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Electronics
Journal Prestige (SJR): 0.548
Citation Impact (citeScore): 3
Number of Followers: 184  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2079-9292
Published by MDPI Homepage  [258 journals]
  • Electronics, Vol. 13, Pages 2229: Color Image Encryption Based on a Novel
           Fourth-Direction Hyperchaotic System

    • Authors: Zhuoyi Lei, Jiacheng Yang, Hanshuo Qiu, Xiangzi Zhang, Jizhao Liu
      First page: 2229
      Abstract: Neuromorphic computing draws inspiration from the brain to design energy-efficient hardware for information processing, enabling highly complex tasks. In neuromorphic computing, chaotic phenomena describe the nonlinear interactions and dynamic behaviors. Chaotic behavior can be utilized in neuromorphic computing to accomplish complex information processing tasks; therefore, studying chaos is crucial. Today, more and more color images are appearing online. However, the generation of numerous images has also brought about a series of security issues. Ensuring the security of images is crucial. We propose a novel fourth-direction hyperchaotic system in this paper. In comparison to low-dimensional chaotic systems, the proposed hyperchaotic system exhibits a higher degree of unpredictability and various dynamic behaviors. The dynamic behaviors include fourth-direction hyperchaos, third-direction hyperchaos, and second-direction hyperchaos. The hyperchaotic system generates chaotic sequences. These chaotic sequences are the foundation of the encryption scheme discussed in this paper. Images are altered by employing methods such as row and column scrambling as well as diffusion. These operations will alter both the pixel values and positions. The proposed encryption scheme has been analyzed through security and application scenario analyses. We perform a security analysis to evaluate the robustness and weaknesses of the encryption scheme. Moreover, we conduct an application scenario analysis to help determine the practical usability and effectiveness of the encryption scheme in real-world situations. These analyses demonstrate the efficiency of the encryption scheme.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122229
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2230: Improving Object Detection Accuracy with
           Self-Training Based on Bi-Directional Pseudo Label Recovery

    • Authors: Shoaib Sajid, Zafar Aziz, Odilbek Urmonov, HyungWon Kim
      First page: 2230
      Abstract: Semi-supervised training methods need reliable pseudo labels for unlabeled data. The current state-of-the-art methods based on pseudo labeling utilize only high-confidence predictions, whereas poor confidence predictions are discarded. This paper presents a novel approach to generate high-quality pseudo labels for unlabeled data. It utilizes predictions with high- and low-confidence levels to generate refined labels and then validates the accuracy of those predictions through bi-directional object tracking. The bi-directional object tracker leverages both past and future information to recover missing labels and increase the accuracy of the generated pseudo labels. This method can also substantially reduce the effort and time needed in label creation compared to the conventional manual labeling. The proposed method utilizes a buffer to accumulate detection labels (bounding boxes) predicted by the object detector. These labels are refined for accuracy though forward and backward tracking, ultimately constructing the final set of pseudo labels. The method is integrated in the YOLOv5 object detector and tested on the BDD100K dataset. Through the experiments, we demonstrate the effectiveness of the proposed scheme in automating the process of pseudo label generation with notably higher accuracy than the recent state-of-the-art pseudo label generation schemes. The results show that the proposed method outperforms previous methods in terms of mean average precision (mAP), label generation accuracy, and speed. Using the bi-directional recovery method, an increase in mAP@50 for the BDD100K dataset by 0.52% is achieved, and for the Waymo dataset, it provides an improvement of mAP@50 by 8.7% to 9.9% compared to 8.1% of the existing method when pre-training with 10% of the dataset. An improvement by 2.1% to 2.9% is achieved as compared to 1.7% of the existing method when pre-training with 20% of the dataset. Overall, the improved method leads to a significant enhancement in detection accuracy, achieving higher mAP scores across various datasets, thus demonstrating its robustness and effectiveness in diverse conditions.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122230
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2231: A Data-Driven Motor Optimization Method
           Based on Support Vector Regression—Multi-Objective, Multivariate,
           and with a Limited Sample Size

    • Authors: Guanghao Li, Ruicheng Li, Haobo Hou, Guoyi Zhang, Zhiyong Li
      First page: 2231
      Abstract: The increasing demand for sustainable development and energy efficiency underscores the importance of optimizing motors in driving the upgrade of energy structures. This paper studies a data-driven approach for the multi-objective optimization of motors designed for scenarios involving multiple variables, objectives, and limited sample sizes and validates its efficacy. Initially, sensitivity analysis is employed to identify potentially influential variables, thus selecting key design parameters. Subsequently, Latin hypercube sampling (LHS) is utilized to select experimental points, ensuring the coverage of the modeled test points across the experimental space to enhance fitting accuracy. Finally, the support vector regression (SVR) algorithm is employed to fit the objective function, in conjunction with multi-objective particle swarm optimization (MOPSO) for solution derivation. The presented method is used to optimize the efficiency, average output torque, and induced electromotive force harmonic distortion rate of a permanent magnet synchronous motor (PMSM). The results show an improvement of approximately 6.80% in average output torque and a significant decrease of about 59.5% in the induced electromotive force harmonic distortion rate, with minimal impact on efficiency. This study offers a pathway for enhancing motor performance, holding practical significance.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122231
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2232: Sequential Learning of Flame Objects
           Sorted by Size for Early Fire Detection in Surveillance Videos

    • Authors: Widia A. Samosir, Duy B. Nguyen, Seong G. Kong
      First page: 2232
      Abstract: This paper presents a sequential learning method aimed at improving the performance of a lightweight deep learning model used for detecting fires at an early stage in surveillance video streams. The proposed approach involves a sequence of supervised learning steps, wherein the entire training dataset is partitioned into multiple sub-datasets based on the size of fire objects. The size of fire objects is measured by object size ratio, which is the ratio of the bounding box area of the detected fire flame object relative to the entire image area. The initial training sub-dataset contains the largest-sized fire objects, progressing to the final sub-dataset containing the smallest-sized fire objects. The objective is to employ sequential learning to enhance the detection of small-sized fire objects relative to the image area using a lightweight model suitable for edge computing devices. Experiment results demonstrate that a deep learning fire detection model trained sequentially with a descending order of object size can effectively detect small flame objects with an object size ratio less than 0.006, achieving an F1 score of 93.1%, representing a 27% improvement compared to traditional supervised learning with no sequential learning steps. Additionally, performance in detecting tiny flame objects with an object size ratio less than 0.0016 achieves an F1 score of 94.5%, showing a 17.5% increase compared to the baseline without sequential learning.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122232
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2233: Electron-Induced Single-Event Effect in
           28 nm SRAM-Based FPGA

    • Authors: Jiayu Tian, Rongxing Cao, Yan Liu, Yulong Cai, Bo Mei, Lin Zhao, Shuai Cui, He Lv, Yuxiong Xue
      First page: 2233
      Abstract: As the feature size of integrated circuit decreases, the critical charge of single-event effect decreases as well, making nano-scale devices more susceptible to the high-energy charged particles during their application in space. Here, we study the electron-induced single-event effect in 28 nm static random-access memory (SRAM)-based field programmable gate array (FPGA) utilizing high-energy electrons with energy of 1 MeV~5 MeV. The experimental results demonstrate that the 3 MeV electrons can cause single-event functional interrupts (SEFIs) in FPGA, while the electrons with other energies cannot. To further explore the mechanism of electron-induced SEFIs in this nanoscale FPGA, we combined Monte Carlo, Technology Computer-Aided Design (TCAD), and Simulation Program with Integrated Circuit Emphasis (SPICE) simulations. It is revealed that the SEFI was mainly caused by the direct ionization effect of high-energy electrons, and the SEFI was related to the interactions between multiple sensitive nodes.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122233
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2234: Flying Base Station Channel Capacity
           Limits: Dependent on Stationary Base Station and Independent of
           Positioning

    • Authors: Sang-Yoon Chang, Kyungmin Park, Jonghyun Kim, Jinoh Kim
      First page: 2234
      Abstract: Flying base stations, also known as aerial base stations, provide wireless connectivity to the user and utilize their aerial mobility to improve communication performance. Flying base stations depend on traditional stationary terrestrial base stations for connectivity, as stationary base stations act as the gateway to the backhaul/cloud via a wired connection. We introduce the flying base station channel capacity to build on the Shannon channel capacity, which quantifies the upper-bound limit of the rate at which information can be reliably transmitted using the communication channel regardless of the modulation and coding techniques used. The flying base station’s channel capacity assumes aerial mobility and ideal positioning for maximum channel capacity. Therefore, the channel capacity limit holds for any digital and signal processing technique used and for any location or positioning of the flying base station. Because of its inherent reliance on the stationary terrestrial base station, the flying base station channel capacity depends on the stationary base station’s parameters, such as its location and SNR performance to the user, in contrast to previous research, which focused on the link between the user and the flying base station without the stationary base station. For example, the beneficial region (where there is a positive flying base station capacity gain) depends on the stationary base station’s power and channel SNR in addition to the flying base station’s own transmission power and whether it has full duplex vs. half-duplex capability. We jointly study the mobility and the wireless communications of the flying base station to analyze its position, channel capacity, and beneficialness over the stationary terrestrial base station (capacity gain). As communication protocols and implementations for flying base stations undergo development for next-generation wireless networking, we focus on information-theoretical analyses and channel capacity to inform future research and development in flying base station networking.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122234
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2235: Research on the Gunn Oscillation Effect
           of GaN HEMT with Field Plate Structure in the Terahertz Frequency Band

    • Authors: Ruicong Yuan, Jiamin Wu, Lin Wang
      First page: 2235
      Abstract: Based on the enormous application potential of GaN-based high electron mobility transistors (HEMT) in high-frequency and high-power scenarios, this article focuses mainly on the study of the Gunn oscillation effect of GaN-based HEMT devices. From the perspective of electric field regulation, a sandwich structure GaN HEMT device model with field plate structure is proposed, and a hydrodynamic physical model is established. The negative resistance characteristics in the GaN HEMT are obtained by the finite element method and the influence of the gate field plate on the Gunn oscillation frequency in the device channel is studied. The numerical simulation results show that the suitable field plate structure can modulate the distribution of the channel electric field below the gate, promote the electric field to enter the negative differential mobility region, undergo valley to valley electron transfer, form electron domains, and generate the Gunn oscillation currents in the terahertz band. Meanwhile, the length of the field plate regulates the oscillation current frequency of the device, and the stable and usable terahertz frequency band signal can be realized. This research opens up the possibility for semiconductor solid-state devices to realize terahertz frequency band radiation, and provides the basis for realizing new breakthroughs in HEMT for terahertz applications.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122235
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2236: Analysis of DC-Link Low-Frequency
           Current Harmonics in Multi-Source Multi-Three-Phase Electric Drives

    • Authors: Yiyu Lai, Antoine Cizeron, Adrien Voldoire, Javier Ojeda, Olivier Béthoux
      First page: 2236
      Abstract: In a multi-source permanent magnet synchronous motor (PMSM) drive, three distinct winding structures can be implemented: multi-sector, multi-three-phase, and highly coupled. However, due to variations in the magnetic coupling between windings, their low-frequency DC-link current ripple components differ. This paper presents a method to identify the phenomena associated with each low-frequency harmonic content. Three analytical models are developed for the DC current ripple induced by unbalanced winding, counter-electromotive force (back-EMF) harmonics and aliasing effects, respectively, with the results validated through simulations. Experimental validation is conducted for highly coupled winding drives, demonstrating agreement with the analytical models and simulations. The maximum DC current ripple ratio found in the analytical model, the simulation and the experiments is less than 15%, which is deemed acceptable for motor drive applications.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122236
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2237: Zynerator: Bridging Model-Driven
           Architecture and Microservices for Enhanced Software Development

    • Authors: Younes Zouani, Mohamed Lachgar
      First page: 2237
      Abstract: Model-driven architecture (MDA) has demonstrated significant potential in automating code generation processes, yet its application often falls short in addressing the complexities of modern architectural styles, notably microservices. Microservice architecture, characterized by its decomposition of applications into small, independently deployable services, presents unique challenges and opportunities that traditional MDA approaches struggle to accommodate. In this paper, Zynerator, a novel framework that bridges the gap between model-driven architecture and microservice development, is presented. By integrating semantic decorators into the PIM, Zynerator empowers end-users to express intricate functional and non-functional requirements, laying the foundation for the generation of contextually appropriate code. Moreover, Zynerator goes beyond traditional MDA capabilities by offering a solution for microservice architecture integration, enabling the generation of service gateways, service discovery mechanisms, and other essential components inherent to microservice ecosystems. This integration not only streamlines the development process but also ensures the scalability, resilience, and maintainability of microservice-based applications. Through Zynerator, a flexible and comprehensive solution is presented that leverages the strengths of model-driven architecture (MDA), while addressing the evolving needs of modern software architecture, particularly in the realm of microservice development. Empirical results showed that Zynerator enhances code generation alignment to functional requirements by 55%, reduces microservice adoption in terms of communication and deployment times by 30%, and increases system scalability by supporting up to 10,000 concurrent users, without performance degradation.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122237
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2238: Ka-Band Wide-Angle Scanning Phased Array
           with Dual Circular Polarization

    • Authors: Lei Zhang, Jianyong Yin
      First page: 2238
      Abstract: A wide-angle scanning phased array with dual circular polarization in the Ka-band is presented in this paper. To improve the scanning capability of the phased array, the microstrip element is modified by loading many metal posts at its center and periphery. In addition, a stripline coupler is designed to achieve dual circularly polarized (CP) radiation, and the inner conductor of the subminiature micro-push-on (SSMP) connectors for feeding the coupler is extended to the top layer of the multilayer element by introducing an open stub, which simplifies the assembly process between the SSMP connector and multilayer printed circuit board (PCB) due to through-hole soldering instead of blind-hole soldering. The proposed element can cover a frequency range from 28 GHz to 30.5 GHz with a relative bandwidth of 8.5% in the Ka-band. An 8 × 8 phased array is constructed based on this proposed element, and a wide-angle scanning range from −65° to +65° is obtained for the dual circular polarization. The proposed array has a gain fluctuation of 5.1 dB and an axial ratio (AR) of less than 3.3 dB during beam-steering. The prototype is fabricated and measured, with a good agreement between the measured and simulated results. The proposed phased array can be applied in a Ka-band millimeter-wave (MMW) communication system.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122238
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2239: Path Planning Techniques for Real-Time
           Multi-Robot Systems: A Systematic Review

    • Authors: Nour AbuJabal, Tamer Rabie, Mohammed Baziyad, Ibrahim Kamel, Khawla Almazrouei
      First page: 2239
      Abstract: A vast amount of research has been conducted on path planning over recent decades, driven by the complexity of achieving optimal solutions. This paper reviews multi-robot path planning approaches and presents the path planning algorithms for various types of robots. Multi-robot path planning approaches have been classified as deterministic approaches, artificial intelligence (AI)-based approaches, and hybrid approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. However, multi-robot systems suffer from well-known problems such as the number of robots in the system, energy efficiency, fault tolerance and robustness, and dynamic targets. Deploying systems with multiple interacting robots offers numerous advantages. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot systems.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122239
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2240: IFSrNet: Multi-Scale IFS Feature-Guided
           Registration Network Using Multispectral Image-to-Image Translation

