 pages, 15069 KiB  
Article
Enhanced Remote Sensing Image Compression Method Using Large Network with Sparse Extracting Strategy
by Hui Li, Tianpeng Pan and Lili Zhang
Electronics 2024, 13(13), 2677; https://doi.org/10.3390/electronics13132677 - 8 Jul 2024
Cited by 1 | Viewed by 1053
Abstract
Deep neural networks based on hyper-encoders play a critical role in estimating prior distributions in remote sensing image compression issues. However, most of the existing encoding methods suffer from a problem on the hyper-encoding side, namely the mismatch of extraction ability with the [...] Read more.
Deep neural networks based on hyper-encoders play a critical role in estimating prior distributions in remote sensing image compression issues. However, most of the existing encoding methods suffer from a problem on the hyper-encoding side, namely the mismatch of extraction ability with the encoder. This ability bias results in likelihood features that fail to extract sufficient information from latent representations. To solve this problem, the feature extraction capabilities of the hyper-encoder are enhanced to better estimate the Gaussian likelihood of the latent representation in end-to-end network optimization. Specifically, residual blocks and a parameter estimation module are incorporated to balance the performance of the encoder and the hyper-encoder. Furthermore, it is observed that the well-trained compression model tends to generate a fixed pattern of latent representations. Therefore, we incorporate a nonlocal cross-channel graph (NCG) on the backside of the encoder. Specifically, it aggregates features between similar latent representations in a graphical manner to further enhance the side information extraction capability of the hyper-encoder. Considering the computational cost, a sparse graph strategy is further developed to dynamically select the most relevant latent representations for aggregation operations, which greatly reduces the computational effort. The proposed algorithm is named nonlocal cross-channel efficient graph (NCEG). A long-dependent residual network is selected as the backbone, and a sparse attention module is inserted into the encoder/decoder side to enhance the perceptual field of the network. The experimental results on two evaluation datasets demonstrate that the proposed method achieves satisfactory results compared to other learning-based methods. Full article
(This article belongs to the Special Issue Image and Video Processing Based on Deep Learning)
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13 pages, 18304 KiB  
Article
A High-Performance and Ultra-Low-Power Accelerator Design for Advanced Deep Learning Algorithms on an FPGA
by Achyuth Gundrapally, Yatrik Ashish Shah, Nader Alnatsheh and Kyuwon Ken Choi
Electronics 2024, 13(13), 2676; https://doi.org/10.3390/electronics13132676 - 8 Jul 2024
Cited by 3 | Viewed by 3915
Abstract
This article addresses the growing need in resource-constrained edge computing scenarios for energy-efficient convolutional neural network (CNN) accelerators on mobile Field-Programmable Gate Array (FPGA) systems. In particular, we concentrate on register transfer level (RTL) design flow optimization to improve programming speed and power [...] Read more.
This article addresses the growing need in resource-constrained edge computing scenarios for energy-efficient convolutional neural network (CNN) accelerators on mobile Field-Programmable Gate Array (FPGA) systems. In particular, we concentrate on register transfer level (RTL) design flow optimization to improve programming speed and power efficiency. We present a re-configurable accelerator design optimized for CNN-based object-detection applications, especially suitable for mobile FPGA platforms like the Xilinx PYNQ-Z2. By not only optimizing the MAC module using Enhanced clock gating (ECG), the accelerator can also use low-power techniques such as Local explicit clock gating (LECG) and Local explicit clock enable (LECE) in memory modules to efficiently minimize data access and memory utilization. The evaluation using ResNet-20 trained on the CIFAR-10 dataset demonstrated significant improvements in power efficiency consumption (up to 22%) and performance. The findings highlight the importance of using different optimization techniques across multiple hardware modules to achieve better results in real-world applications. Full article
(This article belongs to the Section Microelectronics)
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18 pages, 15489 KiB  
Article
Ultra-Broadband Minuscule Polarization Beam Splitter Based on Dual-Core Photonic Crystal Fiber with Two Silver Wires
by Yuxiang Ji, Hui Zou, Yuhang Du and Ningyi Wang
Electronics 2024, 13(13), 2675; https://doi.org/10.3390/electronics13132675 - 8 Jul 2024
Viewed by 1247
Abstract
This paper presents a polarizing beam splitter (PBS) based on a hexagonal lattice silver-filled photonic crystal fiber (PCF) with two silver wires, which possesses advantages such as a short splitting length, high extinction ratio (ER), and an ultra-wide bandwidth in commonly used communication [...] Read more.
This paper presents a polarizing beam splitter (PBS) based on a hexagonal lattice silver-filled photonic crystal fiber (PCF) with two silver wires, which possesses advantages such as a short splitting length, high extinction ratio (ER), and an ultra-wide bandwidth in commonly used communication bands. Utilizing the full-vector finite element method (FV-FEM), thorough investigations were conducted on lasers within the wavelength range of 1.1 to 1.9 μm. The PBS demonstrates a working bandwidth of 725 nm (1.14 to 1.865 μm) under an ultra-short splitting length of 55.3 μm, with an ER exceeding 20 dB, covering all bands of O + E + S + C + L + U optical communication, and achieving a maximum ER of 74.65 dB, where the surface plasmon resonance (SPR) effect of silver metal plays a significant role. It not only features an ultra-short splitting length and an ultra-wide splitting bandwidth but also exhibits excellent manufacturing tolerances and anti-interference capabilities. This polarizing beam splitter represents a promising candidate in communication and may find various applications in optical communication. Full article
(This article belongs to the Special Issue Advances in Optical Fibers for Fiber Sensors)
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16 pages, 7340 KiB  
Article
Software-Defined Virtual Private Network for SD-WAN
by Chunle Fu, Bailing Wang, Hongri Liu and Wei Wang
Electronics 2024, 13(13), 2674; https://doi.org/10.3390/electronics13132674 - 8 Jul 2024
Cited by 1 | Viewed by 2522
Abstract
Software-Defined Wide Area Networks (SD-WANs) are an emerging Software-Defined Network (SDN) technology to reinvent Wide Area Networks (WANs) for ubiquitous network interconnections in cloud computing, edge computing, and the Internet of Everything. The state-of-the-art overlay-based SD-WANs are simply conjunctions of Virtual Private Network [...] Read more.
