[go: up one dir, main page]
Previous Issue
Volume 14, March-1
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Volume 14
Issue 6
share announcement

Electronics, Volume 14, Issue 6 (March-2 2025) – 28 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
20 pages, 9115 KiB  
Article
Optimized Real-Time Decision Making with EfficientNet in Digital Twin-Based Vehicular Networks
by Qasim Zia, Avais Jan, Dong Yang, Haijing Zhang and Yingshu Li
Electronics 2025, 14(6), 1084; https://doi.org/10.3390/electronics14061084 (registering DOI) - 9 Mar 2025
Abstract
Real-time decision-making is vital in vehicular ad hoc networks (VANETs). It is essential to improve road safety and ensure traffic efficiency and flow. Integrating digital twins within VANET (DT-VANET) creates virtual replicas of physical vehicles, allowing in-depth analysis and effective decision-making. Many vehicular [...] Read more.
Real-time decision-making is vital in vehicular ad hoc networks (VANETs). It is essential to improve road safety and ensure traffic efficiency and flow. Integrating digital twins within VANET (DT-VANET) creates virtual replicas of physical vehicles, allowing in-depth analysis and effective decision-making. Many vehicular ad hoc network applications now use convolutional neural networks (CNNs). However, the growing model size and latency make implementing them in real-time systems challenging, and most previous studies focusing on using CNNs still face significant challenges. Some effective models with sustainable performances have recently been proposed. One of the most advanced models among them is EfficientNet. One may consider it a family of network models with significantly fewer parameters and computational costs. This paper proposes EfficientNet-based optimized real-time decision-making in the DT-VANET architecture. This paper investigates the performance of EfficientNet in digital-based vehicular ad hoc networks. Extensive experiments have proved that EfficientNet outperforms CNN models (ResNet50, VGG16) in accuracy, latency, computational efficiency, and convergence time, which proves its effectiveness in real-time applications of DT-VANET. Full article
(This article belongs to the Special Issue Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous Wireless Networks and Safety Critical Networks)
►▼ Show Figures

Figure 1

Figure 1
<p>High-level architecture using EfficientNet model for digital twin-based vehicular network.</p> Full article ">Figure 2
<p>Functionality of proposed framework at digital twin layer.</p> Full article ">Figure 3
<p>The idea of transfer learning.</p> Full article ">Figure 4
<p>Sample traffic sign images of dataset GTSRB [<a href="#B35-electronics-14-01084" class="html-bibr">35</a>].</p> Full article ">Figure 5
<p>Overview of workflow of proposed EfficientNet implementation [<a href="#B35-electronics-14-01084" class="html-bibr">35</a>].</p> Full article ">Figure 6
<p>Model accuracy and model loss graph for training and validation set using EfficientNet.</p> Full article ">Figure 7
<p>Model accuracy and model loss graph for training and validation set using ResNet50.</p> Full article ">Figure 8
<p>Model accuracy and model loss graph for training and validation set using VGG16.</p> Full article ">Figure 9
<p>Comparison of deep learning model’s accuracy and loss in the graph.</p> Full article ">Figure 10
<p>Confusion matrix of the deep learning models.</p> Full article ">Figure 11
<p>Performance Metrics of the Deep Learning Models.</p> Full article ">
59 pages, 28162 KiB  
Review
Analysis of Circuit Configurations Suitable for Self-Supplied AC-DC Converters Using Thin-Film Piezoelectric Generators and Multilayer Energy Storage Supercapacitors
by Ivaylo Pandiev, Mariya Aleksandrova, Nikolay Kurtev and Stefan Rizanov
Electronics 2025, 14(6), 1083; https://doi.org/10.3390/electronics14061083 (registering DOI) - 9 Mar 2025
Abstract
The improvement of microelectronic technologies and the practical application of some new materials has resulted in the realization of various highly efficient thin-film energy harvesters in the last few years. Self-powered supplies intended to work with thin-film harvesters have been developed. This type [...] Read more.
The improvement of microelectronic technologies and the practical application of some new materials has resulted in the realization of various highly efficient thin-film energy harvesters in the last few years. Self-powered supplies intended to work with thin-film harvesters have been developed. This type of power supply with integrated various thin-film harvesters has proven to be very suitable for providing electrical energy for wearable electronic sensor systems, with practical applications for implementing personalized medicine through continuously monitoring an individual’s state of health. The application of wearable electronics in medicine will become increasingly important in the next few years, as it can support timely decision-making, especially in high-risk patients. This paper presents a review and comparative analysis of the optimal circuit configurations used to design power supply devices with discrete and integrated components, obtaining electrical power from various thin-film piezoelectric generators, and storing electrical energy in low-power multilayer supercapacitors. Based on an analysis of the principle of operation of the selected circuit configurations, analytical expressions for the basic static and dynamic parameters have been obtained, taking into account the peculiarities of their integration with the biomedical signal processing system. Advantages and weaknesses are analyzed through simulation testing for each configuration, as the prospects for improvement are outlined. Also, for each group of circuit configurations, the key parameters and characteristics of recent high-impact papers, especially those focusing on low-power applications, are presented and analyzed in tabular form. As a result of the analysis of the various circuit configurations, some analytical recommendations have been defined regarding the optimal selection of passive and active elements, which can contribute to a better understanding of the design principles of battery-free power supplies converting electrical energy from some specific recently developed thin-film energy harvesters. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Fabrication of the PZT/P(VDF-TrFE) composite film for a prototype wearable thin-film piezoelectric generator [<a href="#B55-electronics-14-01083" class="html-bibr">55</a>]: (<b>a</b>) coating and crystallizing the inorganic PZT film on 2D layered mica substrate, (<b>b</b>) sputtering the Pt interdigital electrodes, (<b>c</b>) coating P(VDF-TrFE) and removing the DMF solvent to yield the organic functional layer, (<b>d</b>) structure diagram of the device, (<b>e</b>,<b>f</b>) photos of the fabricated device.</p> Full article ">Figure 2
<p>Structure–piezoelectric property relationships in PVDF: surface SEM images and their fiber diameter according to the solvent thermal properties (boiling point): (<b>a</b>) acetone solution (56 °C); (<b>b</b>) N, N-Dimethylacetamide solution (165 °C); (<b>c</b>) effect of fiber diameter on transverse piezoelectric coefficient (d<sub>31</sub>) [<a href="#B57-electronics-14-01083" class="html-bibr">57</a>].</p> Full article ">Figure 3
<p>Typical 2D materials and their crystal structures: (<b>a</b>) graphene and GO, (<b>b</b>) WS<sub>2</sub>, MoS<sub>2</sub>, and (<b>c</b>) MXene. Other 2D materials: (<b>d</b>) hBN, BP, COF, MOF, and LDHs [<a href="#B58-electronics-14-01083" class="html-bibr">58</a>]; (<b>e</b>) wearable biosensor based on piezoelectric self-powering; (<b>f</b>) effect of the MoS<sub>2</sub> structure on the piezoelectric voltage yield; (<b>g</b>) tracking the respiratory rate by the combined sensing and harvesting element. Reprinted with permission from [<a href="#B59-electronics-14-01083" class="html-bibr">59</a>].</p> Full article ">Figure 4
