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Electronics, Volume 12, Issue 10 (May-2 2023) – 192 articles

Cover Story (view full-size image): In neural interface applications, wirelessly powered data transmission is essential for semi-permanent use. This study demonstrates body-coupled (BC) data transmission and multi-source power delivery systems for neural interface applications. The implanted data transmitter and power receiver utilize an electrode interface rather than an antenna or coil interface for wireless transmission, enabling the external data receiver and power transmitter with patch electrodes to be placed away from the implant without requiring precise alignment. The implanted power receiver uses a wireless power source from the external power transmitter and a body-coupled 60 Hz signal to generate a supply voltage to increase the recovered power level and the voltage conversion efficiency (VCE). View this paper
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Article
Parallelized A Posteriori Multiobjective Optimization in RF Design
Electronics 2023, 12(10), 2343; https://doi.org/10.3390/electronics12102343 - 22 May 2023
Viewed by 587
Abstract
A posteriori multiobjective optimization relies on a series of mutually independent single-objective optimization subproblems, which can be run in parallel, thus making full use of a multiprocessor (or multicore) computer. This paper presents a parallel process launching scheme, such that practically no computing [...] Read more.
A posteriori multiobjective optimization relies on a series of mutually independent single-objective optimization subproblems, which can be run in parallel, thus making full use of a multiprocessor (or multicore) computer. This paper presents a parallel process launching scheme, such that practically no computing capacity gets wasted. This is achieved using standard Windows API kernel objects for process synchronization of the semaphore and mutex types. The algorithm used was further modified to inherently generate the desired Pareto front in the convenient form of a contour plot. Full article
(This article belongs to the Section Circuit and Signal Processing)
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Article
Two-Tier Feature Extraction with Metaheuristics-Based Automated Forensic Speaker Verification Model
Electronics 2023, 12(10), 2342; https://doi.org/10.3390/electronics12102342 - 22 May 2023
Viewed by 680
Abstract
While speaker verification represents a critically important application of speaker recognition, it is also the most challenging and least well-understood application. Robust feature extraction plays an integral role in enhancing the efficiency of forensic speaker verification. Although the speech signal is a continuous [...] Read more.
While speaker verification represents a critically important application of speaker recognition, it is also the most challenging and least well-understood application. Robust feature extraction plays an integral role in enhancing the efficiency of forensic speaker verification. Although the speech signal is a continuous one-dimensional time series, most recent models depend on recurrent neural network (RNN) or convolutional neural network (CNN) models, which are not able to exhaustively represent human speech, thus opening themselves up to speech forgery. As a result, to accurately simulate human speech and to further ensure speaker authenticity, we must establish a reliable technique. This research article presents a Two-Tier Feature Extraction with Metaheuristics-Based Automated Forensic Speaker Verification (TTFEM-AFSV) model, which aims to overcome the limitations of the previous models. The TTFEM-AFSV model focuses on verifying speakers in forensic applications by exploiting the average median filtering (AMF) technique to discard the noise in speech signals. Subsequently, the MFCC and spectrograms are considered as the inputs to the deep convolutional neural network-based Inception v3 model, and the Ant Lion Optimizer (ALO) algorithm is utilized to fine-tune the hyperparameters related to the Inception v3 model. Finally, a long short-term memory with a recurrent neural network (LSTM-RNN) mechanism is employed as a classifier for automated speaker recognition. The performance validation of the TTFEM-AFSV model was tested in a series of experiments. Comparative study revealed the significantly improved performance of the TTFEM-AFSV model over recent approaches. Full article
(This article belongs to the Section Artificial Intelligence Circuits and Systems (AICAS))
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Editorial
Diagnostics and Fault Tolerance in DC–DC Converters and Related Industrial Electronics Technologies
Electronics 2023, 12(10), 2341; https://doi.org/10.3390/electronics12102341 - 22 May 2023
Viewed by 617
Abstract
The deployment of DC energy systems is an attractive alternative to conventional AC-based energy distribution systems, improving the efficiency of energy supplies and promoting renewable energies [...] Full article
Article
Mixed Near-Field and Far-Field Sources Localization via Oblique Projection
Electronics 2023, 12(10), 2340; https://doi.org/10.3390/electronics12102340 - 22 May 2023
Viewed by 493
Abstract
This paper presents a novel mixed source localization algorithm based on high-order cumulant (HOC) and oblique projection techniques. To address the issue of lower accuracy in near-field source (NFS) localization compared to the far-field source (FFS) localization, the presented algorithm further enhances the [...] Read more.
This paper presents a novel mixed source localization algorithm based on high-order cumulant (HOC) and oblique projection techniques. To address the issue of lower accuracy in near-field source (NFS) localization compared to the far-field source (FFS) localization, the presented algorithm further enhances the accuracy of NFS localization. First, the FFS’s direction-of-arrival (DOA) estimate is acquired utilizing a multiple signal classification (MUSIC) spectral peak search. To classify mixed sources more effectively, we utilize the oblique projection technique, which can successfully prevent FFS information from influencing the estimation of NFS parameters. A HOC matrix with solely NFS DOA information is built by choosing array elements in a specific sequence. The estimation of the NFS DOA is then derived using the estimation of signal parameters via a rotational invariance technique (ESPRIT)-like algorithm. Finally, the NFS range is acquired by a MUSIC search. The performance of the presented algorithm is discussed in several aspects. Compared to existing matrix difference methods, the presented algorithm, which adopts the oblique projection method, achieves superior results in the separation of mixed sources. Without excessively increasing the computational complexity, it not only ensures the performance of localization parameter estimation for FFS but also estimates the NFS with higher precision. The numerical simulations attest to the superior performance of the presented algorithm. Full article
(This article belongs to the Special Issue Advances in Array Signal Processing)
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Article
Feature Contrastive Learning for No-Reference Segmentation Quality Evaluation
Electronics 2023, 12(10), 2339; https://doi.org/10.3390/electronics12102339 - 22 May 2023
Viewed by 562
Abstract
No-reference segmentation quality evaluation aims to evaluate the quality of image segmentation without any reference image during the application process. It usually depends on certain quality criteria to describe a good segmentation with some prior knowledge. Therefore, there is a need for a [...] Read more.