Software-Defined Wide Area Networks (SD-WANs) are an emerging Software-Defined Network (SDN) technology to reinvent Wide Area Networks (WANs) for ubiquitous network interconnections in cloud computing, edge computing, and the Internet of Everything. The state-of-the-art overlay-based SD-WANs are simply conjunctions of Virtual Private Network (VPN) and SDN architecture to leverage the controllability and programmability of SDN, which are only applicable for specific platforms and do not comply with the extensibility of SDN. This paper motivates us to refactor traditional VPNs with SDN architecture by proposing an overlay-based SD-WAN solution named Software-Defined Virtual Private Network (SD-VPN). An SDN-based auto-constructed VPN model and its evaluating metrics are put forward to automatically construct overlay WANs by node placement and service orchestration of SD-VPN. Therefore, a joint placement algorithm of VPN nodes and algorithms for overlay WAN service loading and offloading are proposed for SD-VPN controllers. Finally, a three-layer SD-VPN system is implemented and deployed in actual network environments. Simulation experiments and system tests are conducted to prove the high-efficiency controllability, real-time programmability, and auto-constructed deployability of the proposed SD-VPN. Performance trade-off between SD-VPN control channels and data channels is evaluated, and SD-VPN controllers are proven to be extensible for other VPN protocols and advanced services. Full article
(This article belongs to the Special Issue Design and Implementation of Advanced Security Solutions for the Internet of Things)
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21 pages, 6624 KiB  
Review
Road to Efficiency: V2V Enabled Intelligent Transportation System
by Muhammad Ali Naeem, Sushank Chaudhary and Yahui Meng
Electronics 2024, 13(13), 2673; https://doi.org/10.3390/electronics13132673 - 8 Jul 2024
Cited by 5 | Viewed by 6180
Abstract
Intelligent Transportation Systems (ITSs) have grown rapidly to accommodate the increasing need for safer, more efficient, and environmentally friendly transportation options. These systems cover a wide range of applications, from transportation control and management to self-driving vehicles to improve mobility while tackling urbanization [...] Read more.
Intelligent Transportation Systems (ITSs) have grown rapidly to accommodate the increasing need for safer, more efficient, and environmentally friendly transportation options. These systems cover a wide range of applications, from transportation control and management to self-driving vehicles to improve mobility while tackling urbanization concerns. This research looks closely at the important infrastructure parts of vehicle-to-vehicle (V2V) communication systems. It focuses on the different types of communication architectures that are out there, including decentralized mesh networks, cloud-integrated hubs, edge computing-based architectures, blockchain-enabled networks, hybrid cellular networks, ad-hoc networks, and AI-driven dynamic networks. This review aims to critically analyze and compare the key components of these architectures with their contributions and limitations. Finally, it outlines open research challenges and future technological advancements, encouraging the development of robust and interconnected V2V communication systems in ITSs. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicular Networks and Communications)
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21 pages, 4644 KiB  
Article
Reconfigurable CAN Intrusion Detection and Response System
by Rachit Saini and Riadul Islam
Electronics 2024, 13(13), 2672; https://doi.org/10.3390/electronics13132672 - 7 Jul 2024
Cited by 2 | Viewed by 1909
Abstract
The controller area network (CAN) remains the de facto standard for intra-vehicular communication. CAN enables reliable communication between various microcontrollers and vehicle devices without a central computer, which is essential for sustainable transportation systems. However, it poses some serious security threats due to [...] Read more.
The controller area network (CAN) remains the de facto standard for intra-vehicular communication. CAN enables reliable communication between various microcontrollers and vehicle devices without a central computer, which is essential for sustainable transportation systems. However, it poses some serious security threats due to the nature of communication. According to caranddriver.com, there were at least 150 automotive cybersecurity incidents in 2019, a 94% year-over-year increase since 2016, according to a report from Upstream Security. To safeguard vehicles from such attacks, securing CAN communication, which is the most relied-on in-vehicle network (IVN), should be configured with modifications. In this paper, we developed a configurable CAN communication protocol to secure CAN with a hardware prototype for rapidly prototyping attacks, intrusion detection systems, and response systems. We used a field programmable gate array (FPGA) to prototype CAN to improve reconfigurability. This project focuses on attack detection and response in the case of bus-off attacks. This paper introduces two main modules: the multiple generic errors module with the introduction of the error state machine (MGEESM) module and the bus-off attack detection (BOAD) module for a frame size of 111 bits (BOAD111), based on the CAN protocol presenting the introduction of form error, CRC error, and bit error. Our results show that, in the scenario with the transmit error counter (TEC) value 127 for switching between the error-passive state and bus-off state, the detection times for form error, CRC error, and bit error introduced in the MGEESM module are 3.610 ms, 3.550 ms, and 3.280 ms, respectively, with the introduction of error in consecutive frames. The detection time for BOAD111 module in the same scenario is 3.247 ms. Full article
(This article belongs to the Special Issue Security and Privacy in Networks and Multimedia)
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12 pages, 15242 KiB  
Technical Note
Inherently Decoupled Dc-Link Capacitor Voltage Control of Multilevel Neutral-Point-Clamped Converters
by Gabriel Garcia-Rojas, Sergio Busquets-Monge, Robert Griñó and José M. Campos-Salazar
Electronics 2024, 13(13), 2671; https://doi.org/10.3390/electronics13132671 - 7 Jul 2024
Viewed by 1516
Abstract
This letter derives and discusses the superiority of a simple dc-link capacitor voltage control configuration for multilevel neutral-point-clamped converters with any number of levels. The control involves n − 2 control loops regulating the difference between the voltage of neighbor capacitors. These control [...] Read more.