<p>Fiber-based PENG: (<b>a</b>) the coil structure; (<b>b</b>) output voltage of the PVDF, PVDF/BT, PVDF/rGO, and PVDF/rGO/BT nanocomposite filaments [<a href="#B64-electronics-14-01083" class="html-bibr">64</a>].</p> Full article ">Figure 5
<p>Incorporation of piezoelectric generators in textile [<a href="#B62-electronics-14-01083" class="html-bibr">62</a>].</p> Full article ">Figure 6
<p>Innovative techniques for fabrication of piezoelectric films on plastic substrates: (<b>a</b>) transfer printing of PZT ribbons onto flexible rubber substrates; (<b>b</b>) inorganic-based laser lift-off [<a href="#B65-electronics-14-01083" class="html-bibr">65</a>].</p> Full article ">Figure 7
<p>Application of the supercapacitor in an energy harvesting circuit based on piezoelectric energy converting element [<a href="#B75-electronics-14-01083" class="html-bibr">75</a>].</p> Full article ">Figure 8
<p>Schematic diagram of the synthesis of TC@NF// PANIC@CFP ASC device and the rate capability of the Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene, Co-MXene-1, Co-MXene-2, and Co-MXene-3 electrodes [<a href="#B80-electronics-14-01083" class="html-bibr">80</a>].</p> Full article ">Figure 9
<p>(<b>a</b>,<b>b</b>) Scanning electron microscopy (SEM) images of PDMS-rGO/C electrode. (<b>c</b>,<b>d</b>) Cross-sectional SEM images of pure rGO/C film. (<b>e</b>) Transmission electron microscopy image of rGO/C. (<b>f</b>) Contact angle image of electrolyte on the PDMS-rGO/C electrode [<a href="#B84-electronics-14-01083" class="html-bibr">84</a>]. Reprinted from Lu et al., [<a href="#B84-electronics-14-01083" class="html-bibr">84</a>], Copyright (2020), with permission from Elsevier.</p> Full article ">Figure 10
<p>Nickel nanomesh produced from a porous aluminum oxide template: (<b>a</b>) Cross-sectional SEM image of a 3D porous anodic aluminum oxide template. (<b>b</b>) Cross-sectional SEM image of the resulting nickel nanomesh. (<b>c</b>) Structural representation of the porous scaffold after template removal, showing its average unit cell [<a href="#B85-electronics-14-01083" class="html-bibr">85</a>]. Reprinted from Arenas et al., [<a href="#B85-electronics-14-01083" class="html-bibr">85</a>], Copyright (2019), with permission from Elsevier.</p> Full article ">Figure 11
<p>Structure of a flexible piezoelectric energy harvester and integrated backside storage element [<a href="#B88-electronics-14-01083" class="html-bibr">88</a>].</p> Full article ">Figure 12
<p>A diode bridge rectifier with a connected source of an input signal and a connected external load: (<b>a</b>) circuit configuration; (<b>b</b>) basic waveforms of the currents and voltages at <span class="html-italic">r<sub>p</sub></span> → ∞ and <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>L</mi> </msub> <mo>&gt;</mo> <mo>&gt;</mo> <msub> <mi>C</mi> <mi>P</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>≠</mo> <mo>∞</mo> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p>Circuit diagram of an AC-DC converter.</p> Full article ">Figure 14
<p>Circuit diagram of a piezoelectric generator module with added BJT rectifier.</p> Full article ">Figure 15
<p>Comparison between diode and transistor rectifiers.</p> Full article ">Figure 16
<p>Circuit diagram of a voltage-doubler rectifier with external load <span class="html-italic">R<sub>L</sub></span>.</p> Full article ">Figure 17
<p>Basic waveforms of the currents and voltages of the voltage-doubler rectifier (<a href="#electronics-14-01083-f010" class="html-fig">Figure 10</a>).</p> Full article ">Figure 18
<p>Circuit diagram of a voltage-doubler rectifier integrated with the simplified equivalent circuit of the piezoelectric energy harvester (PEH) and thin-film low-power supercapacitor.</p> Full article ">Figure 19
<p>Circuit diagram of a bridge rectifier, employing MOS transistors.</p> Full article ">Figure 20
<p>Schematic diagram of an active diode with regulation loop, composed by an op-amp.</p> Full article ">Figure 21
<p>An active rectifier (or half-wave active rectifier) using op-amp, which drives the p-channel MOSFET: (<b>a</b>) circuit configuration; (<b>b</b>) basic waveforms of the currents and voltages at no load (<span class="html-italic">R<sub>L</sub></span> → ∞) condition.</p> Full article ">Figure 22
<p>Circuit diagram of voltage doubler AC-DC converter using two op-amps, which drive the MOSFETs.</p> Full article ">Figure 23
<p>Functional circuit of an input stage using electronic switch, connected in parallel to the piezoelectric element: (<b>a</b>) general form of the circuit configuration; (<b>b</b>) waveforms of the sinusoidal excitation and the produces voltage bias-flip stage.</p> Full article ">Figure 24
<p>Functional circuit of an input stage using an electronic switch and series connected inductor: (<b>a</b>) general form of the parallel SSHI-based circuit configuration with standard bridge rectifier; (<b>b</b>) waveforms of the sinusoidal excitation and the produced voltages using parallel SSHI.</p> Full article ">Figure 25
<p>Functional circuit of an input stage using an electronic switch and series connected inductor: (<b>a</b>) general form of the series SSHI-based circuit configuration with bridge rectifier; (<b>b</b>) waveforms of the sinusoidal excitation and the produced voltages using series SSHI.</p> Full article ">Figure 26
<p>Circuit diagram of a grounded gyrator.</p> Full article ">Figure 27
<p>Circuit diagram of a synchronized switch harvesting on capacitor (SSHC) energy harvesting circuit using multiple capacitors (or with <span class="html-italic">n</span> stages).</p> Full article ">Figure 28
<p>Circuit diagram of the basic structure of a resonant rectifier circuit, employing parallel SSHI technique.</p> Full article ">Figure 29
<p>Step response of the differentiator for a resonant rectifier circuit: (<b>a</b>) output voltage of the differentiator at a rising edge of the input voltage; (<b>b</b>) output voltage of the differentiator at a falling edge of the input voltage.</p> Full article ">Figure 30
<p>An example waveform of the control pulse <span class="html-italic">v<sub>c</sub></span> for <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>&lt;</mo> <mo>&lt;</mo> <mi>T</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>.</p> Full article ">Figure 31
<p>Schematic of the parallel synchronous switched inductor circuit.</p> Full article ">Figure 32
<p>Transient response of the above-shown synchronous switched inductor circuit (<a href="#electronics-14-01083-f031" class="html-fig">Figure 31</a>).</p> Full article ">Figure 33
<p>Output power as a function of load resistance value in <a href="#electronics-14-01083-f031" class="html-fig">Figure 31</a>.</p> Full article ">Figure 34
<p>Output power versus input power for the circuit in <a href="#electronics-14-01083-f031" class="html-fig">Figure 31</a>.</p> Full article ">Figure 35
<p>A series SSHI technique with a self-supplying BJT switch.</p> Full article ">Figure 36
<p>A series SSHI technique with an output-coupled inductor booster.</p> Full article ">Figure 37
<p>Output voltage versus load resistor value (<span class="html-italic">R<sub>L</sub></span> = 100 Ω—green curve, <span class="html-italic">R<sub>L</sub></span> = 1 kΩ—blue line, <span class="html-italic">R<sub>L</sub></span> = 10 kΩ—red line, and <span class="html-italic">R<sub>L</sub></span> = 100 kΩ—light blue line) in <a href="#electronics-14-01083-f036" class="html-fig">Figure 36</a>.</p> Full article ">Figure 38