    • Authors: Bowei Chen, Li Chen, Umara Khalid, Shuai Zhang
      First page: 2240
      Abstract: Multispectral image registration is the process of aligning the spatial regions of two images with different distributions. One of the main challenges it faces is to resolve the severe inconsistencies between the reference and target images. This paper presents a novel multispectral image registration network, Multi-scale Intuitionistic Fuzzy Set Feature-guided Registration Network (IFSrNet), to address multispectral image registration. IFSrNet generates pseudo-infrared images from visible images using Cycle Generative Adversarial Network (CycleGAN), which is equipped with a multi-head attention module. An end-to-end registration network encodes the input multispectral images with intuitionistic fuzzification, which employs an improved feature descriptor—Intuitionistic Fuzzy Set–Scale-Invariant Feature Transform (IFS-SIFT)—to guide its operation. The results of the image registration will be presented in a direct output. For this task we have also designed specialised loss functions. The results of the experiment demonstrate that IFSrNet outperforms existing registration methods in the Visible–IR dataset. IFSrNet has the potential to be employed as a novel image-to-image translation paradigm.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122240
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2241: Applying “Two Heads Are Better
           Than One” Human Intelligence to Develop Self-Adaptive Algorithms for
           Ridesharing Recommendation Systems

    • Authors: Fu-Shiung Hsieh
      First page: 2241
      Abstract: Human beings have created numerous laws, sayings and proverbs that still influence behaviors and decision-making processes of people. Some of the laws, sayings or proverbs are used by people to understand the phenomena that may take place in daily life. For example, Murphy’s law states that “Anything that can go wrong will go wrong.” Murphy’s law is helpful for project planning with analysis and the consideration of risk. Similar to Murphy’s law, the old saying “Two heads are better than one” also influences the determination of the ways for people to get jobs done effectively. Although the old saying “Two heads are better than one” has been extensively discussed in different contexts, there is a lack of studies about whether this saying is valid and can be applied in evolutionary computation. Evolutionary computation is an important optimization approach in artificial intelligence. In this paper, we attempt to study the validity of this saying in the context of evolutionary computation approach to the decision making of ridesharing systems with trust constraints. We study the validity of the saying “Two heads are better than one” by developing a series of self-adaptive evolutionary algorithms for solving the optimization problem of ridesharing systems with trust constraints based on the saying, conducting several series of experiments and comparing the effectiveness of these self-adaptive evolutionary algorithms. The new finding is that the old saying “Two heads are better than one” is valid in most cases and hence can be applied to facilitate the development of effective self-adaptive evolutionary algorithms. Our new finding paves the way for developing a better evolutionary computation approach for ridesharing recommendation systems based on sayings created by human beings or human intelligence.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122241
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2242: Feature Selection for Data
           Classification in the Semiconductor Industry by a Hybrid of Simplified
           Swarm Optimization

    • Authors: Yeh, Chu
      First page: 2242
      Abstract: In the semiconductor manufacturing industry, achieving high yields constitutes one of the pivotal factors for sustaining market competitiveness. When confronting the substantial volume of high-dimensional, non-linear, and imbalanced data generated during semiconductor manufacturing processes, it becomes imperative to transcend traditional approaches and incorporate machine learning methodologies. By employing non-linear classification models, one can achieve more real-time anomaly detection, subsequently facilitating a deeper analysis of the fundamental causes behind anomalies. Given the considerable dimensionality of production line data in semiconductor manufacturing, there arises a necessity for dimensionality reduction to mitigate noise and reduce computational costs within the data. Feature selection stands out as one of the primary methodologies for achieving data dimensionality reduction. Utilizing wrapper-based heuristics algorithms, although characterized by high time complexity, often yields favorable performance in specific cases. If further combined into hybrid methodologies, they can concurrently satisfy data quality and computational cost considerations. Accordingly, this study proposes a two-stage feature selection model. Initially, redundant features are eliminated using mutual information to reduce the feature space. Subsequently, a Simplified Swarm Optimization algorithm is employed to design a unique fitness function aimed at selecting the optimal feature subset from candidate features. Finally, support vector machines are utilized as the classification model for validation purposes. For practical cases, it is evident that the feature selection method proposed in this study achieves superior classification accuracy with fewer features in the context of wafer anomaly classification problems. Furthermore, its performance on public datasets further substantiates the effectiveness and generalization capability of the proposed approach.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122242
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2243: An Efficient Transformer–CNN
           Network for Document Image Binarization

    • Authors: Lina Zhang, Kaiyuan Wang, Yi Wan
      First page: 2243
      Abstract: Color image binarization plays a pivotal role in image preprocessing work and significantly impacts subsequent tasks, particularly for text recognition. This paper concentrates on document image binarization (DIB), which aims to separate an image into a foreground (text) and background (non-text content). We thoroughly analyze conventional and deep-learning-based approaches and conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields of pre- and post-network training to underscore the Transformer model’s advantages. Subsequently, we introduce a lightweight model based on the U-Net structure and enhanced with the MobileViT module to capture global information features in document images better. Given its adeptness at learning both local and global features, our proposed model demonstrates competitive performance on two standard datasets (DIBCO2012 and DIBCO2017) and good robustness on the DIBCO2019 dataset. Notably, our proposed method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing, eschewing the use of ensemble models. Moreover, its parameter count is less than one-eighth of the model, which achieves the best results on most DIBCO datasets. Finally, two sets of ablation experiments are conducted to verify the effectiveness of the proposed binarization model.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122243
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2244: The Impact of Grid Distortion on the
           

    • Authors: Marwa S. Osheba, Abdellatif M. Aboutaleb, Jan Desmet, Jos Knockaert
      First page: 2244
      Abstract: AC/DC converters, controlled by pulse width modulation (PWM) and used as power factor correction (PFC), is considered one of the main contributors to emissions in the range 2 kHz–150 kHz, recently known as the supraharmonic (SH) range. This study looks at the impact of SH grid distortion on the LF (<2 kHz) and HF (>2 kHz) emission of an AC/DC converter. The PFC boost converter is used as a particular case for validation of the results. It is observed that the AC/DC converters emit additional LF interharmonics and subharmonics when the grid voltage contains interharmonic components in the SH range. A mathematical analysis is provided to study and assess the interference between the SH in the background distortion and the AC/DC converters. Experimental studies are then performed for a PFC boost setup based on dSPACE MicroLabBox for the purposes of validating the mathematical analysis.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122244
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2245: Wearable Loops for Dynamic Monitoring of
           Joint Flexion: A Machine Learning Approach

    • Authors: Henry Saltzman, Rahul Rajaram, Yingzhe Zhang, Md Asiful Islam, Asimina Kiourti
      First page: 2245
      Abstract: We present a machine learning driven system to monitor joint flexion angles during dynamic motion, using a wearable loop-based sensor. Our approach uses wearable loops to collect transmission coefficient data and an Artificial Neural Network (ANN) with fine-tuned parameters to increase accuracy of the measured angles. We train and validate the ANN for sagittal plane flexion of a leg phantom emulating slow motion, walking, brisk walking, and jogging. We fabricate the loops on conductive threads and evaluate the effect of fabric drift via measurements in the absence and presence of fabric. In the absence of fabric, our model produced a root mean square error (RMSE) of 5.90°, 6.11°, 5.90°, and 5.44° during slow motion, walking, brisk walking, and jogging. The presence of fabric degraded the RMSE to 8.97°, 7.21°, 9.41°, and 7.79°, respectively. Without the proposed ANN method, errors exceeded 35.07° for all scenarios. Proof-of-concept results on three human subjects further validate this performance. Our approach empowers feasibility of wearable loop sensors for motion capture in dynamic, real-world environments. Increasing speed of motion and the presence of fabric degrade sensor performance due to added noise. Nevertheless, the proposed framework is generalizable and can be expanded upon in the future to improve upon the reported angular resolution.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122245
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2246: Detecting Logos for Indoor Environmental
           Perception Using Unsupervised and Few-Shot Learning

    • Authors: Changjiang Yin, Qin Ye, Shaoming Zhang, Zexin Yang
      First page: 2246
      Abstract: Indoor scenes are crucial components of urban spaces, with logos serving as vital information within these environments. The accurate perception of logos is essential for effectively operating mobile robots in indoor environments, which significantly contributes to many upper-level applications. With the rapid development of neural networks, numerous deep-learning-based object-detection methods have been applied to logo detection. However, most of these methods depend on large labeled datasets. Given the fast-changing nature of logos in indoor scenes, achieving reliable detection performance with either the existing large labeled datasets or a limited number of labeled logos remains challenging. In this article, we propose a method named MobileNetV2-YOLOv4-UP, which integrates unsupervised learning with few-shot learning for logo detection. We develop an autoencoder to obtain latent feature representations of logos by pre-training on a public unlabeled logo dataset. Subsequently, we construct a lightweight logo-detection network and embed the encoder weights as prior information. Training is performed on a small dataset of labeled indoor-scene logos to update the weights of the logo-detection network. Experimental results on the public logo625 dataset and our self-collected LOGO2000 dataset demonstrate that our method outperforms classic object-detection methods, achieving a mean average detection precision of 83.8%. Notably, our unsupervised pre-training strategy (UP) has proven effective, delivering a 15.4% improvement.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122246
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2247: Continuous Recording of Resonator
           Characteristics Using Single-Sideband Modulation

    • Authors: Martin Lippmann, Moritz Hitzemann, Leonardo Hermeling, Kirsten J. Dehning, Jonas Winkelholz, Rene Wantosch, Stefan Zimmermann
      First page: 2247
      Abstract: Electrical resonators are usually characterized by their resonance frequency, attenuation and quality factor. External quantities can affect these parameters, resulting in a characteristic change in the resonator, which can be used as a sensor effect. This work presents a new concept and electronic device for the continuous recording of resonator characteristics using single-sideband modulation. A test signal consisting of a center frequency and two sidebands is generated and the center frequency is set close to the resonator’s resonance frequency while the two sidebands are adjusted symmetrically around the center frequency. By exiting the resonator with the test signal and demodulating the resulting output into individual frequency components, a continuous measurement of the attenuation is possible. The center frequency is adjusted so that both sidebands have equal attenuation, resulting in a center frequency that corresponds to the resonance frequency of the resonator. If the resonator does not show a symmetrical frequency response, the sideband attenuation ratio can be adjusted accordingly. Continuous recording of the resonator characteristics at a sampling rate of 100 Sps was verified using a digitally tunable RLC series resonator with resonance frequencies between 250 MHz and 450 MHz, resulting in a maximum error below 1.5%.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122247
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2248: Diagnosis Aid System for Colorectal
           Cancer Using Low Computational Cost Deep Learning Architectures

    • Authors: Álvaro Gago-Fabero, Luis Muñoz-Saavedra, Javier Civit-Masot, Francisco Luna-Perejón, José María Rodríguez Corral, Manuel Domínguez-Morales
      First page: 2248
      Abstract: Colorectal cancer is the second leading cause of cancer-related deaths worldwide. To prevent deaths, regular screenings with histopathological analysis of colorectal tissue should be performed. A diagnostic aid system could reduce the time required by medical professionals, and provide an initial approach to the final diagnosis. In this study, we analyze low computational custom architectures, based on Convolutional Neural Networks, which can serve as high-accuracy binary classifiers for colorectal cancer screening using histopathological images. For this purpose, we carry out an optimization process to obtain the best performance model in terms of effectiveness as a classifier and computational cost by reducing the number of parameters. Subsequently, we compare the results obtained with previous work in the same field. Cross-validation reveals a high robustness of the models as classifiers, yielding superior accuracy outcomes of 99.4 ± 0.58% and 93.2 ± 1.46% for the lighter model. The classifiers achieved an accuracy exceeding 99% on the test subset using low-resolution images and a significantly reduced layer count, with images sized at 11% of those used in previous studies. Consequently, we estimate a projected reduction of up to 50% in computational costs compared to the most lightweight model proposed in the existing literature.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122248
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2249: Advanced Primary Frequency Regulation
           Optimization in Wind Storage Systems with DC Integration Using Double Deep
           Q-Networks

    • Authors: Xiaojiang Liu, Peng Zou, Jin You, Yuhong Wang, Jiabao Wu, Zongsheng Zheng, Shilin Gao, Wei Hao
      First page: 2249
      Abstract: With the gradual increase in wind power installation capacity, the proportion of traditional synchronous generators driven by fossil fuel is gradually declining. Due to the fact that wind turbines are connected to the grid through power electronic converters, which decouple rotor speeds from the system frequency and reduce system inertia levels, inadequate inertia levels can pose a threat to frequency stability when disturbances occur. To address this issue, this paper proposes a frequency regulation optimization strategy for the direct current (DC) transmission of a wind storage system. This strategy incorporates virtual inertia control and virtual droop control to adjust wind power output based on frequency deviation and rate of change. Fuzzy logic control is employed for energy storage, adaptively adjusting active power based on frequency deviation and the rate of change. Additionally, under the context of multi-DC transmission in renewable energy systems, an optimization strategy for proportion and integration (PI) parameters of the frequency limit controller (FLC) is proposed. Considering frequency deviation and DC regulation power simultaneously, the double deep Q-network (DDQN) algorithm is adopted in the simulation model to attain the optimal parameters of FLC. Simulation results conducted using MATLAB/Simulink 2022a indicate that this strategy increases the lowest frequency by 0.28 Hz and decreases the response time by 1.04 s compared with the non-optimized strategy.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122249
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2250: An Advanced Approach to Object Detection
           and Tracking in Robotics and Autonomous Vehicles Using YOLOv8 and LiDAR
           Data Fusion

    • Authors: Yanyan Dai, DeokGyu Kim, KiDong Lee
      First page: 2250
      Abstract: Accurately and reliably perceiving the environment is a major challenge in autonomous driving and robotics research. Traditional vision-based methods often suffer from varying lighting conditions, occlusions, and complex environments. This paper addresses these challenges by combining a deep learning-based object detection algorithm, YOLOv8, with LiDAR data fusion technology. The principle of this combination is to merge the advantages of these technologies: YOLOv8 excels in real-time object detection and classification through RGB images, while LiDAR provides accurate distance measurement and 3D spatial information, regardless of lighting conditions. The integration aims to apply the high accuracy and robustness of YOLOv8 in identifying and classifying objects, as well as the depth data provided by LiDAR. This combination enhances the overall environmental perception, which is critical for the reliability and safety of autonomous systems. However, this fusion brings some research challenges, including data calibration between different sensors, filtering ground points from LiDAR point clouds, and managing the computational complexity of processing large datasets. This paper presents a comprehensive approach to address these challenges. Firstly, a simple algorithm is introduced to filter out ground points from LiDAR point clouds, which are essential for accurate object detection, by setting different threshold heights based on the terrain. Secondly, YOLOv8, trained on a customized dataset, is utilized for object detection in images, generating 2D bounding boxes around detected objects. Thirdly, a calibration algorithm is developed to transform 3D LiDAR coordinates to image pixel coordinates, which are vital for correlating LiDAR data with image-based object detection results. Fourthly, a method for clustering different objects based on the fused data is proposed, followed by an object tracking algorithm to compute the 3D poses of objects and their relative distances from a robot. The Agilex Scout Mini robot, equipped with Velodyne 16-channel LiDAR and an Intel D435 camera, is employed for data collection and experimentation. Finally, the experimental results validate the effectiveness of the proposed algorithms and methods.
      Citation: Electronics
      PubDate: 2024-06-07
      DOI: 10.3390/electronics13122250
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2251: Textile Antenna with Dual Bands and SAR
           Measurements for Wearable Communication