This letter derives and discusses the superiority of a simple dc-link capacitor voltage control configuration for multilevel neutral-point-clamped converters with any number of levels. The control involves n − 2 control loops regulating the difference between the voltage of neighbor capacitors. These control loops are inherently decoupled, i.e., they are independent and the control action of one loop does not affect the others. This method is proven to be equivalent to previously published approaches, with the added advantages of increased simplicity and scalability to a higher number of levels, all while imposing a lower computational burden. The good performance of such control is confirmed through simulations and experiments. Full article
(This article belongs to the Special Issue Multi-level Power Converters Systems)
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15 pages, 4774 KiB  
Article
DiffPlate: A Diffusion Model for Super-Resolution of License Plate Images
by Sawsan AlHalawani, Bilel Benjdira, Adel Ammar, Anis Koubaa and Anas M. Ali
Electronics 2024, 13(13), 2670; https://doi.org/10.3390/electronics13132670 - 7 Jul 2024
Cited by 3 | Viewed by 2797
Abstract
License plate recognition is a pivotal challenge in surveillance applications, predominantly due to the low resolution and diminutive size of license plates, which impairs recognition accuracy. The advent of AI-based super-resolution techniques offers a promising avenue to ameliorate the resolution of such images. [...] Read more.
License plate recognition is a pivotal challenge in surveillance applications, predominantly due to the low resolution and diminutive size of license plates, which impairs recognition accuracy. The advent of AI-based super-resolution techniques offers a promising avenue to ameliorate the resolution of such images. Despite the deployment of various super-resolution methodologies, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the quest for satisfactory outcomes in license plate image enhancement persists. This paper introduces “DiffPlate”, a novel Diffusion Model specifically tailored for license plate super-resolution. Leveraging the unprecedented capabilities of Diffusion Models in image generation, DiffPlate is meticulously trained on a dataset comprising low-resolution and high-resolution pairs of Saudi license plates, curated for our surveillance application. Our empirical analysis substantiates that DiffPlate markedly eclipses state-of-the-art alternatives such as SwinIR and ESRGAN, evidencing a 26.47% and 37.32% enhancement in Peak Signal-to-Noise Ratio (PSNR) against these benchmarks, respectively. Furthermore, DiffPlate achieves superior performance in terms of Structural Similarity Index (SSIM), with a 4.88% and 16.21% improvement over SwinIR and ESRGAN, respectively. Human evaluative studies further corroborate that images refined by DiffPlate were preferred 92% more frequently compared to those processed by other algorithms. Through DiffPlate, we present a new solution to the license plate super-resolution challenge, demonstrating significant potential for adoption in real-world surveillance systems. Full article
(This article belongs to the Special Issue Signal Processing and AI Applications for Vehicles)
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22 pages, 5460 KiB  
Article
Are There an Infinite Number of Passive Circuit Elements in the World?
by Frank Zhigang Wang
Electronics 2024, 13(13), 2669; https://doi.org/10.3390/electronics13132669 - 7 Jul 2024
Viewed by 1373
Abstract
We found that a second-order ideal memristor [whose state is the charge, i.e., x=q in v=Rx,i,ti] degenerates into a negative nonlinear resistor with an internal power source. After extending analytically and geographically [...] Read more.
We found that a second-order ideal memristor [whose state is the charge, i.e., x=q in v=Rx,i,ti] degenerates into a negative nonlinear resistor with an internal power source. After extending analytically and geographically the above local activity (experimentally verified by the two active higher-integral-order memristors extracted from the famous Hodgkin–Huxley circuit) to other higher-order circuit elements, we concluded that all higher-order passive memory circuit elements do not exist in nature and that the periodic table of the two-terminal passive ideal circuit elements can be dramatically reduced to a reduced table comprising only six passive elements: a resistor, inductor, capacitor, memristor, mem-inductor, and mem-capacitor. Such a bounded table answered an open question asked by Chua 40 years ago: Are there an infinite number of passive circuit elements in the world? Full article
(This article belongs to the Special Issue Memristors beyond the Limitations: Novel Methods and Materials)
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21 pages, 2996 KiB  
Article
Location Privacy Protection in Edge Computing: Co-Design of Differential Privacy and Offloading Mode
by Guowei Zhang, Shengjian Zhang, Zhiyi Man, Chenlin Cui and Wenli Hu
Electronics 2024, 13(13), 2668; https://doi.org/10.3390/electronics13132668 - 7 Jul 2024
Cited by 1 | Viewed by 1961
Abstract
Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices and increases task processing efficiency. Nonetheless, the task offloading process can [...] Read more.
Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices and increases task processing efficiency. Nonetheless, the task offloading process can introduce a critical vulnerability, as adversaries may infer a user’s location through an analysis of their offloading mode, thereby threatening the user’s location privacy. To counteract this vulnerability, this study introduces differential privacy as a protective mechanism to obscure the user’s offloading mode, thereby safeguarding their location information. This research specifically addresses the issue of location privacy leakage stemming from the correlation between a user’s location and their task offloading ratio. The proposed strategy is based on differential privacy. It aims to increase the efficiency of offloading services and the benefits of task offloading. At the same time, it ensures privacy protection. An innovative optimization technique for task offloading that maintains location privacy is presented. Utilizing this technique, users can make informed offloading decisions, dynamically adjusting the level of obfuscation in response to the state of the wireless channel and their privacy requirements. This study substantiates the feasibility and effectiveness of the proposed mechanism through rigorous theoretical analysis and extensive empirical testing. The numerical results demonstrate that the proposed strategy can achieve a balance between offloading privacy and processing overhead. Full article
(This article belongs to the Special Issue AI-Aided Sustainable IoT System: Theories, Techniques, and Applications)
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10 pages, 178 KiB  
Article
The Role of Machine Learning in Advanced Biometric Systems
by Milkias Ghilom and Shahram Latifi
Electronics 2024, 13(13), 2667; https://doi.org/10.3390/electronics13132667 - 7 Jul 2024
Cited by 7 | Viewed by 3526
Abstract
Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false [...] Read more.
Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false positives and false negatives, and better performance. On the other hand, machine learning (ML) has been shown to play a key role in improving such systems. By constantly learning and adapting to users’ changing biometric patterns, ML algorithms can improve accuracy and performance over time. The integration of ML algorithms with biometrics, however, introduces vulnerabilities in such systems. This article investigates the new issues of concern that come about because of the adoption of ML methods in biometric systems. Specifically, techniques to breach biometric systems, namely, data poisoning, model inversion, bias injection, and deepfakes, are discussed. Here, the methodology consisted of conducting a detailed review of the literature in which ML techniques have been adopted in biometrics. In this study, we included all works that have successfully applied ML and reported favorable results after this adoption. These articles not only reported improved numerical results but also provided sound technical justification for this improvement. There were many isolated, unsupported, and unjustified works about the major advantages of ML techniques in improving security, which were excluded from this review. Though briefly mentioned, we did not touch upon encryption/decryption aspects, and, accordingly, cybersecurity was excluded from this study. At the end, recommendations are made to build stronger and more secure systems that benefit from ML adoption while closing the door to adversarial attacks. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
12 pages, 3142 KiB  
Article
Integrated Neural Network Approach for Enhanced Vital Signal Analysis Using CW Radar
by Won Yeol Yoon and Nam Kyu Kwon
Electronics 2024, 13(13), 2666; https://doi.org/10.3390/electronics13132666 - 7 Jul 2024
Cited by 1 | Viewed by 1391
Abstract
This study introduces a novel approach for analyzing vital signals using continuous-wave (CW) radar, employing an integrated neural network model to overcome the limitations associated with traditional step-by-step signal processing methods. Conventional methods for vital signal monitoring, such as electrocardiograms (ECGs) and sphygmomanometers, [...] Read more.
This study introduces a novel approach for analyzing vital signals using continuous-wave (CW) radar, employing an integrated neural network model to overcome the limitations associated with traditional step-by-step signal processing methods. Conventional methods for vital signal monitoring, such as electrocardiograms (ECGs) and sphygmomanometers, require direct contact and impose constraints on specific scenarios. Conversely, our study primarily focused on non-contact measurement techniques, particularly those using CW radar, which is known for its simplicity but faces challenges such as noise interference and complex signal processing. To address these issues, we propose a temporal convolutional network (TCN)-based framework that seamlessly integrates noise removal, demodulation, and fast Fourier transform (FFT) processes into a single neural network. This integration minimizes cumulative errors and processing time, which are common drawbacks of conventional methods. The TCN was trained using a dataset comprising preprocessed in-phase and quadrature (I/Q) signals from the CW radar and corresponding heart rates measured via ECG. The performance of the proposed method was evaluated based on the L1 loss and accuracy against the moving average of the estimated heart rates. The results indicate that the proposed approach has the potential for efficient and accurate non-contact vital signal analysis, opening new avenues in health monitoring and medical research. Additionally, the integration of CW radar and neural networks in our framework offers a robust and scalable solution, enhancing the practicality of non-contact health monitoring systems in diverse environments. This technology can be leveraged in healthcare robots to provide continuous and unobtrusive monitoring of patients’ vital signs, enabling timely interventions and improving overall patient care. Full article
(This article belongs to the Special Issue Intelligence Control and Applications of Intelligence Robotics)
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12 pages, 4096 KiB  
Article
A Geometrically Scalable Lumped Model for Spiral Inductors in Radio Frequency GaN Technology on Silicon
by Simone Spataro, Giuseppina Sapone, Marcello Giuffrida and Egidio Ragonese
Electronics 2024, 13(13), 2665; https://doi.org/10.3390/electronics13132665 - 7 Jul 2024
Cited by 1 | Viewed by 1274
Abstract
This paper presents a lumped scalable model for spiral inductors in a radio frequency (RF) gallium nitride (GaN) technology on silicon substrate. The model has been developed by exploiting electromagnetic (EM) simulations of geometrically scaled spiral inductors. To this aim, the technology substrate, [...] Read more.
This paper presents a lumped scalable model for spiral inductors in a radio frequency (RF) gallium nitride (GaN) technology on silicon substrate. The model has been developed by exploiting electromagnetic (EM) simulations of geometrically scaled spiral inductors. To this aim, the technology substrate, i.e., the metal back-end-of-line along with dielectric and semiconductor layers of the adopted GaN process, has been validated by means of experimental data and then used to define the EM simulator set-up for the spiral inductors. The proposed model adopts a simple π-topology with only seven lumped components and predicts inductor performance in terms of inductance, quality factor (Q-factor) and self-resonance frequency (SRF) for a large range of geometrical parameters of the spiral (i.e., number of turns, metal width, inner diameter). Full article
(This article belongs to the Special Issue Wide-Bandgap Device Application: Devices, Circuits, and Drivers)
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33 pages, 13060 KiB  
Article
Efficient SFC Protection Method against Network Attack Risks in Air Traffic Information Networks
by Yong Yang, Buhong Wang, Jiwei Tian and Peng Luo
Electronics 2024, 13(13), 2664; https://doi.org/10.3390/electronics13132664 - 7 Jul 2024
Cited by 2 | Viewed by 1059
Abstract
With the continuous development of the civil aviation industry toward digitalization and intelligence, the closed architecture of traditional air traffic information networks struggles to meet the rapidly growing demands for air traffic services. Network function virtualization (NFV) is one of the key technologies [...] Read more.