<p>Output power versus load resistance (<span class="html-italic">R<sub>L</sub></span> = 100 Ω—green curve, <span class="html-italic">R<sub>L</sub></span> = 1 kΩ—blue line, <span class="html-italic">R<sub>L</sub></span> = 10 kΩ—red line, and <span class="html-italic">R<sub>L</sub></span> = 100 kΩ—light blue line) in <a href="#electronics-14-01083-f036" class="html-fig">Figure 36</a>.</p> Full article ">Figure 39
<p>Output power versus the value of the capacitor <span class="html-italic">C<sub>L</sub></span><sub>1</sub> (<span class="html-italic">C<sub>L</sub></span><sub>1</sub> = 10 nF—green line, <span class="html-italic">C<sub>L</sub></span><sub>1</sub> = 100 nF—blue line, <span class="html-italic">C<sub>L</sub></span><sub>1</sub> = 10 μF—red line, and <span class="html-italic">C<sub>L</sub></span><sub>1</sub> = 100 μF—light blue line) in <a href="#electronics-14-01083-f036" class="html-fig">Figure 36</a>.</p> Full article ">Figure 40
<p>Output power versus the value of the inductor <span class="html-italic">L</span><sub>2</sub> (<span class="html-italic">L</span><sub>2</sub> = 10 μH—green line, <span class="html-italic">L</span><sub>2</sub> = 100 μH—blue line, <span class="html-italic">L</span><sub>2</sub> = 1 mH—red line, and <span class="html-italic">L</span><sub>2</sub> = 10 mH—light blue line) in <a href="#electronics-14-01083-f036" class="html-fig">Figure 36</a>.</p> Full article ">Figure 41
<p>Evolution of an output voltage at vibration frequency 23 Hz and <span class="html-italic">R<sub>T</sub></span> = 10 kΩ and 30 kΩ. In blue are presented the results for a bridge rectifier; in green the results for a p-SSHI rectifier with <span class="html-italic">L</span> = 10 mH and <span class="html-italic">C</span><sub>0</sub> = 100 μF.</p> Full article ">Figure 42
<p>Evolution of an output voltage at vibration frequency 23 Hz and <span class="html-italic">R<sub>T</sub></span> = 10 kΩ and 30 kΩ. In blue are presented the results for a bridge rectifier; in green the results for a s-SSHI rectifier with <span class="html-italic">L</span> = 10 mH and <span class="html-italic">C</span><sub>0</sub> = 100 μ<span class="html-italic">F</span>.</p> Full article ">Figure 43
<p>Evolution of an output voltage at vibration frequency 147 Hz and <span class="html-italic">R<sub>T</sub></span> = 10 kΩ and 30 kΩ. In blue are presented the results for a bridge rectifier; in green the results for a p-SSHI rectifier with <span class="html-italic">L</span> = 10 mH and <span class="html-italic">C</span><sub>0</sub> = 100 μF.</p> Full article ">Figure 44
<p>Evolution of an output voltage at vibration frequency 147 Hz and <span class="html-italic">R<sub>T</sub></span> = 10 kΩ and 30 kΩ. In blue are presented the results for a bridge rectifier; in green the results for a s-SSHI rectifier with <span class="html-italic">L</span> = 10 mH and <span class="html-italic">C</span><sub>0</sub> = 100 μF.</p> Full article ">Figure 45
<p>Auto-tuning system block representation.</p> Full article ">Figure 46
<p>A typical CMOS LDO circuit diagram.</p> Full article ">
18 pages, 259 KiB  
Article
Deep Learning for Predicting Rehabilitation Success: Advancing Clinical and Patient-Reported Outcome Modeling
by Yasser Mahmoud, Kaleb Horvath and Yi Zhou
Electronics 2025, 14(6), 1082; https://doi.org/10.3390/electronics14061082 (registering DOI) - 9 Mar 2025
Abstract
Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. This study investigates the application of deep learning techniques, including hybrid [...] Read more.
Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. This study investigates the application of deep learning techniques, including hybrid Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to predict rehabilitation success based on clinical and patient-reported outcome measures (CROMs and PROMs). Using a dataset of 1047 rehabilitation patients encompassing diverse musculoskeletal conditions and treatment protocols, we compare the performance of deep learning models with previously established machine learning approaches such as Random Forest and Extra Trees classifiers. Our findings reveal that deep learning significantly enhances predictive performance. The weighted F1-score for direct classification improved from 65% to 74% using a CNN-RNN architecture, and the mean absolute error (MAE) for regression-based success metrics decreased by 12%, translating to more precise estimations of functional recovery. These improvements hold clinical significance as they enhance the ability to tailor rehabilitation interventions to individual patient needs, potentially optimizing recovery timelines and resource allocation. Moreover, attention mechanisms integrated into the deep learning models provided improved interpretability, highlighting key predictors such as age, range of motion, and PROM indices. This study underscores the potential of deep learning to advance outcome prediction in rehabilitation, offering more precise and interpretable tools for clinical decision-making. Future work will explore real-time applications and the integration of multimodal data to further refine these models. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning Techniques for Healthcare)
41 pages, 1522 KiB  
Review
Radiator Enablers for Wireless Communication Evolution
by Apostolos-Christos Tsafaras, Panagiotis Mpatargias, Adamantios Karakilidis, Georgios Giouros, Ioannis Gavriilidis, Vasileios Katsinelis, Georgios Sarinakis and Theodoros Kaifas
Electronics 2025, 14(6), 1081; https://doi.org/10.3390/electronics14061081 (registering DOI) - 9 Mar 2025
Abstract
The general objective of the work is to propose, examine, and study the innovations needed, providing a roadmap in order to place the next generation of wireless communication vision and concepts into technological reach. The main trends and directions are identified; relative challenges [...] Read more.
The general objective of the work is to propose, examine, and study the innovations needed, providing a roadmap in order to place the next generation of wireless communication vision and concepts into technological reach. The main trends and directions are identified; relative challenges are addressed; and needed solutions are anticipated, proposed, and evaluated. In detail, to address the role of the antenna system in the wireless communication evolution, in the work at hand, we examine the challenges addressed by the increase in the degrees of freedom of the radiator systems. Specifically, we study the increase in the degrees of freedom provided by gMIMO, reconfigurable intelligence surfaces (RIS), holographic metasurfaces, and orbital angular momentum (OAM). Then, we thoroughly examine the impact that those potent technologies deliver to the mmWave, satellite, and THz wireless communications systems. Full article
(This article belongs to the Special Issue State-of-the-Art Antenna Technology for Advanced Wireless Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>Diagram of OAM methods.</p> Full article ">Figure 2
<p>Challenges of new mmWave antenna properties.</p> Full article ">Figure 3
<p>Acceptable requirements for design of a mmWave antenna.</p> Full article ">Figure 4
<p>Antenna terminology diagram.</p> Full article ">Figure 5
<p>Wireless evolution-driven antenna convergence diagram.</p> Full article ">
18 pages, 2503 KiB  
Article
Reinforced Disentangled HTML Representation Learning with Hard-Sample Mining for Phishing Webpage Detection
by Jun-Ho Yoon, Seok-Jun Buu and Hae-Jung Kim
Electronics 2025, 14(6), 1080; https://doi.org/10.3390/electronics14061080 (registering DOI) - 9 Mar 2025
Abstract
Phishing webpage detection is critical in combating cyber threats, yet distinguishing between benign and phishing webpages remains challenging due to significant feature overlap in the representation space. This study introduces a reinforced Triplet Network to optimize disentangled representation learning tailored for phishing detection. [...] Read more.