    • Authors: Mahmoud A. Abdelghany, Mohamed I. Ahmed, Ahmed A. Ibrahim, Arpan Desai, Mai. F. Ahmed
      First page: 2251
      Abstract: A novel dual-wideband textile antenna designed for wearable applications is introduced in this study. Embedding antennas into wearable devices requires a detailed analysis of the specific absorption rate (SAR) to ensure safety. To achieve this, SAR values were meticulously simulated and evaluated within a human voxel model, considering various body regions such as the left/right head and the abdominal region. The proposed antenna is a monopole design utilizing denim textile as the substrate material. The characterization of the denim textile substrate is carried out using two different methods. The first analysis included a DAC (Dielectric Assessment Kit), while a ring resonator technique was employed for the second examination. Operating within the frequency bands of (58.06%) 2.2–4 GHz and (61.43) 5.3–10 GHz, the antenna demonstrated flexibility in its dual-wideband capabilities. Extensive simulations and tests were conducted to assess the performance of the antenna in both flat and bent configurations. The SAR results obtained from these tests indicate that the antenna complies with safety standard limits when integrated with the human voxel model. This validation underscores the potential of the proposed antenna for seamless integration into wearable applications, offering a promising solution for future developments in this domain.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122251
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2252: Design of a Switching Strategy for
           Output Voltage Tracking Control in a DC-DC Buck Power Converter

    • Authors: Eduardo Hernández-Márquez, Panuncio Cruz-Francisco, Eric Hernández-Castillo, Dulce Martinez-Peón, Rafael Castro-Linares, José Rafael García-Sánchez, Alfredo Roldán-Caballero, Xóchitl Siordia-Vásquez, Juan Carlos Valdivia-Corona
      First page: 2252
      Abstract: This work proposes the design of a commutation function to solve the output voltage trajectory tracking problem in the DC-DC Buck power electronic converter. Through a Lyapunov-type analysis, sufficient conditions are established, taking into account the discontinuous model, to ensure asymptotic convergence to the desired trajectories. Based on this analysis, a state-dependent switching function was designed to guarantee the closed-loop stability of the tracking error. To validate the control performance, circuit numerical simulations were carried out under abrupt disturbances in the source and load of the converter. The results demonstrate that the voltage tracking at the output of the converter is satisfactorily achieved.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122252
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2253: Artificial Intelligence, Immersive
           Technologies, and Neurotechnologies in Breathing Interventions for Mental
           and Emotional Health: A Systematic Review

    • Authors: Eleni Mitsea, Athanasios Drigas, Charalabos Skianis
      First page: 2253
      Abstract: Breathing is one of the most vital functions for being mentally and emotionally healthy. A growing number of studies confirm that breathing, although unconscious, can be under voluntary control. However, it requires systematic practice to acquire relevant experience and skillfulness to consciously utilize breathing as a tool for self-regulation. After the COVID-19 pandemic, a global discussion has begun about the potential role of emerging technologies in breath-control interventions. Emerging technologies refer to a wide range of advanced technologies that have already entered the race for mental health training. Artificial intelligence, immersive technologies, biofeedback, non-invasive neurofeedback, and other wearable devices provide new, but yet underexplored, opportunities in breathing training. Thus, the current systematic review examines the synergy between emerging technologies and breathing techniques for improving mental and emotional health through the lens of skills development. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is utilized to respond to the objectives and research questions. The potential benefits, possible risks, ethical concerns, future directions, and implications are also discussed. The results indicated that digitally assisted breathing can improve various aspects of mental health (i.e., attentional control, emotional regulation, mental flexibility, stress management, and self-regulation). A significant finding of this review indicated that the blending of different technologies may maximize training outcomes. Thus, future research should focus on the proper design and evaluation of different digital designs in breathing training to improve health in different populations. This study aspires to provide positive feedback in the discussion about the role of digital technologies in assisting mental and emotional health-promoting interventions among populations with different needs (i.e., employees, students, and people with disabilities).
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122253
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2254: A Simple Scan Driver Circuit Suitable
           for Depletion-Mode Metal-Oxide Thin-Film Transistors in Active-Matrix
           Displays

    • Authors: Yikyoung You, Junhyung Lim, Kyoungseok Son, Jaybum Kim, Youngoo Kim, Kyunghoe Lee, Kyunghoon Chung, Keechan Park
      First page: 2254
      Abstract: Metal-oxide (MOx) thin-film transistors (TFTs) require complex circuit structures to cope with their depletion mode characteristics, making them applicable only to large-area active matrix (AM) displays despite their low manufacturing cost and decent performance. In this paper, we report a simple MOx 10T-2C scan driver circuit that overcomes the depletion mode characteristics using a series-connected two transistor (STT) configuration and clock signals with two kinds of low-voltage levels. The proposed circuit has a wide operating range of TFT characteristics, i.e., −2.8 V ≤ VTH ≤ +3.0 V. Through the measurement results of the manufactured sample, it was confirmed that the performance and area of our circuit are suitable for high-resolution mobile displays.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122254
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2255: Improving Training Dataset Balance with
           ChatGPT Prompt Engineering

    • Authors: Mateusz Kochanek, Igor Cichecki, Oliwier Kaszyca, Dominika Szydło, Michał Madej, Dawid Jędrzejewski, Przemysław Kazienko, Jan Kocoń
      First page: 2255
      Abstract: The rapid evolution of large language models, in particular OpenAI’s GPT-3.5-turbo and GPT-4, indicates a growing interest in advanced computational methodologies. This paper proposes a novel approach to synthetic data generation and knowledge distillation through prompt engineering. The potential of large language models (LLMs) is used to address the problem of unbalanced training datasets for other machine learning models. This is not only a common issue but also a crucial determinant of the final model quality and performance. Three prompting strategies have been considered: basic, composite, and similarity prompts. Although the initial results do not match the performance of comprehensive datasets, the similarity prompts method exhibits considerable promise, thus outperforming other methods. The investigation of our rebalancing methods opens pathways for future research on leveraging continuously developed LLMs for the enhanced generation of high-quality synthetic data. This could have an impact on many large-scale engineering applications.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122255
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2256: Localization of Coordinated
           Cyber-Physical Attacks in Power Grids Using Moving Target Defense and
           Machine Learning

    • Authors: Jian Yu, Qiang Li, Lei Li
      First page: 2256
      Abstract: Coordinated cyber-physical attacks (CCPAs) are dangerously stealthy and have considerable destructive effects against power grids. The problem of stealthy CCPA (SCCPA) localization, specifically identifying disconnected lines in attack, is a nonlinear multi-classification problem. To the best of our knowledge, only one paper has studied the problem; nevertheless, the total number of classifications is not appropriate. In the paper, we propose several methods to solve the problem of SCCPA localization. Firstly, considering the practical constraints and abiding by one of our previous studies, we elaborately determine the total number of classifications and design an approach for generating training and testing datasets. Secondly, we develop two algorithms to solve multiple classifications via the support vector machine (SVM) and random forest (RF), respectively. Similarly, we also present a one-dimensional convolutional neural network (1D-CNN) architecture. Finally, extensive simulations are carried out for IEEE 14-bus, 30-bus, and 118-bus power system, respectively, and we verify the effectiveness of our approaches in solving the problem of SCCPA localization.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122256
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2257: A Road Crack Segmentation Method Based
           on Transformer and Multi-Scale Feature Fusion

    • Authors: Yang Xu, Yonghua Xia, Quai Zhao, Kaihua Yang, Qiang Li
      First page: 2257
      Abstract: To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous potential in improving the efficiency of road crack detection. While convolutional neural networks have proven effective in most semantic segmentation tasks, overcoming their limitations in road crack segmentation remains a challenge. To address this, this paper proposes a novel road crack segmentation network that leverages the powerful spatial feature modeling capabilities of Swin Transformer and the Encoder–Decoder architecture of DeepLabv3+. Additionally, the incorporation of a multi-scale coding module and attention mechanism enhances the network’s ability to densely fuse multi-scale features and expand the receptive field, thereby improving the integration of information from feature maps. Performance comparisons with current mainstream semantic segmentation models on crack datasets demonstrate that the proposed model achieves the best results, with an MIoU of 81.06%, Precision of 79.95%, and F1-score of 77.56%. The experimental results further highlight the model’s superior ability in identifying complex and irregular cracks and extracting contours, providing guidance for future applications in this field.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122257
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2258: Digital Twins in Industry 4.0

    • Authors: Sangchan Park, Sira Maliphol, Jiyoung Woo, Liu Fan
      First page: 2258
      Abstract: Since Grieves [...]
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122258
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2259: An Overview of Electric Vehicle Load
           Modeling Strategies for Grid Integration Studies

    • Authors: Anny Huaman-Rivera, Ricardo Calloquispe-Huallpa, Adriana C. Luna Luna Hernandez, Agustin Irizarry-Rivera
      First page: 2259
      Abstract: The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122259
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2260: SPICE-Compatible Circuit Models of
           Multiports Described by Scattering Parameters with Arbitrary Reference
           Impedances

    • Authors: Marek Nałęcz
      First page: 2260
      Abstract: New SPICE-compatible circuit models of a multiport are presented here that are suitable for the frequency-domain and time-domain analyses of hybrid systems containing linear distributed elements and possibly non-linear lumped elements. Distributed elements models are based on scattering parameters with potentially complex reference impedances, which are not necessarily equal for all ports. Both exact and approximated (lumped) models are proposed. The scattering parameters are directly taken as the model element values in the former case. In the latter case, the model element values are equal to the real and imaginary parts of the poles and residues of the rational approximation. The models comprise a multiport (with an admittance matrix numerically equal to the modeled scattering matrix or approximating it) equipped with a pair of coupled impedances at each port. A few examples validate the proposed approach and prove its efficiency in terms of matrix size and analysis time compared to some selected commercial counterparts.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122260
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2261: Analysis of Vulnerabilities in College
           Web-Based System

    • Authors: Younsu Nam, Sunoh Choi
      First page: 2261
      Abstract: Web-based systems are used extensively in Korea because web standards have been adapted by the law (e.g., Electronic Government Act). Users can easily access web-based systems if they are connected to the Internet. However, distinguishing between malicious and benign access is very difficult and many potential vulnerabilities exist. In this study, we attempt to leak the information of other users without permission using a non-encrypted API and web source code analysis in a college web-based system. An experiment demonstrates that the information (e.g., other students’ course grades) can be leaked and abnormal data can be embedded in the request. In addition, we discuss methods for preventing such vulnerability attacks.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122261
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2262: Folded Narrow-Band and Wide-Band
           Monopole Antennas with In-Plane and Vertical Grounds for Wireless Sensor
           Nodes in Smart Home IoT Applications

    • Authors: Mohammad Mahdi Honari, Seyed Parsa Javadi, Rashid Mirzavand
      First page: 2262
      Abstract: This article presents two monopole antennas with an endfire radiation pattern in the UHF band that can be installed on dry walls or metallic cabinets as a part of wireless sensor nodes, making them a suitable choice for smart home applications, such as the wireless remote control of house appliances. Two different antennas are proposed to cover the RFID bands of North America (902–928 MHz) and worldwide (860–960 MHz). The antennas have wide horizontal radiation patterns that provide great reading coverage in their communication with a base station placed at a certain distance from the antennas. The structures have two ground planes, one in-plane and the other vertical. The vertical ground helps the antenna to have a directive radiation and also makes it easily installed on walls. The antenna feeding line lies over the vertical ground substrate. The maximum dimensions of the narrow-band antenna are L × W = 0.3λ× 0.14λ, and those for the wide-band antenna are L × W = 0.39λ× 0.14λ. The measured results show that the bandwidth of the proposed antennas for the North America and worldwide RFID bands are from 902 MHz to 939 MHz and 822 MHz to 961 MHz, with maximum gains of 4.2 dBi and 4.9 dBi, respectively.
      Citation: Electronics
      PubDate: 2024-06-08
      DOI: 10.3390/electronics13122262
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2263: Privacy Essentials

    • Authors: James Taylor, Jane Henriksen-Bulmer, Cagatay Yucel
      First page: 2263
      Abstract: Following a series of legislative changes around privacy over the past 25 years, this study highlights data protection regulations and the complexities of applying these frameworks. To address this, we created a privacy framework to guide organisations in what steps they need to undertake to achieve compliance with the UK GDPR, highlighting the existing privacy frameworks for best practice and the requirements from the Information Commissioners Office. We applied our framework to a UK charity sector; to account for the specific nuances that working in a charity brings, we worked closely with local charities to understand their requirements, and interviewed privacy experts to develop a framework that is readily accessible and provides genuine value. Feeding the results into our privacy framework, a decision tree artefact has been developed for compliance. The artefact has been tested against black-box tests, System Usability Tests and UX Honeycomb tests. Results show that Privacy Essentials! provides the foundation of a data protection management framework and offers organisations the catalyst to start, enhance, or even validate a solid and effective data privacy programme.
      Citation: Electronics
      PubDate: 2024-06-09
      DOI: 10.3390/electronics13122263
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2264: Full-Duplex Unmanned Aerial Vehicle
           Communications for Cellular Spectral Efficiency Enhancement Utilizing
           Device-to-Device Underlaying Structure

    • Authors: Yuetian Zhou, Yang Li
      First page: 2264
      Abstract: Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One significant advantage of UAVs is their ability to partition suitable D2D pairs to increase cell capacity. In this paper, we present a novel network model in which UAVs are considered D2D pairs underlaying cellular networks, integrating FD into the communication links between UAVs to improve spectral efficiency. We then investigate a resource allocation problem for the proposed FD-UAV D2D underlaying structure model, with the objective of maximizing the system’s sum rate. Specifically, the UAVs in our model operate in full-duplex mode as D2D users (DUs), allowing the reuse of both the uplink and downlink subcarrier resources of cellular users (CUs). This optimization challenge is formulated as a mixed-integer nonlinear programming problem, known for its NP-hard and intractable nature. To address this issue, we propose a heuristic algorithm (HA) that decomposes the problem into two steps: power allocation and user pairing. The optimal power allocation is solved as a nonlinear programming problem by searching among a finite set, while the user pairing problem is addressed using the Kuhn–Munkres algorithm. The numerical results indicate that our proposed FD-MaxSumCell-HA (full-duplex UAVs maximizing the cell sum rate with a heuristic algorithm) scheme for FD-UAV D2D underlaying models outperforms HD-UAV underlaying cellular networks, with improved access rates for UAVs in FD-MaxSumCell-HA compared to HD-UAV networks.
      Citation: Electronics
      PubDate: 2024-06-09
      DOI: 10.3390/electronics13122264
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2265: SIDGAN: Efficient Multi-Module
           Architecture for Single Image Defocus Deblurring