No-reference segmentation quality evaluation aims to evaluate the quality of image segmentation without any reference image during the application process. It usually depends on certain quality criteria to describe a good segmentation with some prior knowledge. Therefore, there is a need for a precise description of the objects in the segmentation and an integration of the representation in the evaluation process. In this paper, from the perspective of understanding the semantic relationship between the original image and the segmentation results, we propose a feature contrastive learning method. This method can enhance the performance of no-reference segmentation quality evaluations and be applied in semantic segmentation scenarios. By learning the pixel-level similarity between the original image and the segmentation result, a contrastive learning step is performed in the feature space. In addition, a class activation map (CAM) is used to guide the evaluation, making the score more consistent with the human visual judgement. Experiments were conducted on the PASCAL VOC2012 dataset, with segmentation results obtained by state-of-the-art (SoA) segmentation methods. We adopted two meta-measure criteria to validate the efficiency of the proposed method. Compared with other no-reference evaluation methods, our method achieves a higher accuracy which is comparable to the supervised evaluation methods and partly even exceeds them. Full article
(This article belongs to the Section Artificial Intelligence)
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Article
Optimizing Hill Climbing Algorithm for S-Boxes Generation
Electronics 2023, 12(10), 2338; https://doi.org/10.3390/electronics12102338 - 22 May 2023
Viewed by 713
Abstract
Nonlinear substitutions or S-boxes are important cryptographic primitives of modern symmetric ciphers. They are designed to complicate the plaintext-ciphertext dependency. According to modern ideas, the S-box should be bijective, have high nonlinearity and algebraic immunity, low delta uniformity, and linear redundancy. These criteria [...] Read more.
Nonlinear substitutions or S-boxes are important cryptographic primitives of modern symmetric ciphers. They are designed to complicate the plaintext-ciphertext dependency. According to modern ideas, the S-box should be bijective, have high nonlinearity and algebraic immunity, low delta uniformity, and linear redundancy. These criteria directly affect the cryptographic strength of ciphers, providing resistance to statistical, linear, algebraic, differential, and other cryptanalysis techniques. Many researchers have used various heuristic search algorithms to generate random S-boxes with high nonlinearity; however, the complexity of this task is still high. For example, the best-known algorithm to generate a random 8-bit bijective S-box with nonlinearity 104 requires high computational effort—more than 65,000 intermediate estimates or search iterations. In this article, we explore a hill-climbing algorithm and optimize the heuristic search parameters. We show that the complexity of generating S-boxes can be significantly reduced. To search for a random bijective S-box with nonlinearity 104, only about 50,000 intermediate search iterations are required. In addition, we generate cryptographically strong S-Boxes for which additional criteria are provided. We present estimates of the complexity of the search and estimates of the probabilities of generating substitutions with various cryptographic indicators. The extracted results demonstrate a significant improvement in our approach compared to the state of the art in terms of providing linear non-redundancy, nonlinearity, algebraic immunity, and delta uniformity. Full article
(This article belongs to the Special Issue Electronization of Businesses - Systems Engineering and Analytics)
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Article
Deep Learning-Based Context-Aware Recommender System Considering Change in Preference
Electronics 2023, 12(10), 2337; https://doi.org/10.3390/electronics12102337 - 22 May 2023
Cited by 1 | Viewed by 667
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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Review
Survey of Intelligent Agricultural IoT Based on 5G
Electronics 2023, 12(10), 2336; https://doi.org/10.3390/electronics12102336 - 22 May 2023
Cited by 1 | Viewed by 1379
Abstract
In the future, agriculture will face the need for increasing production, sustainability, wisdom, and efficiency, which will bring significant challenges to the development of modern agriculture. With the gradual popularization of 5G, advanced information technologies such as the Internet of Things and artificial [...] Read more.
In the future, agriculture will face the need for increasing production, sustainability, wisdom, and efficiency, which will bring significant challenges to the development of modern agriculture. With the gradual popularization of 5G, advanced information technologies such as the Internet of Things and artificial intelligence promoted the evolution of modern agriculture to intelligent agriculture. The 5G-based Internet of Things will play an essential role in the development of smart agriculture. This paper investigates the research progress of 5G Internet of Things in smart agriculture. It sorts out the development status of 5G smart agriculture Internet of Things in recent years. Following that, the concept of 5G smart agriculture Internet of Things is put forward. It expounds on the connotation, architecture, and enabling key technologies. According to the key application scenarios of smart agriculture, practical cases are presented, the development trend and application value of 5G smart agriculture Internet of Things are shown, and the future development direction is put forward. Firstly, the concept of smart agriculture is distinguished, and the category scenarios of smart agriculture are summarized. Following that, the current review research on 5G-IoT is analyzed. This paper focuses on the analysis and summary of the changes brought by 5G to various key scenarios in smart agriculture. This paper analyzes the related key technologies and challenges, puts forward some key scientific problems, and summarizes the research ideas. Finally, the development trend and application value of 5G smart agriculture Internet of Things are shown. The future development direction is also proposed. Full article
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Article
Analysis of the Security Challenges Facing the DS-Lite IPv6 Transition Technology
Electronics 2023, 12(10), 2335; https://doi.org/10.3390/electronics12102335 - 22 May 2023
Viewed by 694
Abstract
This paper focuses on one of the most prominent IPv6 transition technologies named DS-Lite (Dual-Stack Lite). The aim was to analyze the security threats to which this technology might be vulnerable. The analysis is based on the STRIDE method, which stands for Spoofing, [...] Read more.