Phishing webpage detection is critical in combating cyber threats, yet distinguishing between benign and phishing webpages remains challenging due to significant feature overlap in the representation space. This study introduces a reinforced Triplet Network to optimize disentangled representation learning tailored for phishing detection. By employing reinforcement learning, the method enhances the sampling of anchor, positive, and negative examples, addressing a core limitation of traditional Triplet Networks. The disentangled representations generated through this approach provide a clear separation between benign and phishing webpages, substantially improving detection accuracy. To achieve comprehensive modeling, the method integrates multimodal features from both URLs and HTML DOM Graph structures. The evaluation leverages a real-world dataset comprising over one million webpages, meticulously collected for diverse and representative phishing scenarios. Experimental results demonstrate a notable improvement, with the proposed method achieving a 6.7% gain in the F1 score over state-of-the-art approaches, highlighting its superior capability and the dataset’s critical role in robust performance. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
►▼ Show Figures

Figure 1

Figure 1
<p>Examples of phishing and benign webpages. Cases (<b>a</b>,<b>c</b>,<b>e</b>) represent phishing webpages designed to mimic benign webpages, while cases (<b>b</b>,<b>d</b>,<b>f</b>) are their benign counterparts. Phishing samples often exhibit similarities in layout and design, making detection challenging.</p> Full article ">Figure 2
<p>Overview of the proposed phishing detection framework. URL and HTML DOM features are extracted via CNN and GCN, and optimized triplets are selected through reinforcement learning to enhance the Triplet Network’s performance. The final classification is achieved using a softmax layer.</p> Full article ">Figure 3
<p>Flowchart of reinforcement learning-based triplet sampling.</p> Full article ">Figure 4
<p>Visual comparison of benign and phishing cases. Case (<b>a</b>) shows a benign sample with a well-distributed HTML DOM structure and a simple URL (accessed on 12 January 2025). Case (<b>b</b>) depicts a phishing sample with a highly imbalanced DOM structure and a complex, obfuscated URL (accessed on 12 January 2025). These visualizations highlight the structural differences leveraged by the proposed method to distinguish between benign and phishing data.</p> Full article ">Figure 5
<p>t-SNE visualization of the representation spaces. (<b>a</b>) Input space with significant overlap between benign and phishing samples. (<b>b</b>) Embeddings from URL and HTML features without triplet optimization showing partial separation. (<b>c</b>) Embeddings generated by the proposed method achieving clear separation.</p> Full article ">Figure 6
<p>HTML DOM structures for misclassified instances. (<b>a</b>,<b>b</b>) depict misclassified phishing pages with sparse structures, while (<b>c</b>,<b>d</b>) correspond to benign pages with complex and dense structures.</p> Full article ">
23 pages, 10571 KiB  
Article
Yolov8s-DDC: A Deep Neural Network for Surface Defect Detection of Bearing Ring
by Yikang Zhang, Shijun Liang, Junfeng Li and Haipeng Pan
Electronics 2025, 14(6), 1079; https://doi.org/10.3390/electronics14061079 (registering DOI) - 9 Mar 2025
Viewed by 86
Abstract
Timely detection and handling of bearings with surface defects are crucial for ensuring the reliability of mechanical devices. Bearing surfaces often exhibit complex machining textures and residual oil, with defects varying in type, shape, and size. To tackle this issue, this paper proposes [...] Read more.
Timely detection and handling of bearings with surface defects are crucial for ensuring the reliability of mechanical devices. Bearing surfaces often exhibit complex machining textures and residual oil, with defects varying in type, shape, and size. To tackle this issue, this paper proposes an improved bearing surface defect detection model, Yolov8s-DDC. First, Depthwise Separable Convolution is introduced into the backbone network, which not only reduces computational complexity and the number of parameters but also enhances the ability to capture spatial and channel information during feature extraction. Next, a Diverse Branch Block is incorporated into the neck network, utilizing diversified branch structures to capture different feature dimensions, thereby providing more comprehensive information and promoting richer feature representation. Additionally, a new module, CMA, is proposed by combining Monte Carlo Attention, which enhances the network’s feature extraction capability and improves its ability to capture information at different scales. Finally, extensive experiments were conducted using a defect dataset constructed with bearing surface defect images collected from actual industrial sites. The experimental results demonstrate that the proposed Yolov8s-DDC model achieves an average precision (mAP) of 96.9%, surpassing current mainstream defect detection algorithms by at least 1.5% in precision. Additionally, the model processes up to 106 frames per second (FPS), making it suitable for real-time defect detection in industrial settings. The experimental results validate that Yolov8s-DDC not only enhances detection accuracy but also meets the speed requirements for online bearing defect detection. The findings highlight the practical applicability and effectiveness of this model in real-world industrial applications. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagram of the bearing surface defect detection device: (<b>a</b>) The internal components of the device; (<b>b</b>) The overall appearance of the device.</p> Full article ">Figure 1 Cont.
<p>Schematic diagram of the bearing surface defect detection device: (<b>a</b>) The internal components of the device; (<b>b</b>) The overall appearance of the device.</p> Full article ">Figure 2
<p>Types of bearing defects: (<b>a</b>) Black spots, (<b>b</b>) Scratches, (<b>c</b>) Dents, (<b>d</b>) Material waste, and (<b>e</b>) Wear, with the defect areas highlighted by the red box.</p> Full article ">Figure 3
<p>Yolov8s-DDC network structure diagram.</p> Full article ">Figure 4
<p>Depthwise separable convolution structure diagram.</p> Full article ">Figure 5
<p>Representative designs of different branch blocks (DBBs).</p> Full article ">Figure 6
<p>Demonstration of the conversion of Diverse Branch Block (DBB) to conventional convolutional layer method.</p> Full article ">Figure 7
<p>Example diagram of converting a 1 × 1—K × K sequence with group number g &gt; 1: (<b>A</b>) Groupwise Conv, (<b>B</b>) Training-time 1×1—K × K, and (<b>C</b>) The perspective from Transform IV.</p> Full article ">Figure 8
<p>CMA structure diagram.</p> Full article ">Figure 9
<p>Monte Carlo Attention (MCA) structure diagram.</p> Full article ">Figure 10
<p>Feature visualization: Brighter colors indicate higher attention, highlighting the regions that the model focuses on more.</p> Full article ">Figure 11
<p>Training and validation loss curves. The vertical axis represents the values, and the horizontal axis represents the number of training epochs.</p> Full article ">Figure 12
<p>mAP curve: (<b>a</b>) Yolov8s; (<b>b</b>) Yolov8s-DDC. The vertical axis represents the values, and the horizontal axis represents the number of training epochs.</p> Full article ">Figure 13
<p>Detection performance of different models: The area represented by each box corresponds to the detected defect.</p> Full article ">Figure 14
<p>False positive and false negative examples: (<b>a</b>) False Positive, (<b>b</b>) False Negative, (<b>c</b>) False Negative, (<b>d</b>) False Negative, and (<b>e</b>) False Negative.</p> Full article ">Figure 15
<p>Detection results of different models on the public dataset: The area represented by each box corresponds to the detected defect.</p> Full article ">
19 pages, 3602 KiB  
Article
EvTransPose: Towards Robust Human Pose Estimation via Event Camera
by Jielun He, Zhaoyuan Zeng, Xiaopeng Li and Cien Fan
Electronics 2025, 14(6), 1078; https://doi.org/10.3390/electronics14061078 (registering DOI) - 8 Mar 2025
Viewed by 110
Abstract
To overcome the interference caused by varying lighting conditions in human pose estimation (HPE), significant advancements have been made in event-based approaches. However, since event cameras are only sensitive to illumination changes, static human bodies often lead to motion ambiguity, making it challenging [...] Read more.
To overcome the interference caused by varying lighting conditions in human pose estimation (HPE), significant advancements have been made in event-based approaches. However, since event cameras are only sensitive to illumination changes, static human bodies often lead to motion ambiguity, making it challenging for existing methods to handle such cases effectively. Therefore, we propose EvTransPose, a novel framework that combines an hourglass module for global dependencies and a pyramid encoding module for local features. Specifically, a transformer for event-based HPE is adopted to capture the spatial relationships between human body parts. To emphasize the impact of high resolution on HPE tasks, this work designs the cascading hourglass architecture to compress and recover the resolution of feature maps frequently. On this basis, an intermediate-supervision constraint is incorporated to guide the network in aggregating sufficient features during the intermediate stages, which ensures better feature refinement and enhances overall performance. Furthermore, to facilitate a thorough evaluation of our method, we construct the first event-based HPE dataset with RGB reference images under diverse lighting conditions. Comprehensive experiments demonstrate that our proposed EvTransPose framework outperforms previous methods in multiple aspects. Full article
(This article belongs to the Section Computer Science & Engineering)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">
16 pages, 1626 KiB  
Article
Self-MCKD: Enhancing the Effectiveness and Efficiency of Knowledge Transfer in Malware Classification
by Hyeon-Jin Jeong, Han-Jin Lee, Gwang-Nam Kim and Seok-Hwan Choi
Electronics 2025, 14(6), 1077; https://doi.org/10.3390/electronics14061077 (registering DOI) - 8 Mar 2025
Viewed by 47
Abstract
As malware continues to evolve, AI-based malware classification methods have shown significant promise in improving the malware classification performance. However, these methods lead to a substantial increase in computational complexity and the number of parameters, increasing the computational cost during the training process. [...] Read more.