    • Authors: Shenggui Ling, Hongmin Zhan, Lijia Cao
      First page: 2265
      Abstract: In recent years, with the rapid developments in deep learning and graphics processing units, learning-based defocus deblurring has made favorable achievements. However, the current methods are not effective in processing blurred images with a large depth of field. The greater the depth of field, the blurrier the image, namely, the image contains large blurry regions and encounters severe blur. The fundamental reason for the unsatisfactory results is that it is difficult to extract effective features from the blurred images with large blurry regions. For this reason, a new FFEM (Fuzzy Feature Extraction Module) is proposed to enhance the encoder’s ability to extract features from images with large blurry regions. After using the FFEM during encoding, its PSNR (Peak Signal-to-Noise Ratio) is improved by 1.33% on the DPDD (Dual-Pixel Defocus Deblurring). Moreover, images with large blurry regions often cause the current algorithms to generate artifacts in their results. Therefore, a new module named ARM (Artifact Removal Module) is proposed in this work and employed during decoding. After utilizing the ARM during decoding, its PSNR is improved by 2.49% on the DPDD. After using the FFEM and the ARM simultaneously, compared to the latest algorithms, the PSNR of our method is improved by 3.29% on the DPDD. Following the previous research in this field, qualitative and quantitative experiments are conducted on the DPDD and the RealDOF (Real Depth of Field), and the experimental results indicate that our method surpasses the state-of-the-art algorithms in three objective metrics.
      Citation: Electronics
      PubDate: 2024-06-09
      DOI: 10.3390/electronics13122265
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2266: Designing Multifunctional Multiferroic
           Composites for Advanced Electronic Applications

    • Authors: Lilian Nunes Pereira, Julio Cesar Agreira Pastoril, Gustavo Sanguino Dias, Ivair Aparecido dos Santos, Ruyan Guo, Amar S. Bhalla, Luiz Fernando Cotica
      First page: 2266
      Abstract: This paper presents a novel approach for the fabrication of magnetoelectric composites aimed at enhancing cross-coupling between electrical and magnetic phases for potential applications in intelligent sensors and electronic components. Unlike previous methodologies known for their complexity and expense, our method offers a simple and cost-effective assembly process conducted at room temperature, preserving the original properties of the components and avoiding undesired phases. The composites, composed of PZT fibers, cobalt (CoFe2O4), and a polymeric resin, demonstrate the uniform distribution of PZT-5A fibers within the cobalt matrix, as demonstrated by scanning electron microscopy. Detailed morphological analyses reveal the interface characteristics crucial for determining overall performance. Dielectric measurements indicate stable behaviors, particularly when PZT-5A fibers are properly poled, showcasing potential applications in sensors or medical devices. Furthermore, H-dependence studies illustrate strong magnetoelectric interactions, suggesting promising avenues for enhancing coupling efficiency. Overall, this study lays the basic work for future optimization of composite composition and exploration of its long-term stability, offering valuable insights into the potential applications of magnetoelectric composites in various technological domains.
      Citation: Electronics
      PubDate: 2024-06-09
      DOI: 10.3390/electronics13122266
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2267: Fusing Design and Machine Learning for
           Anomaly Detection in Water Treatment Plants

    • Authors: Gauthama Raman, Aditya Mathur
      First page: 2267
      Abstract: Accurate detection of process anomalies is crucial for maintaining reliable operations in critical infrastructures such as water treatment plants. Traditional methods for creating anomaly detection systems in these facilities typically focus on either design-based strategies, which encompass physical and engineering aspects, or on data-driven models that utilize machine learning to interpret complex data patterns. Challenges in creating these detectors arise from factors such as dynamic operating conditions, lack of design knowledge, and the complex interdependencies among heterogeneous components. This paper proposes a novel fusion detector that combines the strengths of both design-based and machine learning approaches for accurate detection of process anomalies. The proposed methodology was implemented in an operational secure water treatment (SWaT) testbed, and its performance evaluated during the Critical Infrastructure Security Showdown (CISS) 2022 event. A comparative analysis against four commercially available anomaly detection systems that participated in the CISS 2022 event revealed that our fusion detector successfully detected 19 out of 22 attacks, demonstrating high accuracy with a low rate of false positives.
      Citation: Electronics
      PubDate: 2024-06-09
      DOI: 10.3390/electronics13122267
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2268: Advanced Wireless Sensor Networks:
           Applications, Challenges and Research Trends

    • Authors: Dionisis Kandris, Eleftherios Anastasiadis
      First page: 2268
      Abstract: A typical wireless sensor network (WSN) contains wirelessly interconnected devices, called sensor nodes, which have sensing, processing, and communication abilities and are disseminated within an area of interest [...]
      Citation: Electronics
      PubDate: 2024-06-09
      DOI: 10.3390/electronics13122268
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2269: Polarization-Based Two-Stage Image
           Dehazing in a Low-Light Environment

    • Authors: Xin Zhang, Xia Wang, Changda Yan, Gangcheng Jiao, Huiyang He
      First page: 2269
      Abstract: Fog, as a common weather condition, severely affects the visual quality of images. Polarization-based dehazing techniques can effectively produce clear results by utilizing the atmospheric polarization transmission model. However, current polarization-based dehazing methods are only suitable for scenes with strong illumination, such as daytime scenes, and cannot be applied to low-light scenes. Due to the insufficient illumination at night and the differences in polarization characteristics between it and sunlight, polarization images captured in a low-light environment can suffer from loss of polarization and intensity information. Therefore, this paper proposes a two-stage low-light image dehazing method based on polarization. We firstly construct a polarization-based low-light enhancement module to remove noise interference in polarization images and improve image brightness. Then, we design a low-light polarization dehazing module, which combines the polarization characteristics of the scene and objects to remove fog, thereby restoring the intensity and polarization information of the scene and improving image contrast. For network training, we generate a simulation dataset for low-light polarization dehazing. We also collect a low-light polarization hazy dataset to test the performance of our method. Experimental results indicate that our proposed method can achieve the best dehazing effect.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122269
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2270: A Closer Look at the Statistical
           Behavior of a Chaotic System with Message Inclusion for Cryptographic
           Applications

    • Authors: Adina Elena Lupu (Blaj), Adriana Vlad
      First page: 2270
      Abstract: One technique, especially in chaos-based cryptographic applications, is to include the message in the evolution of the dynamical system. This paper aims to find out if and to what extent the statistical behavior of the chaotic system is affected by the message inclusion in its dynamic evolution. The study is illustrated by the dynamical system described by the logistic map in cryptographic applications based on images. The evaluation of the statistical behavior was performed on an original scheme proposed. The Monte Carlo analysis of the applied Kolmogorov–Smirnov statistical test revealed that the dynamical system in the processing scheme with message inclusion does not modify its proper statistical behavior (revealed by definition relation). This was possible due to the proposed scheme designed. Namely, this scheme contains a decision switch which, supported by an appropriate choice of the magnitude of the scaling factor, ensures that the values of the dynamical system are maintained in the definition domain. The proposed framework for analyzing the statistical properties and for preserving the dynamical system behavior is one main contribution of this research. The message inclusion scheme also provides an enhancement with cryptographic mixing functions applied internally; the statistical behavior of the dynamical system is also analyzed in this case. Thus, the paper contributes to the theoretical complex characterization of the dynamical system considering also the message inclusion case.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122270
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2271: HR-YOLO: A Multi-Branch Network Model
           for Helmet Detection Combined with High-Resolution Network and YOLOv5

    • Authors: Yuanfeng Lian, Jing Li, Shaohua Dong, Xingtao Li
      First page: 2271
      Abstract: Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122271
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2272: Exploring Android Obfuscators and
           Deobfuscators: An Empirical Investigation

    • Authors: Shouki A. Ebad, Abdulbasit A. Darem
      First page: 2272
      Abstract: Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically classify twenty cutting-edge tools according to their features, analysis type, programming language support, licensing, applied obfuscation transformations, and general technical drawbacks. Our analysis predominantly reveals that very few tools work at the dynamic level, and most tools (which are static-based) work for Java or Java-based ecosystems (e.g., Android). The findings also show that the widespread adoption of renaming transformations is followed by formatting and code injection. In addition, this paper pinpoints the technical shortcomings of each tool; some of these drawbacks are common in static-based analyzers (e.g., resource consumption), and other drawbacks have negative effects on the experiment conducted by students (e.g., a third-party library involved). According to these critical limitations, we provide some timely recommendations for further research. This study can assist not only Android developers and researchers to improve the overall health of their apps but also the managers of computer science and cybersecurity academic programs to embed suitable obfuscation tools in their curricula.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122272
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2273: Image to Label to Answer: An Efficient
           Framework for Enhanced Clinical Applications in Medical Visual Question
           Answering

    • Authors: Jianfeng Wang, Kah Phooi Seng, Yi Shen, Li-Minn Ang, Difeng Huang
      First page: 2273
      Abstract: Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering clinical application and advancement. This paper proposes the ITLTA framework for Med-VQA, designed based on field requirements. ITLTA combines multi-label learning of medical images with the language understanding and reasoning capabilities of large language models (LLMs) to achieve zero-shot learning, meeting natural language module needs without end-to-end training. This approach reduces deployment costs and training data requirements, allowing LLMs to function as flexible, plug-and-play modules. To enhance multi-label classification accuracy, the framework uses external medical image data for pretraining, integrated with a joint feature and label attention mechanism. This configuration ensures robust performance and applicability, even with limited data. Additionally, the framework clarifies the decision-making process for visual labels and question prompts, enhancing the interpretability of Med-VQA. Validated on the VQA-Med 2019 dataset, our method demonstrates superior effectiveness compared to existing methods, confirming its outstanding performance for enhanced clinical applications.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122273
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2274: GNN-Based Network Traffic Analysis for
           the Detection of Sequential Attacks in IoT

    • Authors: Tanzeela Altaf, Xu Wang, Wei Ni, Guangsheng Yu, Ren Ping Liu, Robin Braun
      First page: 2274
      Abstract: This research introduces a novel framework utilizing a sequential gated graph convolutional neural network (GGCN) designed specifically for botnet detection within Internet of Things (IoT) network environments. By capitalizing on the strengths of graph neural networks (GNNs) to represent network traffic as complex graph structures, our approach adeptly handles the temporal dynamics inherent to botnet attacks. Key to our approach is the development of a time-stamped multi-edge graph structure that uncovers subtle temporal patterns and hidden relationships in network flows, critical for recognizing botnet behaviors. Moreover, our sequential graph learning framework incorporates time-sequenced edges and multi-edged structures into a two-layered gated graph model, which is optimized with specialized message-passing layers and aggregation functions to address the challenges of time-series traffic data effectively. Our comparative analysis with the state of the art reveals that our sequential gated graph convolutional neural network achieves substantial improvements in detecting IoT botnets. The proposed GGCN model consistently outperforms the conventional model, achieving improvements in accuracy ranging from marginal to substantial—0.01% for BoT IoT and up to 25% for Mirai. Moreover, our empirical analysis underscores the GGCN’s enhanced capabilities, particularly in binary classification tasks, on imbalanced datasets. These findings highlight the model’s ability to effectively navigate and manage the varying complexity and characteristics of IoT security threats across different datasets.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122274
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2275: Effective Denoising Algorithms for
           Converting Indoor Blueprints Using a 3D Laser Scanner

    • Authors: Sehyeon Yoon, Sanghyun Choi, Jhonghyun An
      First page: 2275
      Abstract: This paper focuses on converting complex 3D maps created by LiDAR and SLAM technology into simple 2D maps to make them easier to understand. While 3D maps provide a lot of useful details for robots and computer programs, they can be difficult to read for humans who are used to flat maps. We developed a new system to clean up these 3D maps and convert them into intuitive and accurate 2D maps. The system uses three steps designed to correct different kinds of errors found in 3D LiDAR scan data: clustering-based denoising, height-based denoising, and Statistical Outlier Removal. In particular, height-based denoising is the method we propose in this paper, an algorithm that leaves only indoor structures such as walls. The paper proposes an algorithm that considers the entire range of the point cloud, rather than just the points near the ceiling, as is the case with existing methods, to make denoising more effective. This makes the final 2D map easy to understand and useful for building planning or emergency preparedness. Our main goal is to map the interior of buildings faster and more effectively, creating 2D drawings that reflect accurate and current information. We want to make it easier to use LiDAR and SLAM data in our daily work and increase productivity.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122275
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2276: P2P Federated Learning Based on Node
           Segmentation with Privacy Protection for IoV

    • Authors: Jia Zhao, Yating Guo, Bokai Yang, Yanchun Wang
      First page: 2276
      Abstract: The current usage of federated learning in applications relies on the existence of servers. To address the inability to conduct federated learning for IoV (Internet of Vehicles) applications in serverless areas, a P2P (peer-to-peer) architecture for federated learning is proposed in this paper. Following node segmentation based on limited subgraph diameters, an edge aggregation mode is employed to propagate models inwardly, and a mode for propagating the model inward to the C-node (center node) while aggregating is proposed. Simultaneously, a personalized differential privacy scheme was designed under this architecture. Through experimentation and verification, the approach proposed in this paper demonstrates the combination of both security and usability.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122276
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2277: Understanding Learner Satisfaction in
           Virtual Learning Environments: Serial Mediation Effects of Cognitive and
           Social-Emotional Factors

    • Authors: Xin Yin, Jiakai Zhang, Gege Li, Heng Luo
      First page: 2277
      Abstract: This study explored the relationship between technology acceptance and learning satisfaction within a virtual learning environment (VLE) with cognitive presence, cognitive engagement, social presence, and emotional engagement as mediators. A total of 237 university students participated and completed a questionnaire after studying in the Virbela VLE. The results revealed direct and indirect links between technology acceptance and virtual learning satisfaction. The mediation analysis showed the critical mediating roles of cognitive presence and emotional engagement in fostering satisfaction. There also appeared to be a sequential mediating pathway from technology acceptance to learning satisfaction through social presence and emotional engagement. Notably, cognitive engagement and social presence did not have a significant mediating effect on satisfaction. These results provide a supplementary perspective on how technological, cognitive, and emotional factors can enhance student satisfaction in VLEs. The study concludes with several implications for future research and practice of VLEs in higher education.
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122277
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2278: An Advanced Methodology for Crystal
           System Detection in Li-ion Batteries

    • Authors: Nikola Anđelić, Sandi Baressi Šegota
      First page: 2278
      Abstract: Detecting the crystal system of lithium-ion batteries is crucial for optimizing their performance and safety. Understanding the arrangement of atoms or ions within the battery’s electrodes and electrolyte allows for improvements in energy density, cycling stability, and safety features. This knowledge also guides material design and fabrication techniques, driving advancements in battery technology for various applications. In this paper, a publicly available dataset was utilized to develop mathematical equations (MEs) using a genetic programming symbolic classifier (GPSC) to determine the type of crystal structure in Li-ion batteries with a high classification performance. The dataset consists of three different classes transformed into three binary classification datasets using a one-versus-rest approach. Since the target variable of each dataset variation is imbalanced, several oversampling techniques were employed to achieve balanced dataset variations. The GPSC was trained on these balanced dataset variations using a five-fold cross-validation (5FCV) process, and the optimal GPSC hyperparameter values were searched for using a random hyperparameter value search (RHVS) method. The goal was to find the optimal combination of GPSC hyperparameter values to achieve the highest classification performance. After obtaining MEs using the GPSC with the highest classification performance, they were combined and tested on initial binary classification dataset variations. Based on the conducted investigation, the ensemble of MEs could detect the crystal system of Li-ion batteries with a high classification accuracy (1.0).
      Citation: Electronics
      PubDate: 2024-06-10
      DOI: 10.3390/electronics13122278
      Issue No: Vol. 13, No. 12 (2024)
       
  • Electronics, Vol. 13, Pages 2179: Grant-Free Random Access Enhanced by
           Massive MIMO and Non-Orthogonal Preambles