This paper focuses on one of the most prominent IPv6 transition technologies named DS-Lite (Dual-Stack Lite). The aim was to analyze the security threats to which this technology might be vulnerable. The analysis is based on the STRIDE method, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, and Elevation of Privilege. A testbed was built for the DS-Lite topology using several virtual machines, which were created using CentOS Linux images. The testbed was used to perform several types of attacks against the infrastructure of DS-Lite, especially against the B4 (Basic Bridging Broadband) and the AFTR (Address Family Transition Router) elements, where it was shown that the pool of source ports can be exhausted in 14 s. Eventually, the most common attacks that DS-Lite is susceptible to were summarized, and methods for mitigating such attacks were proposed. Full article
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Article
Improving Norwegian Translation of Bicycle Terminology Using Custom Named-Entity Recognition and Neural Machine Translation
Electronics 2023, 12(10), 2334; https://doi.org/10.3390/electronics12102334 - 22 May 2023
Viewed by 998
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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Article
3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection
Electronics 2023, 12(10), 2333; https://doi.org/10.3390/electronics12102333 - 22 May 2023
Cited by 1 | Viewed by 629
Abstract
In traditional clinical medicine, respiratory physicians or radiologists often identify the location of lung nodules by highlighting targets in consecutive CT slices, which is labor-intensive and easy-to-misdiagnose work. To achieve intelligent detection and diagnosis of CT lung nodules, we designed a 3D convolutional [...] Read more.
In traditional clinical medicine, respiratory physicians or radiologists often identify the location of lung nodules by highlighting targets in consecutive CT slices, which is labor-intensive and easy-to-misdiagnose work. To achieve intelligent detection and diagnosis of CT lung nodules, we designed a 3D convolutional neural network, called 3DAGNet, for pulmonary nodule detection. Inspired by the diagnostic process of lung nodule localization by physicians, the 3DGNet includes a spatial attention and a global search module. A multi-scale cascade module has also been introduced to enhance the model detection using attention enhancement, global information search, and contextual feature fusion. The experimental results showed that the proposed network achieved accurate detection of lung nodule information, and our method achieves a high sensitivity of 88.08% of the average FROC score on the LUNA16 dataset. In addition, ablation experiments also demonstrated the effectiveness of our method. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Multimedia Information Processing)
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Article
A 200 kb/s 36 µw True Random Number Generator Based on Dual Oscillators for IOT Security Application
Electronics 2023, 12(10), 2332; https://doi.org/10.3390/electronics12102332 - 22 May 2023
Viewed by 527
Abstract
As a module of the internet of things (IOT) information security system, the true random number generator (TRNG) plays an important role in overall performance. In this paper, a low-power TRNG based on dual oscillators is proposed. Two high-frequency cross-coupled oscillators are used [...] Read more.
As a module of the internet of things (IOT) information security system, the true random number generator (TRNG) plays an important role in overall performance. In this paper, a low-power TRNG based on dual oscillators is proposed. Two high-frequency cross-coupled oscillators are used to generate high-jitter clock signals, and then the SR latch with power supply below standard power supply voltage is adopted to process the oscillator output to maintain its metastability and increase jitter. The circuit is realized by an SMIC 180 nm 1P6M mixed-signal process. The experimental results show that when power supply voltage is 1.8 V, the circuit outputs a random number bit rate of 200 kb/s, the core area is 0.0039 mm2, and the power consumption is only 36 µw. The output random sequences can pass the NIST SP 800-22 test. Full article
(This article belongs to the Special Issue Electron Devices and Solid-State Circuits)
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Article
An Integrated Off-Line Echo Signal Acquisition System Implemented in SoC-FPGA for High Repetition Rate Lidar
Electronics 2023, 12(10), 2331; https://doi.org/10.3390/electronics12102331 - 22 May 2023
Viewed by 651
Abstract
High repetition rate lidar is typically equipped with a low-energy, high repetition rate laser, and small aperture telescopes. Therefore, it is small, compact, low-cost, and can be networked for observation. However, its data acquisition and control functions are generally not specially designed, and [...] Read more.