As malware continues to evolve, AI-based malware classification methods have shown significant promise in improving the malware classification performance. However, these methods lead to a substantial increase in computational complexity and the number of parameters, increasing the computational cost during the training process. Moreover, the maintenance cost of these methods also increases, as frequent retraining and transfer learning are required to keep pace with evolving malware variants. In this paper, we propose an efficient knowledge distillation technique for AI-based malware classification methods called Self-MCKD. Self-MCKD transfers output logits that are separated into the target class and non-target classes. With the separation of the output logits, Self-MCKD enables efficient knowledge transfer by assigning weighted importance to the target class and non-target classes. Also, Self-MCKD utilizes small and shallow AI-based malware classification methods as both the teacher and student models to overcome the need to use large and deep methods as the teacher model. From the experimental results using various malware datasets, we show that Self-MCKD outperforms the traditional knowledge distillation techniques in terms of the effectiveness and efficiency of its malware classification. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">
12 pages, 11841 KiB  
Article
High-Voltage Electrostatic Discharge/Electrical Overstress Co-Protection Implementing Gradual-Triggered SCR and MOS-Stacked Configuration
by Hailian Liang, Jianfeng Li, Jun Sun, Dejin Wang, Fang Wang, Dong Wang and Junliang Liu
Electronics 2025, 14(6), 1076; https://doi.org/10.3390/electronics14061076 (registering DOI) - 8 Mar 2025
Viewed by 134
Abstract
This paper proposes a monolithic electrostatic discharge/electrical overstress (ESD/EOS) co-protection device featuring gradual triggering by silicon-controlled rectifier (SCR) and metal–oxide semiconductor (MOS) structures, demonstrating enhanced voltage clamping and current-conducting capabilities. Compared with conventional PMOS-triggered SCR (PMOS-SCR) for ESD protection, the proposed dual-PMOS-triggered SCR [...] Read more.
This paper proposes a monolithic electrostatic discharge/electrical overstress (ESD/EOS) co-protection device featuring gradual triggering by silicon-controlled rectifier (SCR) and metal–oxide semiconductor (MOS) structures, demonstrating enhanced voltage clamping and current-conducting capabilities. Compared with conventional PMOS-triggered SCR (PMOS-SCR) for ESD protection, the proposed dual-PMOS-triggered SCR (DPMOS-SCR) architecture within a compact area achieves monolithic ESD/EOS protection performance due to the strategic semiconductor structures integration. ESD measurement results show that the snapback voltage of the designed DPMOS-SCR with the width of 170 μm is approximately 2.5 V, the failure current (It2) is up to 4.5 A, and both the simulation and measurement results demonstrate that the designed DPMOS-SCR is helpful for reducing the leakage current and accelerating the response time. By embedding an additional p-type well in the DPMOS-SCR, the optimized DPMOS-SCR (ODPMOS-SCR) presents a higher breakdown voltage, trigger voltage, and holding voltage while keeping a similar It2. The EOS current-conducting ability measured by a surge test system indicates the peak surge current is up to 3.7 A, demonstrating superior monolithic ESD/EOS protection performance. As a result, the designed DPMOS-SCR and ODPMOS-SCR structures achieve high-voltage ESD/EOS co-protection with high efficiency in a small chip area, providing a chip-scale solution for improving the reliability of high-voltage ICs. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Cross-sections and internal equivalent circuits of (<b>a</b>) PMOS-SCR, (<b>b</b>) DPMOS-SCR, and (<b>c</b>) ODPMOS-SCR.</p> Full article ">Figure 2
<p>Typical ESD/EOS protection design window.</p> Full article ">Figure 3
<p>Equivalent circuits of ODPMOS-SCR.</p> Full article ">Figure 4
<p>A 3D simulation of the current density distribution (<b>a</b>) before and (<b>b</b>) after turning on ODPMOS-SCR.</p> Full article ">Figure 5
<p><b>A</b> 2D simulation of the current density distribution of ODPMOS-SCR at different times: (<b>a</b>) t = 10 ns, (<b>b</b>) t = 100 ns, and (<b>c</b>) t = 10 μs.</p> Full article ">Figure 6
<p>Simulated electrostatic potential distribution curves of ODPMOS-SCR_WO and ODPMOS-SCR.</p> Full article ">Figure 7
<p>Simulated electrical field of (<b>a</b>) ODPMOS-SCR_WO and (<b>b</b>) ODPMOS-SCR.</p> Full article ">Figure 8
<p>Simulated DC <span class="html-italic">I</span>-<span class="html-italic">V</span> characteristic curves of PMOS-SCR, DPMOS-SCR, and ODPMOS-SCR.</p> Full article ">Figure 9
<p>DC <span class="html-italic">I</span>-<span class="html-italic">V</span> characteristic curves of ODPMOS-SCR and DPMOS-SCR.</p> Full article ">Figure 10
<p>TLP <span class="html-italic">I</span>-<span class="html-italic">V</span> curves of ODPMOS-SCR and DPMOS-SCR.</p> Full article ">Figure 11
<p>TLP <span class="html-italic">I</span>-<span class="html-italic">V</span> curves of ODPMOS-SCR with different <span class="html-italic">F</span>.</p> Full article ">Figure 12
<p>The EOS characteristic curves of ODPMOS-SCR: (<b>a</b>) EOS voltage (<b>b</b>) clamping voltage (<b>c</b>) pulse peak current.</p> Full article ">Figure 13
<p>The EOS characteristic curves of ODPMOS-SCR with different <span class="html-italic">F</span>.</p> Full article ">
22 pages, 8643 KiB  
Article
A Comparison of Deep Learning Techniques for Pose Recognition in Up-and-Go Pole Walking Exercises Using Skeleton Images and Feature Data
by Wan-Chih Lin, Yu-Chen Tu, Hong-Yi Lin and Ming-Hseng Tseng
Electronics 2025, 14(6), 1075; https://doi.org/10.3390/electronics14061075 - 7 Mar 2025
Viewed by 284
Abstract
This study evaluates the performance of seven deep learning methods for recognizing motion patterns in Up-and-Go pole walking exercises, aiming to improve rehabilitation technologies for the elderly population. For the ageing population, improving the accuracy of movement posture for elderly people is crucial [...] Read more.