    • Authors: Hao Jiang, Hongming Chen, Hongming Hu, Jie Ding
      First page: 2179
      Abstract: Massive multiple input multiple output (MIMO) enabled grant-free random access (mGFRA) stands out as a promising random access (RA) solution, thus effectively addressing the need for massive access in massive machine-type communications (mMTCs) while ensuring high spectral efficiency and minimizing signaling overhead. However, the bottleneck of mGFRA is mainly dominated by the orthogonal preamble collisions, since the orthogonal preamble pool is small and of a fixed-sized. In this paper, we explore the potential of non-orthogonal preambles to overcome limitations and enhance the success probability of mGFRA without extending the length of the preamble. Given the RA procedure of mGFRA, we analyze the factors influencing the success rate of mGFRA with non-orthogonal preamble and propose to use two types of sequences, namely Gold sequence and Gaussian distribution sequence, as the preambles for mGFRA. Simulation results demonstrate the effectiveness of these two types pf non-orthogonal preambles in improving the success probability of mGFRA. Moreover, the system parameters’ impact on the performance of mGFRA with non-orthogonal preambles is examined and deliberated.
      Citation: Electronics
      PubDate: 2024-06-03
      DOI: 10.3390/electronics13112179
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2180: The Real-Time Image Sequences-Based
           Stress Assessment Vision System for Mental Health

    • Authors: Mavlonbek Khomidov, Deokwoo Lee, Chang-Hyun Kim, Jong-Ha Lee
      First page: 2180
      Abstract: Early detection and prevention of stress is crucial because stress affects our vital signs like heart rate, blood pressure, skin temperature, respiratory rate, and heart rate variability. There are different ways to determine stress using different devices, such as the electrocardiogram (ECG), electrodermal activity (EDA), the electroencephalogram (EEG), photoplethysmography (PPG), or a questionnaire-based method of stress assessment. In this study, we proposed a camera-based real-time stress detection system using remote photoplethysmography (rPPG). We trained different machine learning models using three datasets: the SWELL dataset, the PPG sensor dataset, and the last ECG and EEG-based stress dataset. The models with the highest predictive accuracy were used to classify stress based on HR and HRV features obtained from the face using a camera. HR and HRV estimations from the face were validated on the PURE public dataset and the custom dataset. In this study, it was observed that the random forest algorithm performs significantly better than other models, achieving an impressive 99% predictive accuracy in the SWELL dataset. In the second dataset, the logistic regression technique shows the best result, achieving an accuracy rate of 84.24%. In the last dataset, the ensemble model achieved an accuracy rate of 67%. We also checked the proposed algorithm in the process of public speaking to estimate stress in a real-time situation.
      Citation: Electronics
      PubDate: 2024-06-03
      DOI: 10.3390/electronics13112180
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2181: Robust Tensor Learning for Multi-View
           Spectral Clustering

    • Authors: Deyan Xie, Zibao Li, Yingkun Sun, Wei Song
      First page: 2181
      Abstract: Tensor-based multi-view spectral clustering methods are promising in practical clustering applications. However, most of the existing methods adopt the ℓ2,1 norm to depict the sparsity of the error matrix, and they usually ignore the global structure embedded in each single view, compromising the clustering performance. Here, we design a robust tensor learning method for multi-view spectral clustering (RTL-MSC), which employs the weighted tensor nuclear norm to regularize the essential tensor for exploiting the high-order correlations underlying multiple views and adopts the nuclear norm to constrain each frontal slice of the essential tensor as the block diagonal matrix. Simultaneously, a novel column-wise sparse norm, namely, ℓ2,p, is defined in RTL-MSC to measure the error tensor, making it sparser than the one derived by the ℓ2,1 norm. We design an effective optimization algorithm to solve the proposed model. Experiments on three widely used datasets demonstrate the superiority of our method.
      Citation: Electronics
      PubDate: 2024-06-03
      DOI: 10.3390/electronics13112181
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2182: RVDR-YOLOv8: A Weed Target Detection
           Model Based on Improved YOLOv8

    • Authors: Yuanming Ding, Chen Jiang, Lin Song, Fei Liu, Yunrui Tao
      First page: 2182
      Abstract: Currently, weed control robots that can accurately identify weeds and carry out removal work are gradually replacing traditional chemical weed control techniques. However, the computational and storage resources of the core processing equipment of weeding robots are limited. Aiming at the current problems of high computation and the high number of model parameters in weeding robots, this paper proposes a lightweight weed target detection model based on the improved YOLOv8 (You Only Look Once Version 8), called RVDR-YOLOv8 (Reversible Column Dilation-wise Residual). First, the backbone network is reconstructed based on RevCol (Reversible Column Networks). The unique reversible columnar structure of the new backbone network not only reduces the computational volume but also improves the model generalisation ability. Second, the C2fDWR module is designed using Dilation-wise Residual and integrated with the reconstructed backbone network, which improves the adaptive ability of the new backbone network RVDR and enhances the model’s recognition accuracy for occluded targets. Again, GSConv is introduced at the neck end instead of traditional convolution to reduce the complexity of computation and network structure while ensuring the model recognition accuracy. Finally, InnerMPDIoU is designed by combining MPDIoU with InnerIoU to improve the prediction accuracy of the model. The experimental results show that the computational complexity of the new model is reduced by 35.8%, the number of parameters is reduced by 35.4% and the model size is reduced by 30.2%, while the mAP50 and mAP50-95 values are improved by 1.7% and 1.1%, respectively, compared to YOLOv8. The overall performance of the new model is improved compared to models such as Faster R-CNN, SSD and RetinaNet. The new model proposed in this paper can achieve the accurate identification of weeds in farmland under the condition of limited hardware resources, which provides theoretical and technical support for the effective control of weeds in farmland.
      Citation: Electronics
      PubDate: 2024-06-03
      DOI: 10.3390/electronics13112182
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2183: Temporal Attention for Few-Shot Concept
           Drift Detection in Streaming Data

    • Authors: Ximing Lin, Longtao Chang, Xiushan Nie, Fei Dong
      First page: 2183
      Abstract: Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift is a phenomenon in which the statistical properties of a target domain change over time in an arbitrary way. These changes might be caused by changes in hidden variables that cannot be measured directly. With the onset of the big data era, domains such as social networks, meteorology, and finance are generating copious amounts of streaming data. Embedded within these data, the issue of concept drift can affect the attributes of streaming data in various ways, leading to a decline in the accuracy and performance of models. There is a pressing need for new models to re-adapt to the changes in streaming data. Traditional concept drift detection algorithms struggle to effectively capture and utilize the key feature points of concept drift within complex time series, thereby failing to maintain the accuracy and efficiency of the models. In light of these challenges, this study introduces a novel concept drift detection method that incorporates a temporal attention mechanism within a prototypical network. By integrating a temporal attention mechanism during the feature extraction process, our approach enhances the capability to process complex time series data, preserves temporal locality, strengthens the learning of key features, and reduces the amount of labeled data required. This method significantly improves the detection accuracy and efficiency of small sample streaming data by better capturing the local features of the data. Experiments conducted across multiple datasets demonstrate that this method exhibits comprehensive leading performance in terms of accuracy and F1-score, with excellent recall and precision, thereby validating its effectiveness in enhancing concept drift detection in streaming data.
      Citation: Electronics
      PubDate: 2024-06-03
      DOI: 10.3390/electronics13112183
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2184: Underwater Image Enhancement Algorithm
           Based on Adversarial Training

    • Authors: Monan Zhang, Yichen Li, Wenbin Yu
      First page: 2184
      Abstract: Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information capacity. However, below the sea surface, objective factors such as blurriness, turbulence, and underwater color casting can cause image distortion and affect the acquisition of images. In this paper, the enhancement of underwater images is tackled using an adversarial learning-based approach. First, pre-processing is applied to address the significant color casting in the dataset, thus enhancing feature learning for subsequent style transfer. Then, corresponding improvements are made to a generative adversarial network’s structure and loss functions to better restore the features of the network output. Finally, evaluations and comparisons are performed using underwater image quality assessment metrics and several public datasets. Through multidimensional experiments, the proposed algorithm is shown to exhibit excellent performance in both subjective and objective evaluation metrics compared to state-of-the-art algorithms, as well as in practical visual applications.
      Citation: Electronics
      PubDate: 2024-06-03
      DOI: 10.3390/electronics13112184
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2185: A Review of Endogenous Security Research

    • Authors: Xiaoyu Liu, Haizhou Wang, Cuixia Li
      First page: 2185
      Abstract: The development of society has deepened the application of the Internet in production and daily life. At the same time, network security risks are becoming increasingly severe. For the security problems faced in cyberspace, most of the traditional defenses are currently focused on blocking the discovered vulnerabilities. However, these methods not only rely on prior knowledge of vulnerabilities but also fail to address the security issues brought about by the protection program itself. In view of this, endogenous security, which emphasizes the importance of not relying on a priori knowledge and not bringing in new security problems, has received increasing attention. This review provides a detailed introduction to endogenous security and its related issues, which is lacking in the field of network security. Firstly, this paper outlines the detrimental effects of vulnerabilities, identifies issues within moving target defense, and contrasts it with mimic defense. Additionally, the concepts, models, principles, and application scenarios of endogenous security are introduced. Finally, the challenges encountered by this technology are comprehensively summarized, and potential future development trends are further explored.
      Citation: Electronics
      PubDate: 2024-06-03
      DOI: 10.3390/electronics13112185
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2186: Real-Time Implementation of Three-Phase
           Z Packed U-Cell Modular Multilevel Grid-Connected Converter Using CPU and
           FPGA

    • Authors: Sandy Atanalian, Fadia Sebaaly, Rawad Zgheib, Kamal AL-Haddad
      First page: 2186
      Abstract: The Modular Multilevel Converter (MMC) is a promising converter for medium-/high voltage applications due to its various features. The waveform quality could be enhanced further by expanding the number of generated voltage levels, which increases the number of submodules (SMs); however, this improvement enlarges the size and cost of the converter, posing a persistent challenge. Hence, there exists a trade-off between power quality and the size and complexity of the converter. To verify the performance of such a complex converter and to validate the effectiveness of the control system, especially in the absence of a physical system, Real-Time (RT) simulation becomes crucial. However, the large number of components of a MMC creates important numerical challenges and computational difficulties in RT simulation. This paper proposes a grid-connected MMC employing a Z Packed U-Cell converter as a SM to generate a higher number of voltage levels while minimizing the required number of SMs. The ZPUC-MMC is implemented on an FPGA-based RT simulation platform using Electric Hardware Solver to reduce computational burden and simulation time, while improving the accuracy of the obtained results. Conventional controllers of MMCs are applied to assess the effectiveness and robustness of the proposed system during steady-state and dynamic operations.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112186
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2187: Airborne Radar Space–Time Adaptive
           Processing Algorithm Based on Dictionary and Clutter Power Spectrum
           Correction

    • Authors: Zhiqi Gao, Wei Deng, Pingping Huang, Wei Xu, Weixian Tan
      First page: 2187
      Abstract: Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112187
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2188: Impact of Solder Voids on IGBT Thermal
           Behavior: A Multi-Methodological Approach

    • Authors: Omid Alavi, Ward De Ceuninck, Michaël Daenen
      First page: 2188
      Abstract: This study investigates the thermal behavior of Insulated Gate Bipolar Transistors (IGBTs) with a focus on the influence of solder voids within the device. Utilizing a combination of Finite Element Method (FEM) simulations, X-ray imaging, and SEM-EDX analysis, we accurately modeled the internal structure of IGBTs to assess the impact of void characteristics on thermal resistance. The findings reveal that the presence and characteristics of solder voids—particularly their size, number, and distribution—significantly affect the thermal resistance of IGBT devices. Experimental measurements validate the FEM model’s accuracy, confirming that voids disrupt the heat flow path, which can lead to increased thermal resistance and potential device failure. Five regression models, including Gaussian process regression (GPR) and neural networks, were employed to predict the thermal resistance based on void characteristics, with the GPR models demonstrating superior performance. The optimal GPR RQ model consistently provided accurate predictions with an RMSE of 0.0050 and R2 of 0.9728. Using the void percentage as the only input parameter for the regression models significantly impacted the prediction accuracy, showing the importance of the void extraction method. This study shows the necessity of minimizing solder voids and offers a robust methodological framework for a better prediction of the reliability of IGBTs.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112188
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2189: A Simple Thermal Model for Junction and
           Hot Spot Temperature Estimation of 650 V GaN HEMT during Short Circuit

    • Authors: Simone Palazzo, Annunziata Sanseverino, Giovanni Canale Parola, Emanuele Martano, Francesco Velardi, Giovanni Busatto
      First page: 2189
      Abstract: Temperature is a critical parameter for the GaN HEMT as it sharply impacts the electrical characteristics of the device more than for SiC or Si MOSFETs. Either when designing a power converter or testing a device for reliability and robustness characterizations, it is essential to estimate the junction temperature of the device. For this aim, manufacturers provide compact models to simulate the device in SPICE-based simulators. These models provide the junction temperature, which is considered uniform along the channel. We demonstrate through two-dimensional numerical simulations that this approach is not suitable when the device undergoes high electrothermal stress, such as during short circuit (SC), when the temperature distribution along the channel is strongly not uniform. Based on numerical simulations and experimental measurements on a 650 V/4 A GaN HEMT, we derived a thermal network suitable for SPICE simulations to correctly compute the junction temperature and the SC current, even if not providing information about the possible failure of the device due to the formation of a local hot spot. For this reason, we used a second thermal network to estimate the maximum temperature reached inside the device, whose results are in good agreement with the experimental observed failures.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112189
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2190: Design and Analysis of a High-Gain,
           Low-Noise, and Low-Power Analog Front End for Electrocardiogram
           Acquisition in 45 nm Technology Using gm/ID Method

    • Authors: Md. Zubair Alam Emon, Khosru Mohammad Salim, Md. Iqbal Bahar Chowdhury
      First page: 2190
      Abstract: In this work, an analog front-end (AFE) circuit for an electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using an advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter—all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the gm/ID methodology. Both the pre-layout and post-layout simulation are carried out. The layout consumes an area of 0.00628 mm2 without the resistors and capacitors. Analysis of various simulation results are carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz, with a variable gain between 44 and 58 dB, a notch depth of −56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of −59.65 dB (less than 1%), an input-referred noise spectral density of <34 μVrms/Hz at the pass-band, a dynamic range of 52.71 dB, and a total power consumption of 10.88 μW with a supply of ±0.6 V. Hence, the AFE exhibits the promise of high-quality signal acquisition capability required for portable ECG detection systems in modern healthcare.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112190
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2191: Speech Emotion Recognition Using
           Dual-Stream Representation and Cross-Attention Fusion