High repetition rate lidar is typically equipped with a low-energy, high repetition rate laser, and small aperture telescopes. Therefore, it is small, compact, low-cost, and can be networked for observation. However, its data acquisition and control functions are generally not specially designed, and the data acquisition, storage, and control programs need to be implemented on an IPC (Industrial Personal Computer), which increases the complexity and instability of the lidar system. Therefore, this paper designs an integrated off-line echo signal acquisition system (IOESAS) for lidar developed based on SoC FPGA (System-On-Chip Field Programmable Gate Array). Using a hardware–software co-design approach, the system is implemented in a heterogeneous multi-core chip ZYNQ-7020 (integrated FPGA and ARM). The FPGA implements dual-channel echo data acquisition (gated counting and hardware accumulation). At the same time, the ARM performs laser control and monitoring, laser pointing control, pulse energy monitoring, data storage, and wireless transmission. Offline data acquisition and control software was developed based on LabVIEW, which can remotely control the status of the lidar and download the echo data stored in IOESAS. To verify the performance of the data acquisition system, IOESAS was compared with the photon counting card P7882 and MCS-PCI, respectively. The test results show that they are in good agreement; the linear correlation coefficients were 0.99967 and 0.99884, respectively. IOESAS was installed on lidar outdoors for continuous detection, and the system was able to work independently and stably in different weather conditions, and control functions were tested normally. The gating delay and gating width time jitter error are ±5 ns and ±2 ns, respectively. The IOESAS is now used in several small lidars for networked observations. Full article
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Article
A Compact W-Band Low-Noise Radiometry Sensor for a Single-Pixel Passive Millimeter-Wave Imager
Electronics 2023, 12(10), 2330; https://doi.org/10.3390/electronics12102330 - 22 May 2023
Viewed by 653
Abstract
Recently, studies on the remote detection of dangerous objects on the person have gained importance with increased security problems. Therefore, the use of passive millimeter waves in security systems is increasing because they are harmless to health and can penetrate clothes. In this [...] Read more.
Recently, studies on the remote detection of dangerous objects on the person have gained importance with increased security problems. Therefore, the use of passive millimeter waves in security systems is increasing because they are harmless to health and can penetrate clothes. In this study, a compact low-noise radiometric sensor (LNRS) that can be used to view hidden objects on the person was constructed. The LNRS can be arrayed thanks to its small size and ease of use, and can be used in imaging applications thanks to the 0.24 K resolution obtained. In addition, a passive millimeter imaging system (PMMWI) was developed to obtain images with the LNRS. The PMMWI system, which is realized in a quasi-optical structure, can be used in many experimental studies thanks to its compact structure. Full article
(This article belongs to the Special Issue Recent Advances in Micro/Millimeter-Wave Imaging Technology)
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Article
Maximum Service Coverage in Business Site Selection Using Computer Geometry Software
Electronics 2023, 12(10), 2329; https://doi.org/10.3390/electronics12102329 - 22 May 2023
Viewed by 570
Abstract
A planar maximum coverage location problem in a continuous formulation is considered. The demand zone and service areas are presented as geometric items of given shapes and sizes. Each service area is associated with a point (centroid), relative to which the corresponding geometric [...] Read more.
A planar maximum coverage location problem in a continuous formulation is considered. The demand zone and service areas are presented as geometric items of given shapes and sizes. Each service area is associated with a point (centroid), relative to which the corresponding geometric item forms. The task is to find the position of the centroids to provide an optimal service for the demand zone according to a given criterion. The mathematical model is constructed as a nonlinear optimization problem, in which the variables are the coordinates of the centroids, and the objective function is defined as the area of the demand zone covered by the services. For the formalization and calculation of the objective function, both analytical expressions and computer geometry software are used. The methodology we propose is applicable to the arbitrary shapes of both the demand zone and the service areas. Moreover, this technique does not depend on the complexity of the corresponding items, since it uses the Shapely library, which operates with the same Polygon class. An approach to solving the problem based on the consistent application of local and global optimization methods is proposed. An auxiliary problem is posed that allows one to significantly reduce the run time at the stage of local optimization. The implementation of the approach is illustrated by examples of the maximum coverage location problem when the demand zone is a polygon and the service areas have the shape of a circle and an ellipse. The innovation of this paper lies in the fact that the maximum service coverage problem in business site selection is studied in such a way that both the demand zone and the service areas have an arbitrary shape. Full article
(This article belongs to the Special Issue Electronization of Businesses - Systems Engineering and Analytics)
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Article
Designing a Technological Pathway to Empower Vocational Education and Training in the Circular Wood and Furniture Sector through Extended Reality
Electronics 2023, 12(10), 2328; https://doi.org/10.3390/electronics12102328 - 22 May 2023
Cited by 1 | Viewed by 1078
Abstract
Extended Reality (XR) is a term that refers to virtual, augmented, and, more recently, mixed reality (VR/AR//MR), which are key enabling technologies of the Industry 4.0 (I4.0) and the simulated digital environment of the metaverse. XR enables the simulation of workplace scenarios, providing [...] Read more.
Extended Reality (XR) is a term that refers to virtual, augmented, and, more recently, mixed reality (VR/AR//MR), which are key enabling technologies of the Industry 4.0 (I4.0) and the simulated digital environment of the metaverse. XR enables the simulation of workplace scenarios, providing workers with training in a risk-free environment, resulting in cost savings, improved occupational risk prevention, and enhanced decision-making processes. XR is ideal for supporting digital transformation for organisations in fields such as production, occupational risk prevention, maintenance, and marketing. XR is also a key driver for training initiatives aimed at promoting good practices in the circular economy in specific sectors such as woodworking and furniture (W&F). The European Commission has recognised the potential of XR for the W&F sector, funding initiatives such as the European project, Allview, which seeks to identify the most appropriate and beneficial technologies of I4.0 with a green and digital transition focus from the perspective of vocational education and training (VET). This paper presents the work carried out within the framework of Allview, including the research and comparison of current software and hardware of XR tools suitable for VET in the W&F field, a review of successful examples of XR applied to W&F training actions, and an analysis of the opinions gathered from European students, teachers, and training organisations regarding the use of XR in education. As a result, the authors present a training pathway aimed at the development and implementation of a XR training scenario/lab/environment focused on VR, 360° videos, and MR, as a guideline for developing immersive XR training contents, contributing to the digital and green transformation of VET in the W&F sector. Full article
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Article
Ethereum Smart Contract Vulnerability Detection Model Based on Triplet Loss and BiLSTM
Electronics 2023, 12(10), 2327; https://doi.org/10.3390/electronics12102327 - 22 May 2023
Viewed by 989
Abstract
The wide application of Ethereum smart contracts in the Internet of Things, finance, medical, and other fields is associated with security challenges. Traditional detection methods detect vulnerabilities by stacking hard rules, which are associated with the bottleneck of a high false-positive rate and [...] Read more.