This study evaluates the performance of seven deep learning methods for recognizing motion patterns in Up-and-Go pole walking exercises, aiming to improve rehabilitation technologies for the elderly population. For the ageing population, improving the accuracy of movement posture for elderly people is crucial in obtaining better rehabilitation outcomes. Up-and-Go pole walking exercises offer significant health benefits, but attaining the correct pose in motion is essential for achieving these benefits. The dataset includes skeleton images generated by OpenPose 1.7.0 and 2D and 3D skeleton images extracted through MediaPipe 0.10.21. Two sets of feature data were developed for model evaluation: one that comprises 12 features representing the key coordinates of the hands and feet and another consisting of 30 features derived from subdivided full-body skeletons. The study compares the accuracy and performance of each method, examining the impact of different combinations and representations on motion patterns. The experimental results indicate that the Swin model based on MediaPipe 2D skeleton images achieved the highest accuracy (99.7%), demonstrating superior performance in recognizing motion patterns of Up-and-Go pole walking exercises. The study summarizes the advantages and limitations of each approach, highlighting the contributions of different features and data representations to recognition outcomes. This research provides scientific evidence to advance elderly rehabilitation technologies by accurately recognizing poses. Full article
(This article belongs to the Special Issue Advances in Information, Intelligence, Systems and Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Workflow framework diagram.</p> Full article ">Figure 2
<p>OpenPose skeletal images.</p> Full article ">Figure 3
<p>Mediapipe 3D skeletal images.</p> Full article ">Figure 4
<p>The designed image-based models in this study.</p> Full article ">Figure 5
<p>Mediapipe full-body joint coordinates [<a href="#B31-electronics-14-01075" class="html-bibr">31</a>].</p> Full article ">Figure 6
<p>Twelve-feature dataset generated in this study.</p> Full article ">Figure 7
<p>The designed FCN model in this study.</p> Full article ">Figure 8
<p>Training and validation process of VGG16 model using Mediapipe 2D skeleton images.</p> Full article ">Figure 9
<p>Training confusion matrix of VGG16 model using Mediapipe 2D skeleton images.</p> Full article ">Figure 10
<p>Test confusion matrix of VGG16 model using Mediapipe 2D skeleton images.</p> Full article ">Figure 11
<p>Training and validation process of Swin model using Mediapipe 2D skeleton images.</p> Full article ">Figure 12
<p>Training confusion matrix of Swin model using Mediapipe 2D skeleton images.</p> Full article ">Figure 13
<p>Test confusion matrix of Swin model using Mediapipe 2D skeleton images.</p> Full article ">Figure 14
<p>Training and validation process of FCN model using 30-feature set.</p> Full article ">Figure 15
<p>Training confusion matrix of FCN model using 30-feature set.</p> Full article ">Figure 16
<p>Test confusion matrix of FCN model using 30-feature set.</p> Full article ">Figure 17
<p>A correct example of video predictions using the proposed model.</p> Full article ">Figure 18
<p>An incorrect example of video predictions using the proposed model.</p> Full article ">
20 pages, 1038 KiB  
Article
Accelerometer Bias Estimation for Unmanned Aerial Vehicles Using Extended Kalman Filter-Based Vision-Aided Navigation
by Djedjiga Belfadel and David Haessig
Electronics 2025, 14(6), 1074; https://doi.org/10.3390/electronics14061074 - 7 Mar 2025
Viewed by 199
Abstract
Accurate estimation of accelerometer biases in Inertial Measurement Units (IMUs) is crucial for reliable Unmanned Aerial Vehicle (UAV) navigation, particularly in GPS-denied environments. Uncompensated biases lead to an unbounded accumulation of position error and increased velocity error, resulting in significant navigation inaccuracies. This [...] Read more.
Accurate estimation of accelerometer biases in Inertial Measurement Units (IMUs) is crucial for reliable Unmanned Aerial Vehicle (UAV) navigation, particularly in GPS-denied environments. Uncompensated biases lead to an unbounded accumulation of position error and increased velocity error, resulting in significant navigation inaccuracies. This paper examines the effects of accelerometer bias on UAV navigation accuracy and introduces a vision-aided navigation system. The proposed system integrates data from an IMU, altimeter, and optical flow sensor (OFS), employing an Extended Kalman Filter (EKF) to estimate both the accelerometer biases and the UAV position and velocity. This approach reduces the accumulation of velocity and positional errors. The efficiency of this approach was validated through simulation experiments involving a UAV navigating in circular and straight-line trajectories. Simulation results show that the proposed approach significantly enhances UAV navigation performance, providing more accurate estimates of both the state and accelerometer biases while reducing error growth through the use of vision aiding from an Optical Flow Sensor. Full article
(This article belongs to the Special Issue Precision Positioning and Navigation Communication Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>Basic block diagram for a strapdown inertial navigation system, courtesy [<a href="#B16-electronics-14-01074" class="html-bibr">16</a>].</p> Full article ">Figure 2
<p>Strapdown inertial navigation with IMU acceleration input corrected for IMU bias.</p> Full article ">Figure 3
<p>System and Simulation Block Diagram.</p> Full article ">Figure 4
<p>Position Errors, Dead-Reckoning, biases not present.</p> Full article ">Figure 5
<p>Velocity Errors, Dead-Reckoning, biases not present.</p> Full article ">Figure 6
<p>Position Errors, Dead-Reckoning, biases present.</p> Full article ">Figure 7
<p>Velocity Errors, Dead-Reckoning, biases present.</p> Full article ">Figure 8
<p>Position Errors, Kalman Est. on, biases not present, bias est. off.</p> Full article ">Figure 9
<p>Velocity Errors, Kalman Est. on, biases not present, bias est. off.</p> Full article ">Figure 10
<p>Position Errors, Kalman est. on, biases present, bias est. off.</p> Full article ">Figure 11
<p>Velocity Errors, Kalman est. on, biases present, bias est. off.</p> Full article ">Figure 12
<p>Position Errors, Kalman est. on, biases present, bias est. on.</p> Full article ">Figure 13
<p>Velocity Errors, Kalman est. on, biases present, bias est. on.</p> Full article ">Figure 14
<p>Bias Estimates during Scheme 4.</p> Full article ">Figure 15
<p>Position Errors, Kalman est. on, biases present, bias est. off.</p> Full article ">Figure 16
<p>Velocity Errors, Kalman est. on, biases present, bias est. off.</p> Full article ">Figure 17
<p>Position Errors, Kalman est. on, biases present, bias est. on.</p> Full article ">Figure 18
<p>Velocity Errors, Kalman est. on, biases present, bias est. on.</p> Full article ">
24 pages, 2186 KiB  
Article
An Index of Refraction Adaptive Neural Refractive Radiance Field for Transparent Scenes
by Jiangnan Wei, Ziyi Yue, Shuai Li, Zhiqi Cheng, Zhouhui Lian, Mengxiao Song, Yinqian Cheng and Hongying Zhao
Electronics 2025, 14(6), 1073; https://doi.org/10.3390/electronics14061073 - 7 Mar 2025
Viewed by 171
Abstract
Reconstructions and novel view syntheses of transparent object scenes are of great significance in numerous fields such as design, animation, and scientific research. The refraction of transparent objects brings challenges to the reconstruction of the geometry and radiance fields. Traditional photogrammetry methods and [...] Read more.
Reconstructions and novel view syntheses of transparent object scenes are of great significance in numerous fields such as design, animation, and scientific research. The refraction of transparent objects brings challenges to the reconstruction of the geometry and radiance fields. Traditional photogrammetry methods and Neural Radiance Fields (NeRFs) establish geometric relationships based on the rectilinear propagation of light rays and thus fail in refraction scenes. In addition, different transparent objects have significantly different indexes of refraction (IORs) due to differences in materials, which pose challenges to the training of refraction radiance fields based on ray tracing. We propose a refractive radiance field method for the novel view synthesis of transparent object scenes. The method can reconstruct the geometry and radiance field using only a sequence of scene images of transparent objects, and it can automatically search for the optimal IOR without prior information. Our method consists of three stages, including geometric reconstruction, radiance field training, and IOR optimization. We verified our method on different datasets. In terms of its indicators, it is close to previous refraction radiance fields and other relatively advanced transparent scene rendering methods, while possessing higher flexibility. Full article
12 pages, 7869 KiB  
Article
Design of an E-Band Multiplexer Based on Turnstile Junction
by Shaohang Li, Yuan Yao, Xiaohe Cheng and Junsheng Yu
Electronics 2025, 14(6), 1072; https://doi.org/10.3390/electronics14061072 - 7 Mar 2025
Viewed by 79
Abstract
This paper presents an E-band four-channel multiplexer based on a turnstile junction. The proposed multiplexer consists of a power distribution unit featuring a turnstile junction topology and four Chebyshev bandpass filters. Thanks to the implementation of a rotating gate connection structure as the [...] Read more.