    • Authors: Shaode Yu, Jiajian Meng, Wenqing Fan, Ye Chen, Bing Zhu, Hang Yu, Yaoqin Xie, Qiuirui Sun
      First page: 2191
      Abstract: Speech emotion recognition (SER) aims to recognize human emotions through in-depth analysis of audio signals. However, it remains challenging to encode emotional cues and to fuse the encoded cues effectively. In this study, dual-stream representation is developed, and both full training and fine-tuning of different deep networks are employed for encoding emotion patterns. Specifically, a cross-attention fusion (CAF) module is designed to integrate the dual-stream output for emotion recognition. Using different dual-stream encoders (fully training a text processing network and fine-tuning a pre-trained large language network), the CAF module is compared to other three fusion modules on three databases. The SER performance is quantified with weighted accuracy (WA), unweighted accuracy (UA), and F1-score (F1S). The experimental results suggest that the CAF outperforms the other three modules and leads to promising performance on the databases (EmoDB: WA, 97.20%; UA, 97.21%; F1S, 0.8804; IEMOCAP: WA, 69.65%; UA, 70.88%; F1S, 0.7084; RAVDESS: WA, 81.86%; UA, 82.75.21%; F1S, 0.8284). It is also found that fine-tuning a pre-trained large language network achieves superior representation than fully training a text processing network. In a future study, improved SER performance could be achieved through the development of a multi-stream representation of emotional cues and the incorporation of a multi-branch fusion mechanism for emotion recognition.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112191
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2192: A Novel CNFET SRAM-Based
           Compute-In-Memory for BNN Considering Chirality and Nanotubes

    • Authors: Youngbae Kim, Nader Alnatsheh, Nandakishor Yadav, Jaeik Cho, Heeyoung Jo, Kyuwon Ken Choi
      First page: 2192
      Abstract: As AI models grow in complexity to enhance accuracy, supporting hardware encounters challenges such as heightened power consumption and diminished processing speed due to high throughput demands. Compute-in-memory (CIM) technology emerges as a promising solution. Furthermore, carbon nanotube field-effect transistors (CNFETs) show significant potential in bolstering CIM technology. Despite advancements in silicon semiconductor technology, CNFETs pose as formidable competitors, offering advantages in reliability, performance, and power efficiency. This is particularly pertinent given the ongoing challenges posed by the reduction in silicon feature size. We proposed an ultra-low-power architecture leveraging CNFETs for Binary Neural Networks (BNNs), featuring an advanced state-of-the-art 8T SRAM bit cell and CNFET model to optimize performance in intricate AI computations. Through meticulous optimization, we fine-tune the CNFET model by adjusting tube counts and chiral vectors, as well as optimizing transistor ratios for SRAM transistors and nanotube diameters. SPICE simulation in 32 nm CNFET technology facilitates the determination of optimal transistor ratios and chiral vectors across various nanotube diameters under a 0.9 V supply voltage. Comparative analysis with conventional FinFET-based CIM structures underscores the superior performance of our CNFET SRAM-based CIM design, boasting a 99% reduction in power consumption and a 91.2% decrease in delay compared to state-of-the-art designs.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112192
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2193: Continuous Time Simulation and
           System-Level Model of a MVDC Distribution Grid Including SST and MMC-Based
           AFE

    • Authors: Daniel Siemaszko, Mauro Carpita
      First page: 2193
      Abstract: Medium-voltage DC (MVDC) technology has gained increasing attention in recent years. Power electronics devices dominate these grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system-level control model of such a modern MVDC distribution grid. The aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/−10 kV MVDC grid case study, including a solid-state transformer (SST)- and modular multilevel converter (MMC)-based active front end (AFE). An additional aim of this paper is to supply educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112193
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2194: A Review of the Antenna Field Regions

    • Authors: Amedeo Capozzoli, Claudio Curcio, Francesco D’Agostino, Angelo Liseno
      First page: 2194
      Abstract: We review the field regions and their boundaries around an electromagnetic source. We consider the cases of sources whose dimensions are comparable or larger than the wavelength, of planar sources/apertures, and of sources whose dimensions are small with respect to the wavelength and the criteria involving the strength of the reactive components of the electromagnetic field with respect to the radiative ones. The Fraunhofer and the Fresnel Regions are detailed, along with references to the paraxial approximation for planar apertures. The near-field and intermediate regions are also discussed. We review the standard boundaries between the regions. However, the standard boundaries are not clearly marked, nor are the regions uniquely defined. Accordingly, we also discuss different criteria that have been proposed during the years, which depend on the application and typically rely on numerical arguments, but are not necessarily universally accepted.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112194
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2195: Ergodic Rate Analysis for Full-Duplex
           and Half-Duplex Networks with Energy Harvesting

    • Authors: Bin Zhong, Liang Chen, Zhongshan Zhang
      First page: 2195
      Abstract: Considering energy harvesting, the ergodic data rates for both in band full-duplex (FD) and half-duplex (HD) wireless communications were studied. The analytic expressions of downlink and uplink ergodic rates for the proposed system were first derived with independent and identically distributed (i.i.d.) Rayleigh fading link. It was revealed that the uplink data rate can be improved by decreasing the downlink data rate. Furthermore, the uplink/downlink data rates are also shown to be influenced by some significance parameters, for example, the power split parameter and signal-to-noise ratio (SNR) (i.e., PS/σ2) of each link. Additionally, unlike the HD, the proposed FD node is capable of harvesting energy during the communication process; however, this is at the cost of performance loss induced by the residual self-interference (RSI), which is caused by the essence of simultaneous uplink and downlink transmissions in a single frequency band.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112195
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2196: A Compact C-Band Multiple-Input
           Multiple-Output Circular Microstrip Patch Antenna Array with Octagonal
           Slotted Ground Plane and Neutralization Line for Improved Port Isolation
           in 5G Handheld Devices

    • Authors: Asad Ali Khan, Zhenyong Wang, Dezhi Li, Ali Ahmed
      First page: 2196
      Abstract: In this paper, an eight-port antenna array is presented for 5G handheld terminals to support multiple-input multiple-output (MIMO) operations. The reported design involves three layers: the top contains eight circular microstrip feed elements; the middle is a low-cost FR-4 substrate, and the bottom layer is a ground plane with four etched octagonal slots. Each resonating element is fed by 50-ohm sub-miniature connectors. To mitigate the detrimental effects of mutual coupling of ports and enhance overall isolation between the adjacent microstrip-fed circular patch elements, a neutralization line is strategically implemented between the feed lines of the antenna array. The design configuration involves two elements at each vertex of the printed circuit board (PCB). The overall dimensions of the PCB are 150 × 75 mm2. Each slot and its corresponding radiating elements exhibit linear dual polarization and diverse radiation patterns. The proposed antenna design achieves the required operating bandwidth of more than 1000 MHz spanning from 3 to 4.2 GHz, effectively covering all the upper C-band frequency range of 3.3 GHz to 4.2 GHz allocated for 5G n77 and n78 frequency range 1 (FR1). Required port isolation and lower envelop correlation coefficient (ECC) are achieved for the band of interest. The proposed design gives a peak gain of up to 4 dB for the said band. In addition to these results, degradation in the performance of the antenna array is also investigated during different operating modes of the handheld device. Measured results from the fabricated unit cell and whole array also have a good match with simulated results. On the whole, the proposed antenna possesses the potential to be used in 5G and the open radio access network (ORAN) compliant handheld devices.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112196
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2197: An Improved YOLOv5s Model for Building
           Detection

    • Authors: Jingyi Zhao, Yifan Li, Jing Cao, Yutai Gu, Yuanze Wu, Chong Chen, Yingying Wang
      First page: 2197
      Abstract: With the continuous advancement of autonomous vehicle technology, the recognition of buildings becomes increasingly crucial. It enables autonomous vehicles to better comprehend their surrounding environment, facilitating safer navigation and decision-making processes. Therefore, it is significant to improve detection efficiency on edge devices. However, building recognition faces problems such as severe occlusion and large size of detection models that cannot be deployed on edge devices. To solve these problems, a lightweight building recognition model based on YOLOv5s is proposed in this study. We first collected a building dataset from real scenes and the internet, and applied an improved GridMask data augmentation method to expand the dataset and reduce the impact of occlusion. To make the model lightweight, we pruned the model by the channel pruning method, which decreases the computational costs of the model. Furthermore, we used Mish as the activation function to help the model converge better in sparse training. Finally, comparing it to YOLOv5s (baseline), the experiments show that the improved model reduces the model size by 9.595 MB, and the mAP@0.5 reaches 82.3%. This study will offer insights into lightweight building detection, demonstrating its significance in environmental perception, monitoring, and detection, particularly in the field of autonomous driving.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112197
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2198: Shaping Tomorrow: Anticipating Skills
           Requirements Based on the Integration of Artificial Intelligence in
           Business Organizations—A Foresight Analysis Using the Scenario
           Method

    • Authors: Nicolae Bobitan, Diana Dumitrescu, Adriana Florina Popa, Daniela Nicoleta Sahlian, Ioan Codrut Turlea
      First page: 2198
      Abstract: This study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths for AI adoption—gradual, aggressive, and selective—to project the evolving skills needed for employee success in changing business environments. The findings emphasize the critical need for both enhanced technical proficiency and soft skills, such as creative problem-solving and interpersonal abilities, across all AI adoption scenarios. The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI. It provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future.
      Citation: Electronics
      PubDate: 2024-06-04
      DOI: 10.3390/electronics13112198
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2199: MEDAL: A Multimodality-Based Effective
           Data Augmentation Framework for Illegal Website Identification

    • Authors: Li Wen, Min Zhang, Chenyang Wang, Bingyang Guo, Huimin Ma, Pengfei Xue, Wanmeng Ding, Jinghua Zheng
      First page: 2199
      Abstract: The emergence of illegal (gambling, pornography, and attraction) websites seriously threatens the security of society. Due to the concealment of illegal websites, it is difficult to obtain labeled data with high quantity. Meanwhile, most illegal websites usually disguise themselves to avoid detection; for example, some gambling websites may visually resemble gaming websites. However, existing methods ignore the means of camouflage in a single modality. To address the above problems, this paper proposes MEDAL, a multimodality-based effective data augmentation framework for illegal website identification. First, we established an illegal website identification framework based on tri-training that combines information from different modalities (including image, text, and HTML) while making full use of numerous unlabeled data. Then, we designed a multimodal mutual assistance module that is integrated with the tri-training framework to mitigate the introduction of error information resulting from an unbalanced single-modal classifier performance in the tri-training process. Finally, the experimental results on the self-developed dataset demonstrate the effectiveness of the proposed framework, performing well on accuracy, precision, recall, and F1 metrics.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112199
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2200: Noise-like-Signal-Based Sub-Synchronous
           Oscillation Prediction for a Wind Farm with Doubly-Fed Induction
           Generators

    • Authors: Junjie Ma, Linxing Lyu, Junfeng Man, Mengqi Chen, Yijun Cheng
      First page: 2200
      Abstract: The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. The equivalent SSO damping is influenced by both wind speed and compensation level. However, it is hard for the wind farm to obtain a compensation level in time to predict the SSO risk. In this paper, an SSO risk prediction method for a DFIG wind farm is proposed based on the characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, the potential SSO frequency and damping are identified from signals at normal working points by integration using variational mode decomposition and Prony analysis. Finally, a fuzzy inference system is established to predict the SSO risk of a DFIG wind farm. The effectiveness of the proposed method is verified by simulation. The proposed prediction method can predict SSO risks caused by the variation in wind speed, while the transmission line parameters are undetectable for the wind farm.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112200
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2201: Detection of Train Wheelset Tread
           Defects with Small Samples Based on Local Inference Constraint Network

    • Authors: Jianhua Liu, Shiyi Jiang, Zhongmei Wang, Jiahao Liu
      First page: 2201
      Abstract: Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance of trains. In this paper, a new method based on a local inference constraint network is proposed to detect wheelset tread defects, and the main purpose is to address the issue of insufficient feature spaces caused by small samples. First, a generative adversarial network is applied to generate diverse samples with semantic consistency. An attention mechanism module is introduced into the feature extraction network to increase the importance of defect features. Then, the residual spine network for local input decisions is constructed to establish an association between sample features and defect types. Furthermore, the network’s activation function is improved to obtain higher learning speed and accuracy with fewer parameters. Finally, the validity and feasibility of the proposed method are verified using experimental data.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112201
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2202: Enhancing Signal Recognition Accuracy in
           Delay-Based Optical Reservoir Computing: A Comparative Analysis of
           Training Algorithms

    • Authors: Ruibo Zhang, Tianxiang Luan, Shuo Li, Chao Wang, Ailing Zhang
      First page: 2202
      Abstract: To improve the accuracy of signal recognition in delay-based optical reservoir computing (RC) systems, this paper proposes the use of nonlinear algorithms at the output layer to replace traditional linear algorithms for training and testing datasets and apply them to the identification of frequency-modulated continuous wave (FMCW) LiDAR signals. This marks the inaugural use of the system for the identification of FMCW LiDAR signals. We elaborate on the fundamental principles of a delay-based optical RC system using an optical-injected distributed feedback laser (DFB) laser and discriminate four FMCW LiDAR signals through this setup. In the output layer, three distinct training algorithms—namely linear regression, support vector machine (SVM), and random forest—were employed to train the optical reservoir. Upon analyzing the experimental results, it was found that regardless of the size of the dataset, the recognition accuracy of the two nonlinear training algorithms was superior to that of the linear regression algorithm. Among the two nonlinear algorithms, the Random Forest algorithm had a higher recognition accuracy than SVM when the sample size was relatively small.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112202
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2203: Short-Term Flood Prediction Model Based
           on Pre-Training Enhancement

    • Authors: Yang Xia, Jiamin Lu
      First page: 2203
      Abstract: With the rapid advancement of deep learning techniques, deep learning-based flood prediction models have drawn significant attention. However, for short-term prediction in small- and medium-sized river basins, models typically rely on hydrological data spanning from the past several hours to one day, utilizing a fixed-length input window. Such input limits the models’ adaptability to diverse rainfall events and restricts their capability to comprehend historical temporal patterns. To address the underutilization of historical information by existing models, we introduce a Pre-training Enhanced Short-term Flood Prediction Method (PE-SFPM) to enrich the model’s temporal understanding without necessitating changes to the input window size. In the pre-training stage, the model uses a random masking and prediction strategy to learn segment features, capturing a more comprehensive evolutionary trend of historical floods. In the flow forecasting stage, temporal features and spatial features are captured and fused using the temporal attention, spatial attention, and gated fusion. Features are further enhanced by integrating segment features using a feed-forward network. Experimental results demonstrate that the proposed PE-SFPM model achieves excellent performance in short-term flood prediction tasks.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112203
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2204: D2Former: Dual-Domain Transformer for
           Change Detection in VHR Remote Sensing Images

    • Authors: Huanhuan Zheng, Hui Liu, Lei Lu, Shiyin Li, Jiyan Lin
      First page: 2204
      Abstract: Computational intelligence technologies have been extensively applied for the interpretation of remote sensing imagery. Recently, the computational-intelligence-based Transformer change detection (CD) approach has attracted increasing attention. However, the current Transformer-based CD method can better capture global features, but there is no good solution for the loss of local detail information. For this reason, introducing semantic and frequency information from the perspective of a dual-domain can be beneficial for improving the representation of detailed features to improve CD performance. To overcome this limitation, a dual-domain Transformer (D2Former) is proposed for CD. Firstly, we adopt a semantic tokenizer to capture the semantic information, which promotes the enrichment and refinement of semantic change information in the Transformer. Secondly, a frequency tokenizer is introduced to acquire the frequency information of the features, which offers the proposed D2Former another aspect and dimension to enhance the ability to detect change information. Therefore, the proposed D2Former employs dual-domain tokenizers to acquire and fuse the feature representation with rich semantic and frequency information, which can refine the features to acquire more fine-grained CD ability. Extensive experiments on three CD benchmark datasets demonstrate that the proposed D2Former obviously outperforms some other existing approaches. The results present the competitive performance of our method on the WHU-CD, LEVIR-CD, and GZ-CD datasets, for which it achieved F1-Score metrics of 92.85%, 90.60%, and 87.02%, respectively.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112204
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2205: Chip-Level Defect Analysis with Virtual
           Bad Wafers Based on Huge Big Data Handling for Semiconductor Production