The wide application of Ethereum smart contracts in the Internet of Things, finance, medical, and other fields is associated with security challenges. Traditional detection methods detect vulnerabilities by stacking hard rules, which are associated with the bottleneck of a high false-positive rate and low detection efficiency. To make up for the shortcomings of traditional methods, existing deep learning methods improve model performance by combining multiple models, resulting in complex structures. From the perspective of optimizing the model feature space, this study proposes a vulnerability detection scheme for Ethereum smart contracts based on metric learning and a bidirectional long short-term memory (BiLSTM) network. First, the source code of the Ethereum contract is preprocessed, and the word vector representation is used to extract features. Secondly, the representation is combined with metric learning and the BiLSTM model to optimize the feature space and realize the cohesion of similar contracts and the discreteness of heterogeneous contracts, improving the detection accuracy. In addition, an attention mechanism is introduced to screen key vulnerability features to enhance detection observability. The proposed method was evaluated on a large-scale dataset containing four types of vulnerabilities: arithmetic vulnerabilities, re-entrancy vulnerabilities, unchecked calls, and inconsistent access controls. The results show that the proposed scheme exhibits excellent detection performance. The accuracy rates reached 88.31%, 93.25%, 91.85%, and 90.59%, respectively. Full article
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Article
Visual Extraction of Refined Operation Mode of New Power System Based on IPSO-Kmeans
Electronics 2023, 12(10), 2326; https://doi.org/10.3390/electronics12102326 - 22 May 2023
Viewed by 853
Abstract
Due to the influence of the high proportion of renewable energy penetration, the time-varying and complex operation mode of the new power system is gradually increasing, leading to a lack of fineness and practicality of traditional operation modes. To this end, a new [...] Read more.
Due to the influence of the high proportion of renewable energy penetration, the time-varying and complex operation mode of the new power system is gradually increasing, leading to a lack of fineness and practicality of traditional operation modes. To this end, a new visual extraction method for fine operation mode of power system is proposed. Specifically, aiming at the dimensional problem between high-dimensional electrical characteristic variables, a power grid operation data preprocessing method based on maximum absolute standardization (MaxAbs) is designed. Then, in order to reduce the impact of redundant features on the accuracy of the operation mode extraction results, the Pearson correlation coefficient is introduced to optimize the feature space relationship matrix, constructing a screening model of operating mode characteristic variables based on pearson kernel principal component analysis (P_KPCA). Then, with the clustering elbow index as the constraint condition, a K-means algorithm based on improved particle swarm optimization (IPSO-Kmeans) was proposed to realize fine operation mode extraction. Finally, the experimental analysis is carried out with the actual operation data of the power grid for one year and based on uniform manifold approximation and projection (UMAP) to visualize the extraction results of the operation mode. The validity and accuracy of the proposed method are verified. Full article
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Article
Design of a GaAs-Based Ka-Band Low Noise Amplifier MMIC with Gain Flatness Enhancement
Electronics 2023, 12(10), 2325; https://doi.org/10.3390/electronics12102325 - 21 May 2023
Cited by 1 | Viewed by 884
Abstract
This paper presents a GaAs-based Ka-band low noise amplifier (LNA) with gain flatness enhancement. Active device optimization and inductive degeneration techniques were employed to obtain a low noise figure (NF) and good input/output return loss. In order to achieve a flat gain response [...] Read more.
This paper presents a GaAs-based Ka-band low noise amplifier (LNA) with gain flatness enhancement. Active device optimization and inductive degeneration techniques were employed to obtain a low noise figure (NF) and good input/output return loss. In order to achieve a flat gain response over a wide bandwidth, the stagger tuning technique was utilized. The proposed LNA was implemented by 0.15 μm GaAs pHEMT process, and the chip area is only 1.5 × 0.9 mm2. Measurement results show that the presented LNA exhibits a small signal gain of 21.5 ± 0.3 dB, and the NF of the LNA is less than 2.2 dB from 32 to 40 GHz at room temperature. Full article
(This article belongs to the Special Issue Advanced Design of RF/Microwave Circuit)
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Article
Adaptive Droop Control of VSC-MTDC System Based on Virtual Inertia
Electronics 2023, 12(10), 2324; https://doi.org/10.3390/electronics12102324 - 21 May 2023
Viewed by 569
Abstract
In order to solve the problem that the voltage source converter based multi-terminal direct current (VSC-MTDC) system cannot provide inertia and participate in frequency modulation after connecting to the AC power grid under the traditional control strategy, an adaptive control strategy based on [...] Read more.