This paper presents an E-band four-channel multiplexer based on a turnstile junction. The proposed multiplexer consists of a power distribution unit featuring a turnstile junction topology and four Chebyshev bandpass filters. Thanks to the implementation of a rotating gate connection structure as the distribution unit, the overall compactness was enhanced, and the complexity of optimization was significantly reduced. Furthermore, this configuration offers a well-organized spatial port distribution, facilitating scalability for additional channels. According to the frequency band planning and design requirements of the communication system, an E-band four-channel multiplexer was designed and manufactured using high-precision computer numerical control (CNC) milling technology, achieving an error margin of ±5 μm. The experimental results indicate that the passbands are 70.6–73.07 GHz, 73.7–76.07 GHz, 82.55–82.9 GHz, and 83.4–85.9 GHz. The in-band insertion loss of each channel is below 1.7 dB, while the return loss at the common port exceeds 12 dB. The measured results align closely with simulations, demonstrating promising potential for practical applications. Full article
(This article belongs to the Section Microwave and Wireless Communications)
►▼ Show Figures

Figure 1

Figure 1
<p>Physical structure and dimensions of the turnstile junction.</p> Full article ">Figure 2
<p>Electric field distributions and schematic diagram of the turnstile junction.</p> Full article ">Figure 3
<p>Schematic diagram of energy transmission in the turnstile junction. The <span class="html-italic">a</span><sub>Ei</sub><sup>+</sup> and <span class="html-italic">a</span><sub>Ei</sub><sup>−</sup> represent the input E-field intensity and the output E-field intensity of Port 1, respectively. (<span class="html-italic">i</span> = 1, 2). The <span class="html-italic">a</span><sub>j</sub><sup>+</sup> and <span class="html-italic">a</span><sub>j</sub><sup>−</sup> represent the output E-field intensity and the input E-field intensity of Port j, respectively. (<span class="html-italic">j</span> = 2, 3, 4, 5).</p> Full article ">Figure 4
<p>Simulated S-parameter results of the turnstile junction.</p> Full article ">Figure 5
<p>Physical structure and dimensions of the bandpass filter.</p> Full article ">Figure 6
<p>(<b>a</b>) Effects of <span class="html-italic">w</span><sub>12</sub> on the coupling coefficient; (<b>b</b>) effects of <span class="html-italic">w</span><sub>i</sub> on <span class="html-italic">Q</span><sub>ex</sub>.</p> Full article ">Figure 7
<p>Simulation results of each bandpass filter.</p> Full article ">Figure 7 Cont.
<p>Simulation results of each bandpass filter.</p> Full article ">Figure 8
<p>(<b>a</b>) Distributed model and (<b>b</b>) physical structure and dimensions of the turnstile junction multiplexer.</p> Full article ">Figure 9
<p>Simulated S-parameter results of the turnstile junction multiplexer.</p> Full article ">Figure 10
<p>The electric field distribution at different frequencies.</p> Full article ">Figure 11
<p>The fabrication model of the turnstile junction multiplexer.</p> Full article ">Figure 12
<p>The fabricated prototype and test scenario.</p> Full article ">Figure 13
<p>The simulated and measured results of the turnstile junction multiplexer.</p> Full article ">
21 pages, 1178 KiB  
Article
User Behavior on Value Co-Creation in Human–Computer Interaction: A Meta-Analysis and Research Synthesis
by Xiaohong Chen and Yuan Zhou
Electronics 2025, 14(6), 1071; https://doi.org/10.3390/electronics14061071 - 7 Mar 2025
Viewed by 81
Abstract
Value co-creation in online communities refers to a process in which all participants within a platform’s ecosystem exchange and integrate resources while engaging in mutually beneficial interactive processes to generate perceived value-in-use. User behavior plays a crucial role in influencing value co-creation in [...] Read more.
Value co-creation in online communities refers to a process in which all participants within a platform’s ecosystem exchange and integrate resources while engaging in mutually beneficial interactive processes to generate perceived value-in-use. User behavior plays a crucial role in influencing value co-creation in human–computer interaction. However, existing research contains controversies, and there is a lack of comprehensive studies exploring which factors of user behavior influence it and the mechanisms through which they operate. This paper employs meta-analysis to examine the factors and mechanisms based on 42 studies from 2006 to 2023 with a sample size of 30,016. It examines the relationships at the individual, interaction, and environment layers and explores moderating effects through subgroup analysis. The results reveal a positive overall effect between user behavior and value co-creation performance. Factors including self-efficacy, social identity, enjoyment, and belonging (individual layer); information support, social interaction, trust, and reciprocity (interaction layer); as well as shared values, incentives, community culture, and subjective norms (environment layer) positively influence value co-creation. The moderating effect of situational and measurement factors indicates that Chinese communities and monocultural environments have more significant effects than international and multicultural ones, while community type is not significant. Structural equation models and subjective collaboration willingness have a stronger moderating effect than linear regression and objective behavior, which constitutes a counterintuitive finding. This study enhances theoretical research on user behavior and provides insights for managing value co-creation in human–computer interaction. Full article
(This article belongs to the Special Issue Human-Robot Interaction and Applications: Challenges and Future Perspectives)
►▼ Show Figures

Figure 1

Figure 1
<p>Data Collection and Sample Selection.</p> Full article ">Figure 2
<p>Overall Effect on Value Co-creation Performance.</p> Full article ">Figure 3
<p>Individual Layer on Value Co-creation Performance.</p> Full article ">Figure 4
<p>Interaction Layer on Value Co-creation Performance.</p> Full article ">Figure 5
<p>Environment Layer on Value Co-creation Performance.</p> Full article ">
18 pages, 2055 KiB  
Article
Think Before You Classify: The Rise of Reasoning Large Language Models for Consumer Complaint Detection and Classification
by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas and Dimitrios K. Nasiopoulos
Electronics 2025, 14(6), 1070; https://doi.org/10.3390/electronics14061070 - 7 Mar 2025
Viewed by 97
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks, but their effectiveness in real-world consumer complaint classification without fine-tuning remains uncertain. Zero-shot classification offers a promising solution by enabling models to categorize consumer complaints without prior exposure [...] Read more.