With the continuous development of the civil aviation industry toward digitalization and intelligence, the closed architecture of traditional air traffic information networks struggles to meet the rapidly growing demands for air traffic services. Network function virtualization (NFV) is one of the key technologies that can address the rigidity of traditional air traffic information networks. NFV technology has facilitated the flexible deployment of air traffic services, but it has also expanded the attack surface of the network. In addressing the network attack risks faced by service function chains (SFCs) in NFV environments, a SFC protection method based on honeypots and backup technology (PBHB) is proposed to reduce the resource cost of protecting air traffic information networks while enhancing network security. Initially, PBHB utilizes the TAPD algorithm to deploy the primary VNFs as closely as possible to the shortest path between the source and destination endpoints, thus aiming to reduce SFC latency and save bandwidth resource costs. Subsequently, the RAHDR algorithm is employed to install honeypot VNFs in each physical platform that is at risk of side-channel attacks, thus updating the deployment status of honeypot VNFs in real time based on the VNF lifecycle in order to offer primary protection for SFCs. Lastly, the BDMPE algorithm was used to calculate the backup scheme with the highest protection efficiency to implement secondary protection for the SFCs that still do not meet the security requirements. Through experiments, the maximum backup limit for SFCs in PBHB was determined, confirming its satisfactory performance across various SFC arrival rates. Furthermore, performance comparisons with other SFC protection methods revealed that PBHB achieves optimizations in resources cost while ensuring SFC security and latency. Full article
(This article belongs to the Special Issue 5G Technology for Internet of Things)
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11 pages, 803 KiB  
Article
Cost-Efficient Approximation for Magnitude of a Complex Signal
by Chih-Feng Wu and Muh-Tian Shiue
Electronics 2024, 13(13), 2663; https://doi.org/10.3390/electronics13132663 - 7 Jul 2024
Viewed by 979
Abstract
In this paper, a signal model and a mathematical analysis of an efficient approach are derived to acquire the approximate magnitude of a complex signal by inducing a pre-biased or a pre-scaled factor in the design criteria. According to the deductive results, the [...] Read more.
In this paper, a signal model and a mathematical analysis of an efficient approach are derived to acquire the approximate magnitude of a complex signal by inducing a pre-biased or a pre-scaled factor in the design criteria. According to the deductive results, the pre-biased or pre-scaled factor can be 2∼3 dB, which is determined through its application. The numerical evaluations show that the mean square error (MSE) of the proposed efficient approach for the random signal is around −33 dB. Based on the design templates for the considered approaches, the occupied areas of the proposed type-1 and -2 approaches are merely 0.13 and 0.09 times the area of the direct-method, respectively. As a result, the proposed efficient approach is certainly a cost-efficient method for obtaining the approximate magnitude of a complex signal. Full article
(This article belongs to the Special Issue Modern Circuits and Systems Technologies (MOCAST 2024))
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19 pages, 7090 KiB  
Article
Lightweight Low-Voltage AC Arc-Fault Detection Method Based on the Interpretability Method
by Xin Ning, Dejie Sheng, Tianle Lan, Wenbing He, Jiayu Xiong and Yao Wang
Electronics 2024, 13(13), 2662; https://doi.org/10.3390/electronics13132662 - 7 Jul 2024
Cited by 1 | Viewed by 1630
Abstract
Electrical fires are frequently caused by low-voltage AC series arc faults, which can result in significant injuries and property damage. The installation of arc-fault detection devices is mandated or recommended in many regions and countries across the world, yet the current devices’ detection [...] Read more.
Electrical fires are frequently caused by low-voltage AC series arc faults, which can result in significant injuries and property damage. The installation of arc-fault detection devices is mandated or recommended in many regions and countries across the world, yet the current devices’ detection accuracy is insufficient to completely eliminate the risk posed by arc faults. The method based on artificial intelligence is a solution with high detection accuracy, but the AI model is a ‘black box’. When a misjudgment occurs, the cause of the model error cannot be found fundamentally, and the modification and light weight of the model also presents significant difficulties when using the approach. Given the aforementioned issues, this research proposes a novel lightweight low-voltage AC arc-fault detection method based on the explainability approach. By applying the attention mechanism approach and performing a visual analysis, the contribution of arc features to model detection is determined. Model input data optimization and model structure simplification are achieved at the same time as increased model detection accuracy. Ultimately, an experimental prototype for arc-fault detection is designed and validated. Test results demonstrate the effectiveness of the method by demonstrating that the lightweight model maintains 99.69% detection accuracy, even after optimizing the input data by 80% and reducing the model parameters by 51.52%. Full article
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12 pages, 2838 KiB  
Article
THz Wave Power Enhancement Using a Microstrip Line-Based Combiner Integrated with Arrayed UTC-PDs
by Hussein Ssali, Yoshiki Kamiura, Ryo Doi, Hiroki Agemori, Ming Che, Yuya Mikami and Kazutoshi Kato
Electronics 2024, 13(13), 2661; https://doi.org/10.3390/electronics13132661 - 7 Jul 2024
Viewed by 1525
Abstract
Advancements in semiconductor devices, such as Uni-travelling-carrier photodiodes (UTC-PDs), have played a significant role in the development of Terahertz communication technology. However, the persistent challenge is the limited output power from a single UTC-PD required for practical transmission distances. To enhance the output [...] Read more.