In order to solve the problem that the voltage source converter based multi-terminal direct current (VSC-MTDC) system cannot provide inertia and participate in frequency modulation after connecting to the AC power grid under the traditional control strategy, an adaptive control strategy based on virtual inertia is proposed. First, the relationship between AC frequency and DC voltage was established by a virtual inertia control, allowing the VSC-MTDC system to provide inertia to the AC side. Second, to address the limited inertia coefficient selection due to DC voltage deviation, an adaptive control was adopted. When the DC voltage deviation is small, the inertia coefficient is increased to obtain a better inertial response; on the contrary, the inertia coefficient is reduced to prevent the DC voltage from exceeding the limit. Finally, to solve the problem of insufficient flexibility of the fixed droop coefficient, this paper introduces the power margin of a VSC-station into the droop coefficient to dynamically adjust the distribution ratio of unbalanced power and reduce the DC voltage deviation. The three-terminal VSC-MTDC system was modelled on the PSCAD/EMTDC simulation platform, and the superiority of the control strategy was highlighted in this paper by comparing it with conventional droop control and a fixed virtual inertia coefficient. Full article
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Article
DC-YOLOv8: Small-Size Object Detection Algorithm Based on Camera Sensor
Electronics 2023, 12(10), 2323; https://doi.org/10.3390/electronics12102323 - 21 May 2023
Cited by 7 | Viewed by 6719
Abstract
Traditional camera sensors rely on human eyes for observation. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces [...] Read more.
Traditional camera sensors rely on human eyes for observation. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces efficiency. Object recognition technology is an important technology used to judge the object’s category on a camera sensor. In order to solve this problem, a small-size object detection algorithm for special scenarios was proposed in this paper. The advantage of this algorithm is that it not only has higher precision for small-size object detection but also can ensure that the detection accuracy for each size is not lower than that of the existing algorithm. There are three main innovations in this paper, as follows: (1) A new downsampling method which could better preserve the context feature information is proposed. (2) The feature fusion network is improved to effectively combine shallow information and deep information. (3) A new network structure is proposed to effectively improve the detection accuracy of the model. From the point of view of detection accuracy, it is better than YOLOX, YOLOR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny, and YOLOv8. Three authoritative public datasets are used in these experiments: (a) In the Visdron dataset (small-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 2.5%, 1.9%, and 2.1% higher than those of YOLOv8s, respectively. (b) On the Tinyperson dataset (minimal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 1%, 0.2%, and 1.2% higher than those of YOLOv8s, respectively. (c) On the PASCAL VOC2007 dataset (normal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 0.5%, 0.3%, and 0.4% higher than those of YOLOv8s, respectively. Full article
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Article
Sensing and Secure NOMA-Assisted mMTC Wireless Networks
Electronics 2023, 12(10), 2322; https://doi.org/10.3390/electronics12102322 - 21 May 2023
Viewed by 641
Abstract
Throughout this study, a novel network model for massive machine-type communications (mMTC) is proposed using a compressive sensing (CS) algorithm and a non-orthogonal multiple access (NOMA) scheme. Further, physical-layer security (PLS) is applied in this network to provide secure communication. We first assume [...] Read more.
Throughout this study, a novel network model for massive machine-type communications (mMTC) is proposed using a compressive sensing (CS) algorithm and a non-orthogonal multiple access (NOMA) scheme. Further, physical-layer security (PLS) is applied in this network to provide secure communication. We first assume that all the legitimate nodes operate in full-duplex mode; then, an artificial noise (AN) signal is emitted while receiving the signal from the head node to confuse eavesdroppers (Eve). A convex optimization tool is used to detect the active number of nodes in the proposed network using a sparsity-aware maximum a posteriori (S-MAP) detection algorithm. The sensing-aided secrecy sum rate of the proposed network is analyzed and compared with the sum rate of the network without sensing, and the closed-form expression of the secrecy outage probability of the proposed mMTC network is derived. Finally, our numerical results demonstrate the impact of an active sensing algorithm in the proposed mMTC network; improvement in the secrecy outage of the proposed network is achieved through increasing the distance of the Eve node. Full article
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Article
The Influence of Virtual Character Design on Emotional Engagement in Immersive Virtual Reality: The Case of Feelings of Being
Electronics 2023, 12(10), 2321; https://doi.org/10.3390/electronics12102321 - 21 May 2023
Viewed by 1098
Abstract
Immersive virtual reality applications based on head-mounted displays are gaining momentum among students and educational institutes, but there is a lack of information about the preferences of virtual characters and emotional engagement in these applications. The objectives of this study were to: (i) [...] Read more.
Immersive virtual reality applications based on head-mounted displays are gaining momentum among students and educational institutes, but there is a lack of information about the preferences of virtual characters and emotional engagement in these applications. The objectives of this study were to: (i) evaluate participants’ preferences on virtual characters in virtual reality; (ii) measure emotional engagement among the users in terms of Feelings of Being; and (iii) identify relationships between virtual characters and emotional engagement. We conducted a mixed-method user experience evaluation on the HHVR virtual reality application that introduces the premises of a Finnish university and has three virtual characters: a human virtual character based on a real person, a fictional human virtual character, and a cat virtual character. We set up an eSports event where presenters (N = 12, mean age: 31.09) experienced HHVR using a head-mounted display and spectators (N = 38, mean age: 25.95) observed the experiment through large screens. We administered a questionnaire and conducted semi-structured interviews to gain insights into the participants’ preferences on virtual characters and emotional engagement. The results indicated that the virtual character preferences varied between the presenters and spectators; the cat was a highly liked virtual character in both groups, and the realistic human virtual character garnered mixed reactions from the spectators, although she was generally liked by the presenters. Both groups experienced several Feelings of Being, such as engagement, effectiveness, security, trust, enjoyment, and excitement, during the HHVR experience. Moderate and significant correlations were identified between the virtual characters and some of the Feelings of Being, thus indicating that the type of virtual character could impact emotional engagement; however, this requires further exploration. Full article
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Article
A Scenario-Generic Neural Machine Translation Data Augmentation Method
Electronics 2023, 12(10), 2320; https://doi.org/10.3390/electronics12102320 - 21 May 2023
Cited by 31 | Viewed by 899
Abstract
Amid the rapid advancement of neural machine translation, the challenge of data sparsity has been a major obstacle. To address this issue, this study proposes a general data augmentation technique for various scenarios. It examines the predicament of parallel corpora diversity and high [...] Read more.