    • Authors: Jinsik Kim, Inwhee Joe
      First page: 2205
      Abstract: Semiconductors continue to shrink in die size because of benefits like cost savings, lower power consumption, and improved performance. However, this reduction leads to more defects due to increased inter-cell interference. Among the various defect types, customer-found defects are the most costly. Thus, finding the root cause of customer-found defects has become crucial to the quality of semiconductors. Traditional methods involve analyzing the pathways of many low-yield wafers. Yet, because of the extremely limited number of customer-found defects, obtaining significant results is difficult. After the products are provided to customers, they undergo rigorous testing and selection, leading to a very low defect rate. However, since the timing of defect occurrence varies depending on the environment in which the product is used, the quantity of defective samples is often quite small. Unfortunately, with such a low number of samples, typically 10 or fewer, it becomes impossible to investigate the root cause of wafer-level defects using conventional methods. This paper introduces a novel approach to finding the root cause of these rare defective chips for the first time in the semiconductor industry. Defective wafers are identified using rare customer-found chips and chip-level EDS (Electrical Die Sorting) data, and these newly identified defective wafers are termed vBADs (virtual bad wafers). The performance of root cause analysis is dramatically improved with vBADs. However, the chip-level analysis presented here demands substantial computing power. Therefore, MPP (Massive Parallel Processing) architecture is implemented and optimized to handle large volumes of chip-level data within a large architecture infrastructure that can manage big data. This allows for a chip-level defect analysis system that can recommend the relevant EDS test and identify the root cause in real time even with a single defective chip. The experimental results demonstrate that the proposed root cause search can reveal the hidden cause of a single defective chip by amplifying it with 90 vBADs, and system performance improves by a factor of 61.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112205
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2206: SURE: Structure for Unambiguous
           Requirement Expression in Natural Language

    • Authors: Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Julio Barzola-Monteses, Leonel Vasquez-Cevallos, María Isabel Galarza-Soledispa, Manuel Reyes-Wagnio
      First page: 2206
      Abstract: This study presents three structures for clearly expressing functional requirements (FRs) and quantitative non-functional requirements (qt-NFRs). Expressing requirements with these structures will allow the understanding of requirements by stakeholders and software developers. The first structure is the SURE format, which is composed of three main sections: a title, a short definition, and a detailed description. The second proposed structure is a template to facilitate the definition of the title and description of unambiguous FRs. It is based on the application of CRUD operations on a certain entity, calling it the “CRUDE” structure. Finally, the third structure serves as a template to make it easier to clearly define the description and title of qt-NFRs. It is based on the application of system properties to computer events or actions, calling it the “PROSE” structure. In this, it is very important to specify those metric values that are desired or expected by the stakeholder. To know how much the definition of FRs and qt-NFRs improved when the proposed structures were used, 46 requirement specification documents elaborated as homework by students of the “Requirement Engineering” course offered at the University of Guayaquil between 2020 and 2022 were evaluated by five experts with more than 10 years of experience in software development for Ecuadorian companies. The findings showed that students reduced the percentage of unambiguous FRs and qt-NFRs from over 80% to about 10%. In conclusion, the findings demonstrate how crucial the three structures proposed in this paper are to helping students develop the ability to clearly express requirements.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112206
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2207: An Improved YOLOv5 Algorithm for Tyre
           Defect Detection

    • Authors: Mujun Xie, Heyu Bian, Changhong Jiang, Zhong Zheng, Wei Wang
      First page: 2207
      Abstract: In this study, a tyre defect detection model is improved and optimized under the YOLOv5 framework, aiming at radial tyre defects with characteristics such as an elongated shape and various target sizes and defect types. The DySneakConv module is introduced to replace the first BotteneckCSP in the Backbone network. The deformation offset of the DySneakConv module is used to make the convolutional energy freely adapt to the structure to improve the recognition rate of tyre defects with elongated features; the AIFI module is introduced to replace the fourth BotteneckCSP, and the self-attention mechanism and the processing of large-scale features are used to improve the recognition rate of tyre defects with elongated features using the AIFI module. This latter module has a self-attention mechanism and the ability to handle large-scale features to solve the problems of diverse tyre defects and different sizes. Secondly, the CARAFE up-sampling operator is introduced to replace the up-sampling operator in the Neck network. The up-sampling kernel prediction module in the CARAFE operator is used to increase the receptive field and allow the feature reorganization module to capture more semantic information to overcome the information loss problem of the up-sampling operator. Finally, based on the improved YOLOv5 detection algorithm, the Channel-wise Knowledge Distillation algorithm lightens the model, reducing its computational requirements and size while ensuring detection accuracy. Experimental studies were conducted on a dataset containing four types of tyre defects. Experimental results for the training set show that the improved algorithm improves the mAP0.5 by 4.6 pp, reduces the model size by 25.6 MB, reduces the computational complexity of the model by 31.3 GFLOPs, and reduces the number of parameters by 12.7 × 106 compared to the original YOLOv5m algorithm. Experimental results for the test set show that the improved algorithm improves the mAP0.5 by 2.6 pp compared to the original YOLOv5m algorithm. This suggests that the improved algorithm is more suitable for tyre defect detection than the original YOLOv5.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112207
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2208: Hybrid State Estimation: Integrating
           Physics-Informed Neural Networks with Adaptive UKF for Dynamic Systems

    • Authors: J. de Curtò, I. de Zarzà
      First page: 2208
      Abstract: In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation methods, we refine the PINN architecture with hybrid loss functions and Monte Carlo Dropout for enhanced uncertainty estimation. The Unscented Kalman Filter is augmented with an adaptive noise covariance mechanism and incorporates model parameters into the state vector to improve adaptability. We further validate this hybrid framework by integrating the enhanced PINN with the UKF for a seamless state prediction pipeline, demonstrating significant improvements in accuracy and robustness. Our experimental results show a marked enhancement in state estimation fidelity for both position and velocity tracking, supported by uncertainty quantification via Bayesian inference and Monte Carlo Dropout. We further extend the simulation and present evaluations on a double pendulum system and state estimation on a quadcopter drone. This comprehensive solution is poised to advance the state-of-the-art in dynamic system estimation, providing unparalleled performance across control theory, machine learning, and numerical optimization domains.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112208
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2209: Digital Twins in Agriculture: A Review
           of Recent Progress and Open Issues

    • Authors: Li Wang
      First page: 2209
      Abstract: Digital twin technology is expected to transform agriculture. By creating the virtual representation of a physical entity, it assists food producers in monitoring, predicting, and optimizing the production process remotely and even autonomously. However, the progress in this area is relatively slower than in industries like manufacturing. A systematic investigation of agricultural digital twins’ current status and progress is imperative. With seventy published papers, this work elaborated on the studies targeting agricultural digital twins from overall trends, focused areas (including domains, processes, and topics), reference architectures, and open questions, which could help scholars examine their research agenda and support the further development of digital twins in agriculture.
      Citation: Electronics
      PubDate: 2024-06-05
      DOI: 10.3390/electronics13112209
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2210: Artificial Intelligence Implementation
           in Internet of Things Embedded System for Real-Time Person Presence in Bed
           Detection and Sleep Behaviour Monitor

    • Authors: Minh Long Hoang, Guido Matrella, Paolo Ciampolini
      First page: 2210
      Abstract: This paper works on detecting a person in bed for sleep routine and sleep pattern monitoring based on the Micro-Electro-Mechanical Systems (MEMS) accelerometer and Internet of Things (IoT) embedded system board. This work provides sleep information, patient assessment, and elderly care for patients who live alone via tele-distance to doctors or family members. About 216,000 pieces of acceleration data were collected, including three classes: no person in bed, a static laying position, and a moving state for Artificial Intelligence (AI) application. Six well-known Machine-Learning (ML) algorithms were evaluated with precision, recall, F1-score, and accuracy in the workstation before implementing in the STM32-microcontroller for real-time state classification. The four best algorithms were selected to be programmed into the IoT board and applied for real-time testing. The results demonstrate the high accuracy of the ML performance, more than 99%, and the Classification and Regression Tree algorithm is among the best models with a light code size of 1583 bytes. The smart bed information is sent to the IoT dashboard of Node-RED via a Message Queuing Telemetry broker (MQTT).
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112210
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2211: Multi-Point Sensing via Organic Optical
           Fibres for FLASH Proton Therapy

    • Authors: Crystal Penner, Samuel Usherovich, Sophia Andru, Camille Bélanger-Champagne, Janina Hohnholz, Boris Stoeber, Cheryl Duzenli, Cornelia Hoehr
      First page: 2211
      Abstract: Optical fibres are gaining popularity for relative dosimetry in proton therapy due to their spatial resolution and ability for near real-time acquisition. For FLASH proton therapy, these fibres need to handle higher dose rates and larger doses than for conventional proton dose rates. We developed a multi-point fibre sensor embedded in a 3D-printed phantom which can measure the profile of a FLASH proton beam. Seven PMMA fibres of 1 mm diameter were embedded in a custom 3D-printed plastic phantom of the same density as the fibres. The phantom was placed in a proton beam with FLASH dose rates at the TRIUMF Proton Therapy Research Centre (PTRC). The sensor was exposed to different proton energies, 13.5 MeV, 19 MeV and 40.4 MeV, achieved by adding PMMA bolus in front of the phantom and three different beam currents, varying the dose rates from 7.5 to 101 Gy/s. The array was able to record beam profiles in both transverse and axial directions in relative agreement with measurements from EBT-XD radiochromic films (transverse) and Monte Carlo simulations (axial). A decrease in light output over time was observed, which might be caused by radiation damage in the matrix of the fibre and characterised by an exponential decay function.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112211
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2212: How to Circumvent and Beat the
           

    • Authors: Kornel Drabent, Robert Janowski, Jordi Mongay Batalla
      First page: 2212
      Abstract: Ransomware is one of the most extended cyberattacks. It consists of encrypting a user’s files or locking the smartphone in order to blackmail a victim. The attacking software is ordered on the infected device from the attacker’s remote server, known as command and control. In this work, we propose a method to recover from a Locker.CB!tr ransomware attack after it has infected and hit a smartphone. The novelty of our approach lies on exploiting the communication between the ransomware on the infected device and the attacker’s command and control server as a point to reverse disruptive actions like screen locking or file encryption. For this purpose, we carried out both a dynamic and a static analysis of decompiled Locker.CB!tr ransomware source code to understand its operation principles and exploited communication patterns from the IP layer to the application layer to fully impersonate the command and control server. This way, we gained full control over the Locker.CB!tr ransomware instance. From that moment, we were able to command the Locker.CB!tr ransomware instance on the infected device to unlock the smartphone or decrypt the files. The contributions of this work are a novel method to recover the mobile phone after ransomware attack based on the analysis of the ransomware communication with the C&C server; and a mechanism for impersonating the ransomware C&C server and thus gaining full control over the ransomware instance.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112212
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2213: Multi-Scale Feature Fusion Point Cloud
           Object Detection Based on Original Point Cloud and Projection

    • Authors: Zhikang Zhang, Zhongjie Zhu, Yongqiang Bai, Yiwen Jin, Ming Wang
      First page: 2213
      Abstract: Existing point cloud object detection algorithms struggle to effectively capture spatial features across different scales, often resulting in inadequate responses to changes in object size and limited feature extraction capabilities, thereby affecting detection accuracy. To solve this problem, we present a point cloud object detection method based on multi-scale feature fusion of the original point cloud and projection, which aims to improve the multi-scale performance and completeness of feature extraction in point cloud object detection. First, we designed a 3D feature extraction module based on the 3D Swin Transformer. This module pre-processes the point cloud using a 3D Patch Partition approach and employs a self-attention mechanism within a 3D sliding window, along with a downsampling strategy, to effectively extract features at different scales. At the same time, we convert the 3D point cloud to a 2D image using projection technology and extract 2D features using the Swin Transformer. A 2D/3D feature fusion module is then built to integrate 2D and 3D features at the channel level through point-by-point addition and vector concatenation to improve feature completeness. Finally, the integrated feature maps are fed into the detection head to facilitate efficient object detection. Experimental results show that our method has improved the average precision of vehicle detection by 1.01% on the KITTI dataset over three levels of difficulty compared to Voxel-RCNN. In addition, visualization analyses show that our proposed algorithm also exhibits superior performance in object detection.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112213
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2214: Faster Intra-Prediction of Versatile
           Video Coding Using a Concatenate-Designed CNN via DCT Coefficients

    • Authors: Sio-Kei Im, Ka-Hou Chan
      First page: 2214
      Abstract: As the next generation video coding standard, Versatile Video Coding (VVC) significantly improves coding efficiency over the current High-Efficiency Video Coding (HEVC) standard. In practice, this improvement comes at the cost of increased pre-processing complexity. This increased complexity faces the challenge of implementing VVC for time-consuming encoding. This work presents a technique to simplify VVC intra-prediction using Discrete Cosine Transform (DCT) feature analysis and a concatenate-designed CNN. The coefficients of the (DTC-)transformed CUs reflect the complexity of the original texture, and the proposed CNN employs multiple classifiers to predict whether they should be split. This approach can determine whether to split Coding Units (CUs) of different sizes according to the Versatile Video Coding (VVC) standard. This helps to simplify the intra-prediction process. The experimental results indicate that our approach can reduce the encoding time by 52.77% with a minimal increase of 1.48%. We use the Bjøntegaard Delta Bit Rate (BDBR) compared to the original algorithm, demonstrating a competitive result with other state-of-the-art methods in terms of coding efficiency with video quality.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112214
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2215: Temperature Dependence of Total Ionizing
           Dose Effects of β-Ga2O3 Schottky Barrier Diodes

    • Authors: Weili Fu, Teng Ma, Zhifeng Lei, Chao Peng, Hong Zhang, Zhangang Zhang, Tao Xiao, Hongjia Song, Yuangang Wang, Jinbin Wang, Zhao Fu, Xiangli Zhong
      First page: 2215
      Abstract: This paper investigates the temperature-dependent effects of gamma-ray irradiation on β-Ga2O3 vertical Schottky barrier diodes (SBDs) under a 100 V reverse bias condition at a total dose of 1 Mrad(Si). As the irradiation dose increased, the radiation damage became more severe. The total ionizing dose (TID) degradation behavior and mechanisms were evaluated through DC, capacitance–voltage (C-V), and low-frequency noise (LFN) measurements by varying irradiation, and the test results indicated that TID effects introduced interface defects and altered the carrier concentration within the material. The impact of TID effects was more pronounced at lower temperatures compared to higher temperatures. Additionally, the annealing effect in the high-temperature experimental conditions ameliorated the growth of interface trap defects caused by irradiation. These results suggest that compared to low-temperature testing, the device exhibits higher TID tolerance after high-temperature exposure, providing valuable insights for in-depth radiation reliability studies on subsequent related devices.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112215
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2216: Distributed Group Key Management Based
           on Blockchain