Advancements in semiconductor devices, such as Uni-travelling-carrier photodiodes (UTC-PDs), have played a significant role in the development of Terahertz communication technology. However, the persistent challenge is the limited output power from a single UTC-PD required for practical transmission distances. To enhance the output power, we propose and demonstrate a novel Terahertz wave power combining technique using a photomixer device comprising two arrayed UTC-PDs monolithically integrated with a microstrip line-based 2 × 1 Wilkinson power combiner and a patch antenna on a Silicon Carbide (SiC) substrate at 300 GHz. When the two UTC-PDs are activated at photocurrents of 8 mA and 10 mA, the device exhibits a 7.3 dB increase in power relative to the power obtained when only the 8 mA UTC-PD is activated, and a 4.4 dB increase in power relative to the power obtained with the 10 mA UTC-PD. This implies that power can be enhanced by a factor of N2 if the photocurrent is multiplied by N. Additionally, we demonstrate that the UTC-PD output saturation depends on the space charge effect, which modulates the electric field in the depletion region and results from critical charge density of about 80 kA/cm2 for the device in this work. Full article
(This article belongs to the Special Issue Advances in Wireless Communication Performance Analysis)
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15 pages, 1293 KiB  
Article
An Improved Lightweight YOLOv5s-Based Method for Detecting Electric Bicycles in Elevators
by Ziyuan Zhang, Xianyu Yang and Chengyu Wu
Electronics 2024, 13(13), 2660; https://doi.org/10.3390/electronics13132660 - 7 Jul 2024
Cited by 1 | Viewed by 1353
Abstract
The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection [...] Read more.
The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection rate. To address this issue, this paper presents an improved lightweight method based on YOLOv5s to detect EBs in elevators. This method introduces the MobileNetV2 module to achieve the lightweight performance of the model. By introducing the CBAM attention mechanism and the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5s neck network, the detection precision is improved. In order to better verify that the model can be deployed at the edge of an elevator, this article deploys it using the Raspberry Pi 4B embedded development board and connects it to a buzzer for application verification. The experimental results demonstrate that the model parameters of EBs are reduced by 58.4%, the computational complexity is reduced by 50.6%, the detection precision reaches 95.9%, and real-time detection of electric vehicles in elevators is achieved. Full article
(This article belongs to the Special Issue Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications)
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18 pages, 1584 KiB  
Article
Automatic Generation and Evaluation of French-Style Chinese Modern Poetry
by Li Zuo, Dengke Zhang, Yuhai Zhao and Guoren Wang
Electronics 2024, 13(13), 2659; https://doi.org/10.3390/electronics13132659 - 6 Jul 2024
Viewed by 1362
Abstract
Literature has a strong cultural imprint and regional color, including poetry. Natural language itself is part of the poetry style. It is interesting to attempt to use one language to present poetry in another language style. Therefore, in this study, we propose a [...] Read more.
Literature has a strong cultural imprint and regional color, including poetry. Natural language itself is part of the poetry style. It is interesting to attempt to use one language to present poetry in another language style. Therefore, in this study, we propose a method to fine-tune a pre-trained model in a targeted manner to automatically generate French-style modern Chinese poetry and conduct a multi-faceted evaluation of the generated results. In a five-point scale based on human evaluation, judges assigned scores between 3.29 and 3.93 in seven dimensions, which reached 80.8–93.6% of the scores of the Chinese versions of real French poetry in these dimensions. In terms of the high-frequency poetic imagery, the consistency of the top 30–50 high-frequency poetic images between the poetry generated by the fine-tuned model and the French poetry reached 50–60%. In terms of the syntactic features, compared with the poems generated by the baseline model, the distribution frequencies of three special types of words that appear relatively frequently in French poetry increased by 12.95%, 15.81%, and 284.44% per 1000 Chinese characters in the poetry generated by the fine-tuned model. The human evaluation, poetic image distribution, and syntactic feature statistics show that the targeted fine-tuned model is helpful for the spread of language style. This fine-tuned model can successfully generate modern Chinese poetry in a French style. Full article
(This article belongs to the Special Issue Data Mining Applied in Natural Language Processing)
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12 pages, 878 KiB  
Article
Steganography in QR Codes—Information Hiding with Suboptimal Segmentation
by Katarzyna Koptyra and Marek R. Ogiela
Electronics 2024, 13(13), 2658; https://doi.org/10.3390/electronics13132658 - 6 Jul 2024
Cited by 2 | Viewed by 1881
Abstract
This paper describes a new steganographic method for QR codes. Unlike most information-hiding techniques in this field, it does not rely on the error correction property. Instead, it uses the segmentation feature of QR codes. Encoding of data in a QR code is [...] Read more.
This paper describes a new steganographic method for QR codes. Unlike most information-hiding techniques in this field, it does not rely on the error correction property. Instead, it uses the segmentation feature of QR codes. Encoding of data in a QR code is achieved by creating segments of specific modes, chosen according to data type in order to save space. However, the segmentation does not have to be optimal. A secret message may be embedded into a QR code by selecting an alternative segment type. The presented method generates valid QR codes that may be decoded with standard readers. The solution has been tested using several QR decoders, and it has been confirmed that only the regular message was returned. Additionally, the error correction quality of produced codes is not diminished. The described algorithm is suitable for either digital or printed media, and in both cases, QR codes retain secret data. Full article
(This article belongs to the Special Issue Data Security and Privacy: Challenges and Techniques)
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16 pages, 4071 KiB  
Article
Enhancing Software Code Vulnerability Detection Using GPT-4o and Claude-3.5 Sonnet: A Study on Prompt Engineering Techniques
by Jaehyeon Bae, Seoryeong Kwon and Seunghwan Myeong
Electronics 2024, 13(13), 2657; https://doi.org/10.3390/electronics13132657 - 6 Jul 2024
Cited by 14 | Viewed by 5023
Abstract
This study investigates the efficacy of advanced large language models, specifically GPT-4o, Claude-3.5 Sonnet, and GPT-3.5 Turbo, in detecting software vulnerabilities. Our experiment utilized vulnerable and secure code samples from the NIST Software Assurance Reference Dataset (SARD), focusing on C++, Java, and Python. [...] Read more.