Amid the rapid advancement of neural machine translation, the challenge of data sparsity has been a major obstacle. To address this issue, this study proposes a general data augmentation technique for various scenarios. It examines the predicament of parallel corpora diversity and high quality in both rich- and low-resource settings, and integrates the low-frequency word substitution method and reverse translation approach for complementary benefits. Additionally, this method improves the pseudo-parallel corpus generated by the reverse translation method by substituting low-frequency words and includes a grammar error correction module to reduce grammatical errors in low-resource scenarios. The experimental data are partitioned into rich- and low-resource scenarios at a 10:1 ratio. It verifies the necessity of grammatical error correction for pseudo-corpus in low-resource scenarios. Models and methods are chosen from the backbone network and related literature for comparative experiments. The experimental findings demonstrate that the data augmentation approach proposed in this study is suitable for both rich- and low-resource scenarios and is effective in enhancing the training corpus to improve the performance of translation tasks. Full article
(This article belongs to the Special Issue Natural Language Processing and Information Retrieval)
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Article
Research on Adaptive Cruise Systems Based on Adjacent Vehicle Trajectory Prediction
Electronics 2023, 12(10), 2319; https://doi.org/10.3390/electronics12102319 - 21 May 2023
Viewed by 573
Abstract
Vehicles in the adjacent lane making abrupt lane changes is a common and frequent action during traffic movement. Being aware of adjacent vehicles ahead of time, determining their cut-in intention, monitoring their cut-in trajectory in real time, and actively adjusting following speed are [...] Read more.
Vehicles in the adjacent lane making abrupt lane changes is a common and frequent action during traffic movement. Being aware of adjacent vehicles ahead of time, determining their cut-in intention, monitoring their cut-in trajectory in real time, and actively adjusting following speed are all critical for adaptive cruise systems for vehicles. This study proposes a flexible following-factor-calculation approach that considers the driver’s willingness to take risks for the purpose of identifying cut-in intent, predicting trajectory, and narrowing the window for following cruise speed adjustment to improve passenger ride comfort. To begin, a lane-change trajectory prediction algorithm based on driver adventitious factor correction is proposed in order to correctly predict the lane-change trajectory of adjacent vehicles in urban road traffic scenarios. Second, the flexible following factor and the flexible switching factor of the following target are constructed to overcome the influence of the uncertainty caused by internal and external disturbances on the vehicle following the motion process, and to reduce the impact of cut-in events on passenger comfort. An anti-disturbance rejection control and an adaptive cruise controller based on the vehicle’s longitudinal inverse dynamics model are proposed in order to compensate for and suppress the internal perturbations caused by the vehicle’s internal parameter changes and the random disturbances caused by external road environment changes. The results of simulation and real-world testing showed an average of 28% improvement in passenger comfort. Full article
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Article
Investigating Digital Intensity and E-Commerce as Drivers for Sustainability and Economic Growth in  the EU Countries
Electronics 2023, 12(10), 2318; https://doi.org/10.3390/electronics12102318 - 21 May 2023
Cited by 1 | Viewed by 1119
Abstract
Digital technology development caused the digital transformation of the economy and society. E-commerce, the most widespread among digital innovations, reached a significant share, particularly during the COVID-19 pandemic, impacting economic growth. The progress of digital technologies and the evolution of e-commerce can contribute [...] Read more.
Digital technology development caused the digital transformation of the economy and society. E-commerce, the most widespread among digital innovations, reached a significant share, particularly during the COVID-19 pandemic, impacting economic growth. The progress of digital technologies and the evolution of e-commerce can contribute to the more sustainable development of organizations and worldwide economies. This paper analyzed the influences of digital transformation and e-commerce on GDP and sustainable development. The study used the Eurostat database to gather the research variables for the EU countries. The paper used artificial neural networks and cluster analysis to reveal the significant influence of digital transformation and e-commerce on GDP and sustainable organizational development. Countries with a low level of digital transformation and e-commerce should propel these activities to increase economic performance sustainably. Full article
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Article
An Implantable Bio-Signal Sensor SoC with Low-Standby-Power 8K-Bit SRAM for Continuous Long-Term Monitoring
Electronics 2023, 12(10), 2317; https://doi.org/10.3390/electronics12102317 - 21 May 2023
Viewed by 686
Abstract
Individualized treatment of chronic diseases opens up great opportunities for implantable biosensor systems capable of tracking vital signals over long periods of time. To this end, low-power techniques in standby mode and the efficient utilization of storage space will be important issues for [...] Read more.