    • Authors: Jia Ni, Guowei Fang, Yekang Zhao, Jingjing Ren, Long Chen, Yongjun Ren
      First page: 2216
      Abstract: Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios with a large number of members or frequent member turnover within groups, this may lead to security vulnerabilities such as permission confusion, exacerbating the security risks and management complexity faced by the system. To address these issues, this paper utilizes blockchain technology to achieve distributed storage and management of group keys. This solution combines key management with the distributed characteristics of blockchain, enhancing scalability, and enabling tracking of malicious members. Simultaneously, by integrating intelligent authentication mechanisms and lightweight data update mechanisms, it effectively enhances the security, trustworthiness, and scalability of the key management system. This provides important technical support for constructing a more secure and reliable network environment.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112216
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2217: Matter Protocol Integration Using
           Espressif’s Solutions to Achieve Smart Home Interoperability

    • Authors: Afonso Mota, Carlos Serôdio, António Valente
      First page: 2217
      Abstract: Smart home devices are becoming more popular over the years. A diverse range of appliances is being created, and Ambient Intelligence is growing in homes. However, there are various producers of these gadgets, different kinds of protocols, and diverse environments. The lack of interoperability reduces comfort of the user and turns into a barrier to smart home adoption. Matter is growing by constructing an open-source application layer protocol that can be compatible with all smart home ecosystems. In this article, a Matter overview is provided (namely, of the Commissioning stage), and a Matter Accessory using ESP32-S3 is developed referring to the manufacturer’s SDKs and is inserted into an existent household ecosystem. Its behavior on the network is briefly analyzed, and interactions with the device are carried out. The simplicity of these tasks demonstrates accessibility for developers to create products, especially when it comes to firmware. Additionally, device commissioning and control are straightforward for the consumer. This capacity of gadget incorporation into diverse ecosystems using Matter is already on the market and might result in higher device production and enhanced smart home adoption.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112217
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2218: Position Estimation Method for Small
           Drones Based on the Fusion of Multisource, Multimodal Data and Digital
           Twins

    • Authors: Shaochun Qu, Jian Cui, Zijian Cao, Yongxing Qiao, Xuemeng Men, Yanfang Fu
      First page: 2218
      Abstract: In response to the issue of low positioning accuracy and insufficient robustness in small UAVs (unmanned aerial vehicle) caused by sensor noise and cumulative motion errors during flight in complex environments, this paper proposes a multisource, multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors, including GPS (global positioning system), an IMU (inertial measurement unit), and visual sensors, to complement the strengths and weaknesses of each hardware component, thereby mitigating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, a high-fidelity UAV model is established in the digital twin based on the real UAV parameters, providing a robust reference for data fusion. By utilizing the extended Kalman filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables the real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multisource, multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112218
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2219: Improving the Efficiency of the Axial
           Flux Machine with Hybrid Excitation

    • Authors: Pawel Prajzendanc, Ryszard Palka, Piotr Paplicki, Marcin Wardach, Michal Cichowicz, Kamil Cierzniewski, Lech Dorobczynski, Edison Gundabattini
      First page: 2219
      Abstract: This paper discusses the construction and operating principle of an axial flux electric machine with hybrid excitation. Based on computer simulations using the Finite Element Method, an analysis was conducted with changes in the geometry of the magnetic circuit, which involves the rotation of the rotor disks relative to each other on the operating parameters of the machine. Both the generator state of operation, in the meaning of analyzing the induced voltage (adjustment at −11% ÷ +64%) and the cogging torque, and the motor state of operation, in the meaning of analyzing the ripple of the electromagnetic torque (possible reduction by almost 30%), were examined. The article concludes with observations on how the change in the angle of the rotor disks affects the efficiency of the disk machine with axial flux and hybrid excitation.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112219
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2220: A Cloud-Based WEB Platform for Fall Risk
           Assessment Using a Therapist-Centered User Interface Which Enables
           Patients’ Tracking Remotely

    • Authors: Jesús Damián Blasco-García, Nieves Pavón-Pulido, Juan Antonio López-Riquelme, Ana María Roldán-Ruiz, Jorge Juan Feliu-Batlle
      First page: 2220
      Abstract: This work describes a system to help in the remote assessment of fall risk in elderly people. A portable hardware system equipped with an RGB-D sensor is used for motion capture. A set of anonymous frames, representing the process of skeleton tracking, and a collection of sequences of interesting features, obtained from body landmark evaluations through time, are stored in the Cloud for each patient. A WEB dashboard allows for tailored tests to be designed, which include the typical items within well-known fall risk evaluation tests in the literature. Such a dashboard helps therapists to evaluate each item from the analysis and observation of the sequences and the 3D representation of the body through time, and to compare the results of tests carried out in different moments, checking on the evolution of the fall risk. The software architecture that implements the system allows the information to be stored in a safe manner and preserves patients’ privacy. The paper shows the obtained results after testing an early prototype of the system, a discussion about its advantages, and the current limitations from the Human–Computer Interaction point of view, and a plan to deploy and evaluate the system from the usability perspective in the near future.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112220
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2221: Distributed High-Density Anchor (Cable)
           Support Force Monitoring System Research

    • Authors: Lei Wang, Kai Sun, Junyan Qi, Ruifu Yuan
      First page: 2221
      Abstract: In shaft mining, monitoring the deformation of the roadway due to mining pressure is of great significance to the safe production of coal mines. For this reason, a distributed high-density anchor (cable) support force monitoring system was designed by developing a low-cost anchor (cable) stress monitoring device, which consists of an anchor (cable) stress sensor and a data acquisition device. The whole system consists of an anchor bar (cable) stress monitoring device and a mine roadway deformation monitoring substation. The signals collected by the anchor force sensors are processed by the data acquisition device and sent to the self-developed mine roadway deformation monitoring substation through Long Range Radio (LoRa) wireless communication. All data from the monitoring substation are transmitted to the ground control center in real time via the Message Queuing Telemetry Transport (MQTT) network transmission protocol. The distributed high-density arrangement of monitoring nodes reflects the deformation trend of the whole section of the roadway by monitoring the anchor bar (cable) support force data of multiple sections, which effectively ensures the safety of the roadway.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112221
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2222: Prediction of Machine-Generated
           Financial Tweets Using Advanced Bidirectional Encoder Representations from
           Transformers

    • Authors: Muhammad Asad Arshed, Ștefan Cristian Gherghina, Dur-E-Zahra Dur-E-Zahra, Mahnoor Manzoor
      First page: 2222
      Abstract: With the rise of Large Language Models (LLMs), distinguishing between genuine and AI-generated content, particularly in finance, has become challenging. Previous studies have focused on binary identification of ChatGPT-generated content, overlooking other AI tools used for text regeneration. This study addresses this gap by examining various AI-regenerated content types in the finance domain. Objective: The study aims to differentiate between human-generated financial content and AI-regenerated content, specifically focusing on ChatGPT, QuillBot, and SpinBot. It constructs a dataset comprising real text and AI-regenerated text for this purpose. Contribution: This research contributes to the field by providing a dataset that includes various types of AI-regenerated financial content. It also evaluates the performance of different models, particularly highlighting the effectiveness of the Bidirectional Encoder Representations from the Transformers Base Cased model in distinguishing between these content types. Methods: The dataset is meticulously preprocessed to ensure quality and reliability. Various models, including Bidirectional Encoder Representations Base Cased, are fine-tuned and compared with traditional machine learning models using TFIDF and Word2Vec approaches. Results: The Bidirectional Encoder Representations Base Cased model outperforms other models, achieving an accuracy, precision, recall, and F1 score of 0.73, 0.73, 0.73, and 0.72 respectively, in distinguishing between real and AI-regenerated financial content. Conclusion: This study demonstrates the effectiveness of the Bidirectional Encoder Representations base model in differentiating between human-generated financial content and AI-regenerated content. It highlights the importance of considering various AI tools in identifying synthetic content, particularly in the finance domain in Pakistan.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112222
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2223: A Light-Field Video Dataset of Scenes
           with Moving Objects Captured with a Plenoptic Video Camera

    • Authors: Kamran Javidi, Maria G. Martini
      First page: 2223
      Abstract: Light-field video provides a detailed representation of scenes captured from different perspectives. This results in a visualisation modality that enhances the immersion and engagement of the viewers with the depicted environment. In order to perform research on compression, transmission and signal processing of light field data, datasets with light-field contents of different categories and acquired with different modalities are required. In particular, the development of machine learning models for quality assessment and for light-field processing, including the generation of new views, require large amounts of data. Most existing datasets consist of static scenes and, in many cases, synthetic contents. This paper presents a novel light-field plenoptic video dataset, KULFR8, involving six real-world scenes with moving objects and 336 distorted light-field videos derived from the original contents; in total, the original scenes in the dataset contain 1800 distinctive frames, with angular resolution of 5×5 with and total spatial resolution of 9600×5400 pixels (considering all the views); overall, the dataset consists of 45,000 different views with spatial resolution of 1920×1080 pixels. We analyse the content characteristics based on the dimensions of the captured objects and via the acquired videos using the central views extracted from each quilted frame. Additionally, we encode and decode the contents using various video encoders across different bitrate ranges. For quality assessments, we consider all the views, utilising frames measuring 9600×5400 pixels, and employ two objective quality metrics: PSNR and SSIM.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112223
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2224: A Novel Paradigm for Controlling
           Navigation and Walking in Biped Robotics

    • Authors: Giuseppe Menga
      First page: 2224
      Abstract: This paper extends the three-dimensional inverted pendulum (spherical inverted pendulum or SIP) in a polar coordinate system to simulate human walking in free fall and the energy recovery when the foot collides with the ground. The purpose is to propose a general model to account for all characteristics of the biped and of the gait, while adding minimal dynamical complexity with respect to the SIP. This model allows for both walking omnidirectionally on a flat surface and going up and down staircases. The technique does not use torque control. However, for the gait, the only action is the change in angular velocity at the start of a new step with respect to those given after the collision (emulating the torque action in the brief double stance period) to recover from the losses, as well as the preparation of the position in the frontal and sagittal planes of the swing foot for the next collision for balance and maneuvering. Moreover, in climbing or descending staircases, during the step, the length of the supporting leg is modified for the height of the step of the staircase. Simulation examples are offered for a rectilinear walk, ascending and descending rectilinear or spiral staircases, showing stability of the walk, and the expenditure of energy.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112224
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2225: Reviewof DC Motor Modeling and Linear
           Control: Theory with Laboratory Tests

    • Authors: Miklós Kuczmann
      First page: 2225
      Abstract: This review paper introduces the modeling, measurement, identification and control of direct current motors based on the state space modeling and the transfer function representation. These models are identified by real laboratory measurements, and the simulated results are compared with the measurements. Continuous-time and discrete-time PID (Proportional-Integral-Derivative) controllers, discrete-time state feedback and linear quadratic controllers are designed mathematically. The designed controllers are realized by the microcontroller Arduino UNO, and the behavior of the controllers is compared and analyzed. The noisy current signal has been measured by a discrete-time observer, steady-state Kalman filtering is also studied. The practical results of the implemented controllers support the theoretical results very well.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112225
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2226: Performance Evaluation of Mobile
           RPL-Based IoT Networks under Hello Flood Attack

    • Authors: Amal Hkiri, Sami Alqurashi, Omar Ben Bahri, Mouna Karmani, Hamzah Faraj, Mohsen Machhout
      First page: 2226
      Abstract: The RPL protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem, yet it remains vulnerable to various attacks, particularly in dense and mobile environments where it shows certain limitations and susceptibilities. This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the Hello Flood attack in mobile environments. Using four different group mobility models—the Column Mobility Model (CMM), Reference Point Group Mobility Model (RPGM), Nomadic Community Mobility Model (NCM), and Pursue Mobility Model (PMM)—within the Cooja simulator, this study uniquely investigates the Hello Flood attack in mobile settings, an area previously overlooked. Our systematic evaluation focuses on critical performance metrics, including the Packet Delivery Ratio (PDR), End-to-End Delay (E2ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption (APC). The findings reveal several key insights: PDR decreases significantly, indicating increased packet loss or delivery failures; ETX values rise, necessitating more packet retransmissions and routing hops; E2ED increases, introducing delays in routing decisions and data transmission times; throughput declines as the attack disrupts data flow; and APC escalates due to higher energy usage on packet transmissions, especially over extended paths. These results underscore the urgent need for robust security measures to protect RPL-based IoT networks in mobile environments. Furthermore, our work emphasizes the exacerbated impact of the attack in mobile scenarios, highlighting the evolving security requirements of IoT networks.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112226
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2227: An Improved Current
           Signal-Extraction-Based High-Frequency Pulsating Square-Wave Voltage
           Injection Method for Interior Permanent-Magnet Synchronous Motor
           Position-Sensorless Control

    • Authors: Dongyi Meng, Qiya Wu, Jia Zhang, Lijun Diao
      First page: 2227
      Abstract: The high-frequency (HF) voltage injection method is widely applied in achieving position-sensorless control for interior permanent-magnet synchronous motors (IPMSMs). This method necessitates precise and rapid extraction of the current signal for accurate position estimation and field-oriented control (FOC). In the traditional methods, the position error signal and fundamental current are extracted from the current signal using band-pass filters (BPFs) and low-pass filters (LPFs), or a method based on time-delay filters. However, the traditional extraction method falls short in ensuring simultaneous dynamic performance and accuracy, particularly when the switching frequency is limited or when encountering harmonic and noise interference. In this article, a novel HF pulsating square-wave voltage injection method based on an improved current signal-extraction strategy is proposed to improve the extraction accuracy while maintaining good dynamic performance. The newly devised current signal-extraction method is crafted upon a notch filter (NF). Through harnessing NF’s effective separation characteristics of specific frequency signals, the current signal is meticulously processed. This process yields the extraction of the position error signal and fundamental-current component, crucial for accurate position estimation and motor FOC. Simulation and hardware-in-the-loop (HIL) testing are conducted to validate the effectiveness of the proposed approach.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112227
      Issue No: Vol. 13, No. 11 (2024)
       
  • Electronics, Vol. 13, Pages 2228: Capacitive and Non-Contact Liquid Level
           Detection Sensor Based on Interdigitated Electrodes with Flexible
           Substrate

    • Authors: Yong Ren, Bin Luo, Xueyu Feng, Zihao Feng, Yanyi Song, Fang Yan
      First page: 2228
      Abstract: Achieving accurate and high-sensitivity liquid level detection in medical instruments has always been a knotty task. In this paper, a high-precision, non-contact, flexible capacitive liquid level sensor is proposed, aiming to apply capacitive sensors in test tube liquid level measurement and improving the sensitivity of real-time liquid level sensors. The simulation study is conducted using ANSYS Maxwell and demonstrates the correlation between test tube thickness and sensitivity. A geometric model of the test container and sensing electrodes is established to optimize the design strategy for the physical dimensions of the sensor’s interdigitated (IDT) electrodes based on a flexible printed circuit (FPC). The hardware and software designs are completed based on the FDC2214 capacitive-to-digital converter to collect the capacitance variation data of the sensing electrodes accurately. To assess the system’s performance, an experimental platform for a liquid level sensor system has been constructed, facilitating the measurement, communication, processing, and visualization of liquid levels. The performance results demonstrate that the system is capable of accurately measuring the effective liquid level range within a standard 5 mL test tube with a resolution of up to 1 mm, as well as a sensitivity of 78.68 fF/mm, verifying the simulation results and exhibiting excellent linearity.
      Citation: Electronics
      PubDate: 2024-06-06
      DOI: 10.3390/electronics13112228
      Issue No: Vol. 13, No. 11 (2024)
       
 
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