This study investigates the efficacy of advanced large language models, specifically GPT-4o, Claude-3.5 Sonnet, and GPT-3.5 Turbo, in detecting software vulnerabilities. Our experiment utilized vulnerable and secure code samples from the NIST Software Assurance Reference Dataset (SARD), focusing on C++, Java, and Python. We employed three distinct prompting techniques as follows: Concise, Tip Setting, and Step-by-Step. The results demonstrate that GPT-4o and Claude-3.5 Sonnet significantly outperform GPT-3.5 Turbo in vulnerability detection. GPT-4o showed the highest improvement with the Step-by-Step prompt, achieving an F1 score of 0.9072. Claude-3.5 Sonnet exhibited consistent high performance across all prompt types, with its Step-by-Step prompt yielding the best overall results (F1 score: 0.8933, AUC: 0.74). In contrast, GPT-3.5 Turbo showed minimal performance changes across prompts, with the Tip Setting prompt performing best (AUC: 0.65, F1 score: 0.6772), yet significantly lower than the other models. Our findings highlight the potential of advanced models in enhancing software security and underscore the importance of prompt engineering in optimizing their performance. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications)
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21 pages, 2276 KiB  
Article
Beam Prediction for mmWave V2I Communication Using ML-Based Multiclass Classification Algorithms
by Karamot Kehinde Biliaminu, Sherif Adeshina Busari, Jonathan Rodriguez and Felipe Gil-Castiñeira
Electronics 2024, 13(13), 2656; https://doi.org/10.3390/electronics13132656 - 6 Jul 2024
Cited by 1 | Viewed by 1963
Abstract
Beam management is a key functionality in establishing and maintaining reliable communication in cellular and vehicular networks, and it becomes more critical at millimeter-wave (mmWave) frequencies and for high-mobility scenarios. Traditional approaches consume wireless resources and incur high beam training overheads in finding [...] Read more.
Beam management is a key functionality in establishing and maintaining reliable communication in cellular and vehicular networks, and it becomes more critical at millimeter-wave (mmWave) frequencies and for high-mobility scenarios. Traditional approaches consume wireless resources and incur high beam training overheads in finding the best beam pairings, thus necessitating alternative approaches such as position-aided, vision-aided, or, more generally, sensing-aided beam prediction approaches. Current systems are also leveraging artificial intelligence/machine learning (ML) to optimize the beam management procedures; however, the majority of the proposed ML frameworks have been applied to synthetic datasets, leading to overestimated performances. In this work, in the context of vehicle-to-infrastructure (V2I) communication and using the real-world DeepSense6G experimental datasets, we investigate the performance of four ML algorithms on beam prediction accuracy for mmWave V2I scenarios. We compare the performance of K-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), and naïve Bayes (NB) algorithms on position-aided beam prediction accuracy and related metrics such as precision, recall, specificity, and F1-score. The impacts of different beam codebook sizes and dataset split ratios on five different scenarios’ datasets were investigated, independently and collectively. Confusion matrices and area under the receiver operating characteristic curves were also employed to visualize the (mis)classification statistics of the considered ML algorithms. The results show that SVM outperforms the other three algorithms, for the most part, on the scenario-per-scenario cases. However, for the combined scenario with larger data samples, DT outperforms the other three algorithms for both the different codebook sizes and data split ratios. The results also show comparable performance for the different data split ratios considered for the different algorithms. However, with respect to the codebook sizes, the results show that the higher the codebook size, the lower the beam prediction accuracy. With the best accuracy results around 70% for the combined scenario in this study, multi-modal sensing-aided approaches can be explored to increase the beam prediction performance, although at the expense of higher system complexity when compared to the position-aided approach considered in this study. Full article
(This article belongs to the Special Issue Smart Antennas and Systems for 5G and Beyond: Latest Advances and Prospects)
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17 pages, 15754 KiB  
Article
Computation Offloading with Privacy-Preserving in Multi-Access Edge Computing: A Multi-Agent Deep Reinforcement Learning Approach
by Xiang Dai, Zhongqiang Luo and Wei Zhang
Electronics 2024, 13(13), 2655; https://doi.org/10.3390/electronics13132655 - 6 Jul 2024
Viewed by 1452
Abstract
The rapid development of mobile communication technologies and Internet of Things (IoT) devices has introduced new challenges for multi-access edge computing (MEC). A key issue is how to efficiently manage MEC resources and determine the optimal offloading strategy between edge servers and user [...] Read more.
The rapid development of mobile communication technologies and Internet of Things (IoT) devices has introduced new challenges for multi-access edge computing (MEC). A key issue is how to efficiently manage MEC resources and determine the optimal offloading strategy between edge servers and user devices, while also protecting user privacy and thereby improving the Quality of Service (QoS). To address this issue, this paper investigates a privacy-preserving computation offloading scheme, designed to maximize QoS by comprehensively considering privacy protection, delay, energy consumption, and the task discard rate of user devices. We first formalize the privacy issue by introducing the concept of privacy entropy. Then, based on quantified indicators, a multi-objective optimization problem is established. To find an optimal solution to this problem, this paper proposes a computation offloading algorithm based on the Twin delayed deep deterministic policy gradient (TD3-SN-PER), which integrates clipped double-Q learning, prioritized experience replay, and state normalization techniques. Finally, the proposed method is evaluated through simulation analysis. The experimental results demonstrate that our approach can effectively balance multiple performance metrics to achieve optimal QoS. Full article
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21 pages, 551 KiB  
Review
Cybersecurity in Autonomous Vehicles—Are We Ready for the Challenge?
by Irmina Durlik, Tymoteusz Miller, Ewelina Kostecka, Zenon Zwierzewicz and Adrianna Łobodzińska
Electronics 2024, 13(13), 2654; https://doi.org/10.3390/electronics13132654 - 6 Jul 2024
Cited by 12 | Viewed by 15846
Abstract
The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence [...] Read more.