Individualized treatment of chronic diseases opens up great opportunities for implantable biosensor systems capable of tracking vital signals over long periods of time. To this end, low-power techniques in standby mode and the efficient utilization of storage space will be important issues for the implementation of such rechargeable implants with a built-in memory. This paper presents key circuit techniques, including a leakage-current-based clock generator that eliminates the need for an internal reference clock source, a low-standby-power 8Kbit SRAM with negative wordline and dynamic supply voltage scaling, and an adaptive sensing scheme to improve storage space utilization. When implemented with commercial 180 nm CMOS technology for the circuit simulation, approximately 70% (100 nW) of power dissipation was reduced from internal clock source, about 70% of power consumed by 8Kbit SRAM was saved, and the storage space utilization was improved by about 42.8%. In the end, the proposed implantable biosensor SoC consumes about 82.5 nW of standby power, saving about 42% from the previous approach and can last for 2.5 days using a 5 uAh thin-film battery (CYMBET® 1.7 × 2.2 mm2). Full article
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Article
A Hierarchical Clustering Obstacle Detection Method Applied to RGB-D Cameras
Electronics 2023, 12(10), 2316; https://doi.org/10.3390/electronics12102316 - 21 May 2023
Cited by 1 | Viewed by 657
Abstract
Environment perception is a key part of robot self-controlled motion. When using vision to accomplish obstacle detection tasks, it is difficult for deep learning methods to detect all obstacles due to complex environment and vision limitations, and it is difficult for traditional methods [...] Read more.
Environment perception is a key part of robot self-controlled motion. When using vision to accomplish obstacle detection tasks, it is difficult for deep learning methods to detect all obstacles due to complex environment and vision limitations, and it is difficult for traditional methods to meet real-time requirements when applied to embedded platforms. In this paper, a fast obstacle-detection process applied to RGB-D cameras is proposed. The process has three main steps, feature point extraction, noise removal, and obstacle clustering. Using Canny and Shi–Tomasi algorithms to complete the pre-processing and feature point extraction, filtering noise based on geometry, grouping obstacles with different depths based on the basic principle that the feature points on the same object contour must be continuous or within the same depth in the view of RGB-D camera, and then doing further segmentation from the horizontal direction to complete the obstacle clustering work. The method omits the iterative computation process required by traditional methods and greatly reduces the memory and time overhead. After experimental verification, the proposed method has a comprehensive recognition accuracy of 82.41%, which is 4.13% and 19.34% higher than that of RSC and traditional methods, respectively, and recognition accuracy of 91.72% under normal illumination, with a recognition speed of more than 20 FPS on the embedded platform; at the same time, all detections can be achieved within 1 m under normal illumination, and the detection error is no more than 2 cm within 3 m. Full article
(This article belongs to the Topic Computer Vision and Image Processing)
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Article
Efficient Medical Knowledge Graph Embedding: Leveraging Adaptive Hierarchical Transformers and Model Compression
Electronics 2023, 12(10), 2315; https://doi.org/10.3390/electronics12102315 - 20 May 2023
Viewed by 847
Abstract
Medical knowledge graphs have emerged as essential tools for representing complex relationships among medical entities. However, existing methods for learning embeddings from medical knowledge graphs, such as DistMult, RotatE, ConvE, InteractE, JointE, and ConvKB, may not adequately capture the unique challenges posed by [...] Read more.
Medical knowledge graphs have emerged as essential tools for representing complex relationships among medical entities. However, existing methods for learning embeddings from medical knowledge graphs, such as DistMult, RotatE, ConvE, InteractE, JointE, and ConvKB, may not adequately capture the unique challenges posed by the domain, including the heterogeneity of medical entities, rich hierarchical structures, large-scale, high-dimensionality, and noisy and incomplete data. In this study, we propose an Adaptive Hierarchical Transformer with Memory (AHTM) model, coupled with a teacher–student model compression approach, to effectively address these challenges and learn embeddings from a rich medical knowledge dataset containing diverse entities and relationship sets. We evaluate the AHTM model on this newly constructed “Med-Dis” dataset and demonstrate its superiority over baseline methods. The AHTM model achieves substantial improvements in Mean Rank (MR) and Hits@10 values, with the highest MR value increasing by nearly 56% and Hits@10 increasing by 39%. Furthermore, we observe similar performance enhancements on the “FB15K-237” and “WN18RR” datasets. Our model compression approach, incorporating knowledge distillation and weight quantization, effectively reduces the model’s storage and computational requirements, making it suitable for resource-constrained environments. Overall, the proposed AHTM model and compression techniques offer a novel and effective solution for learning embeddings from medical knowledge graphs and enhancing our understanding of complex relationships among medical entities, while addressing the inadequacies of existing approaches. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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Article
A Six-Switch Mode Decoupled Wireless Power Transfer System with Dynamic Parameter Self-Adaption
Electronics 2023, 12(10), 2314; https://doi.org/10.3390/electronics12102314 - 20 May 2023
Viewed by 694
Abstract
For the fully resonant wireless power transfer (WPT) system, the high coupling of the converter and the resonant network introduced many problems, such as frequency splitting, the power curve peak limit, and the strict switch strategy. To solve these problems, this paper proposed [...] Read more.
For the fully resonant wireless power transfer (WPT) system, the high coupling of the converter and the resonant network introduced many problems, such as frequency splitting, the power curve peak limit, and the strict switch strategy. To solve these problems, this paper proposed a new six-switch topology based on the full–-bridge converter. With the unique structures containing two capacitor-isolated switches and a source-isolated diode, the system decouples the converter and the resonant network, and its modes have been decoupled, called the independent power injection and free resonance WPT (IPIFR–WPT) system. The capacitor-isolated switches and the source-isolated diode make the converter operate only when the voltage on the primary capacitor is equal to the source voltage, and the source will be isolated by the diode when the capacitor voltage is great than the source, which provides a wide time margin for the switches of the converter to turn on in advance. In this margin, the operation point is self-determined the same whenever the switches turn on so that the system’s performance is consistent. Based on this characteristic, the system can self-adapt a dynamic change in system parameters, with at least 15% tolerance for the coupling coefficient and 14% for the load resistance. Full article
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