The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence (AI). This review examines the current state of cybersecurity in autonomous vehicles, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks. It also explores existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols. Despite these efforts, numerous challenges remain, including the complexity of AV systems, lack of standardization, latency issues, and resource constraints. This review concludes by highlighting future directions in cybersecurity research and development, emphasizing the potential of AI and machine learning, blockchain technology, industry collaboration, and legislative measures to enhance the security of autonomous vehicles. Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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21 pages, 3840 KiB  
Article
Digital Forensics for Analyzing Cyber Threats in the XR Technology Ecosystem within Digital Twins
by Subin Oh and Taeshik Shon
Electronics 2024, 13(13), 2653; https://doi.org/10.3390/electronics13132653 - 6 Jul 2024
Viewed by 2069
Abstract
Recently, advancements in digital twin and extended reality (XR) technologies, along with industrial control systems (ICSs), have driven the transition to Industry 5.0. Digital twins mimic and simulate real-world systems and play a crucial role in various industries. XR provides innovative user experiences [...] Read more.
Recently, advancements in digital twin and extended reality (XR) technologies, along with industrial control systems (ICSs), have driven the transition to Industry 5.0. Digital twins mimic and simulate real-world systems and play a crucial role in various industries. XR provides innovative user experiences through virtual reality (VR), augmented reality (AR), and mixed reality (MR). By integrating digital twin simulations into XR devices, these technologies are utilized in various industrial fields. However, the prevalence of XR devices has increased the exposure to cybersecurity threats in ICS and digital twin environments. Because XR devices are connected to networks, the control and production data they process are at risk of being exposed to cyberattackers. Attackers can infiltrate XR devices through malicious code or hacking attacks to take control of the ICS or digital twin or paralyze the system. Therefore, this study emphasizes the cybersecurity threats in the ecosystem of XR devices used in ICSs and conducts research based on digital forensics. It identifies potentially sensitive data and artifacts in XR devices and proposes secure and reliable security response measures in the Industry 5.0 environment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Cybersecurity Frontiers: Insights from Industry 4.0 and Innovations for Industry 5.0)
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20 pages, 5502 KiB  
Article
An Efficient Privacy and Anonymity Setup on Hyperledger Fabric for Blockchain-Enabled Internet of Things (IoT) Devices
by Muhammad Saad, Saqib Ali Haidery, Aavash Bhandari, Muhammad Raheel Bhutta, Dong-Joo Park and Tae-Sun Chung
Electronics 2024, 13(13), 2652; https://doi.org/10.3390/electronics13132652 - 6 Jul 2024
Cited by 4 | Viewed by 2069
Abstract
The rise in IoT (Internet of Things) devices poses a significant security challenge. Maintaining privacy and ensuring anonymity within the system is a sought-after feature with inevitable trade-offs, such as scalability and increased complexity, making it incredibly challenging to handle. To tackle this, [...] Read more.
The rise in IoT (Internet of Things) devices poses a significant security challenge. Maintaining privacy and ensuring anonymity within the system is a sought-after feature with inevitable trade-offs, such as scalability and increased complexity, making it incredibly challenging to handle. To tackle this, we introduce our proposed work for managing IoT devices using Hyperledger Fabric. We integrated our system on the blockchain with a closed-circuit television (CCTV) security camera fixed at a rental property. The CCTV security camera redirects its feed whenever a new renter walks in. We have introduced the web token for authentication from the renter to the owner. Our contributions include an efficient framework architecture using key invalidation scenarios and token authentication, a novel chain code algorithm, and stealth addresses with modified ring signatures. We performed different analyses to show the system’s throughput and latency through stress testing. We have shown the significant advantages of the proposed architectures by comparing similar existing schemes. Our proposed scheme enhances the security of blockchain-enabled IoT devices and mitigates the single point of failure issue in the existing scheme, providing a robust and reliable solution. Our future work includes scaling it up to cater to the needs of the healthcare system. Full article
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30 pages, 4245 KiB  
Article
Evolving High-Performance Computing Data Centers with Kubernetes, Performance Analysis, and Dynamic Workload Placement Based on Machine Learning Scheduling
by Vedran Dakić, Mario Kovač and Jurica Slovinac
Electronics 2024, 13(13), 2651; https://doi.org/10.3390/electronics13132651 - 5 Jul 2024
Cited by 10 | Viewed by 115133
Abstract
In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via [...] Read more.
In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in this process, as much work is still needed to figure out how to apply containerization platforms for HPC. Containers have many advantages, as they tend to have less overhead while providing flexibility, modularity, and maintenance benefits. This makes them well-suited for tasks requiring a lot of computing power that are latency- or bandwidth-sensitive. But they are complex to manage, and many daily operations are based on command-line procedures that take years to master. This paper proposes a different architecture based on seamless hardware integration and a user-friendly UI (User Interface). It also offers dynamic workload placement based on real-time performance analysis and prediction and Machine Learning-based scheduling. This solves a prevalent issue in Kubernetes: the suboptimal placement of workloads without needing individual workload schedulers, as they are challenging to write and require much time to debug and test properly. It also enables us to focus on one of the key HPC issues—energy efficiency. Furthermore, the application we developed that implements this architecture helps with the Kubernetes installation process, which is fully automated, no matter which hardware platform we use—x86, ARM, and soon, RISC-V. The results we achieved using this architecture and application are very promising in two areas—the speed of workload scheduling and workload placement on a correct node. This also enables us to focus on one of the key HPC issues—energy efficiency. Full article
(This article belongs to the Section Computer Science & Engineering)
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5 pages, 147 KiB  
Editorial
Object Detection, Segmentation and Categorization in Artificial Intelligence
by Hao Li, Fei Xie, Jianbo Zhou and Jieyi Liu
Electronics 2024, 13(13), 2650; https://doi.org/10.3390/electronics13132650 - 5 Jul 2024
Cited by 2 | Viewed by 1897
Abstract
In the field of computer vision, three basic tasks are particularly important: object detection [...] Full article
(This article belongs to the Special Issue Object Detection, Segmentation and Categorization in Artificial Intelligence)
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