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks, but their effectiveness in real-world consumer complaint classification without fine-tuning remains uncertain. Zero-shot classification offers a promising solution by enabling models to categorize consumer complaints without prior exposure to labeled training data, making it valuable for handling emerging issues and dynamic complaint categories in finance. However, this task is particularly challenging, as financial complaint categories often overlap, requiring a deep understanding of nuanced language. In this study, we evaluate the zero-shot classification performance of leading LLMs and reasoning models, totaling 14 models. Specifically, we assess DeepSeek-V3, Gemini-2.0-Flash, Gemini-1.5-Pro, Anthropic’s Claude 3.5 and 3.7 Sonnet, Claude 3.5 Haiku, and OpenAI’s GPT-4o, GPT-4.5, and GPT-4o Mini, alongside reasoning models such as DeepSeek-R1, o1, and o3. Unlike traditional LLMs, reasoning models are specifically trained with reinforcement learning to exhibit advanced inferential capabilities, structured decision-making, and complex reasoning, making their application to text classification a groundbreaking advancement. The models were tasked with classifying consumer complaints submitted to the Consumer Financial Protection Bureau (CFPB) into five predefined financial classes based solely on complaint text. Performance was measured using accuracy, precision, recall, F1-score, and heatmaps to identify classification patterns. The findings highlight the strengths and limitations of both standard LLMs and reasoning models in financial text processing, providing valuable insights into their practical applications. By integrating reasoning models into classification workflows, organizations may enhance complaint resolution automation and improve customer service efficiency, marking a significant step forward in AI-driven financial text analysis. Full article
(This article belongs to the Special Issue Innovations in NLP and Large Language Models: Shaping the Future of AI)
►▼ Show Figures

Figure 1

Figure 1
<p>Comparison of Model Predictions with Actual Categories Using Heatmaps.</p> Full article ">Figure 2
<p>Comparative Classification Performance of LLMs and Reasoning Models Across Three Key Metrics: Accuracy, Cost, and Speed.</p> Full article ">Figure 3
<p>Trade-off Between Accuracy and Cost for LLMs and Reasoning Models.</p> Full article ">
20 pages, 555 KiB  
Article
In-Depth Inception–Attention Time Model: An Application for Driver Drowsiness Detection
by Minseop Lee, Minsu Cha and Jiyoung Woo
Electronics 2025, 14(6), 1069; https://doi.org/10.3390/electronics14061069 - 7 Mar 2025
Viewed by 88
Abstract
Drowsiness while driving is a common problem for many drivers and a significant problem in contemporary society. This study presents a method for detecting drowsiness while driving. The key finding is that six channels of EEG data are closely associated with drowsiness detection; [...] Read more.
Drowsiness while driving is a common problem for many drivers and a significant problem in contemporary society. This study presents a method for detecting drowsiness while driving. The key finding is that six channels of EEG data are closely associated with drowsiness detection; this finding will contribute significantly to the development of new drowsiness detection systems. To process EEG data with high frequencies and large datasets, an in-depth Inception model suitable for time-series data was employed, incorporating a self-attention mechanism. This model effectively extracts the time–frequency representation of EEG data using a short-time Fourier transform and selectively learns important features by applying the self-attention mechanism within the Inception block structure. Additionally, channel-wise convolution is utilized to reduce the dimensionality of input data, and modified Inception blocks are stacked to enable more profound data representation. The model manages its complexity by adding partial sequential convolution filters and self-attention to the Inception blocks while performing complementary roles. Our method achieved high-performance drowsiness detection with an accuracy of 79.02% using only six EEG channels. The method contributes to ensuring accurate detection by minimizing information loss through the introduction of a self-attention mechanism. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>In-depth Inception model with self-attention.</p> Full article ">Figure 2
<p>Raw sensor data image.</p> Full article ">Figure 3
<p>Transformed image from sensor data using STFT.</p> Full article ">Figure 4
<p>Structure of Inception block proposed in GoogLeNet.</p> Full article ">Figure 5
<p>International 10–20 system.</p> Full article ">
22 pages, 452 KiB  
Article
Threat Modeling of Smart Grid Control Architectures
by Lars Halvdan Flå, Jonatan Ralf Axel Klemets and Martin Gilje Jaatun
Electronics 2025, 14(6), 1068; https://doi.org/10.3390/electronics14061068 - 7 Mar 2025
Viewed by 100
Abstract
In this paper, we perform a threat modeling of architectures for controlling the medium voltage (MV) part of the power grid, arguing the importance of this topic with a brief summary of serious cyber security attacks from the last decade. As more Distributed [...] Read more.
In this paper, we perform a threat modeling of architectures for controlling the medium voltage (MV) part of the power grid, arguing the importance of this topic with a brief summary of serious cyber security attacks from the last decade. As more Distributed Energy Resources (DERs) are introduced into this part of the grid, the need to control these resources arises. A threat modeling of two alternative control architectures is performed to study two different aspects. Firstly, we study and compare the cyber security of the two architectures to determine whether one of them is inherently more secure than the other. While both architectures rely on 5G, one of the architectures uses a centralized design, while the other uses a distributed design. Our results indicate that at the current level of detail, contrary to common belief, it is difficult to draw definitive conclusions as to which architecture is more secure. The second aspect we study is the applied threat modeling method itself. We evaluate and test the method and suggest improvements. Full article
(This article belongs to the Special Issue Stability Analysis and Control of Smart Grids)
►▼ Show Figures

Figure 1

Figure 1
<p>Overview of the phases of the threat modeling method (see below for details on each phase).</p> Full article ">Figure 2
<p>Model elements for the threat modeling method.</p> Full article ">Figure 3
<p>Centralized architecture.</p> Full article ">Figure 4
<p>Distributed control.</p> Full article ">Figure 5
<p>Model of the centralized control architecture.</p> Full article ">Figure 6
<p>Model of the distributed control architecture.</p> Full article ">
17 pages, 5534 KiB  
Article
The Pole-to-Ground Fault Current Calculation Method and Impact Factor Investigation for Monopole DC Grids
by Liang Chen, Wei Yi, Pan Deng, Shen Ma, Da Kuang and Hongyu Cai
Electronics 2025, 14(6), 1067; https://doi.org/10.3390/electronics14061067 - 7 Mar 2025
Viewed by 70
Abstract
Flexible DC grids are an important technological means for optimizing power supply structures and promoting energy transition. However, as a system with low inertia and weak damping, the flexible DC grid inherently faces challenges, such as rapid rising of fault currents, vulnerability to [...] Read more.
Flexible DC grids are an important technological means for optimizing power supply structures and promoting energy transition. However, as a system with low inertia and weak damping, the flexible DC grid inherently faces challenges, such as rapid rising of fault currents, vulnerability to significant damage, difficulty in fault interruption, and with regard to the poor overcurrent-withstanding capabilities of power electronic devices. To address these issues, this paper proposes a method for calculating the single-pole ground fault current in a symmetrical monopolar DC grid, and further introduces a matrix exponential calculation method. This method enables quantitative analysis of the influence of various component parameters on the fault current, taking into account the dynamic characteristics of both the faulted and healthy poles in the DC system. The results demonstrate the high accuracy of this calculation method. The analysis reveals that the inductance of the faulted branch has the greatest impact on the fault current, while the inductances of the adjacent outgoing lines also have a certain influence. In contrast, the inductances of lines not adjacent to the faulted branch have minimal impacts on the fault current. Furthermore, the grounding electrode parameters of the converter station connected to the faulted branch exert the most significant influence on the fault current, with the grounding electrode parameters of neighboring converter stations also showing a notable effect. This indicates that the fault current is impacted by the topology of the nearby DC grid, but is not affected by the fault currents at remote converter stations. Full article
(This article belongs to the Special Issue Celebrating the 128th Anniversary of Sichuan University—Electrical and Electronic Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>The different grounding types for a monopole DC system.</p> Full article ">Figure 2
<p>The detailed and equivalent symmetrical monopole MMC model for analysis.</p> Full article ">Figure 3
<p>DC grid model for pole-to-ground fault analysis.</p> Full article ">Figure 4
<p>Four-terminal symmetrical monopole DC grid model.</p> Full article ">Figure 5
<p>The fault current results comparison for positive converters.</p> Full article ">Figure 6
<p>The fault current results comparison for negative converters.</p> Full article ">Figure 7
<p>The fault currents <span class="html-italic">i</span><sub>10</sub> influenced by line inductances.</p> Full article ">Figure 8
<p>The <span class="html-italic">i</span><sub>10</sub> fault currents, influenced by grounding electrode resistances.</p> Full article ">Figure 9
<p>The <span class="html-italic">i</span><sub>10</sub> fault currents, influenced by grounding electrode inductances.</p> Full article ">Figure 10
<p>The <span class="html-italic">i</span><sub>10</sub> fault currents, influenced by arm resistances in a positive pole in the fault.</p> Full article ">Figure 11
<p>The <span class="html-italic">i</span><sub>10</sub> fault currents, influenced by arm inductances in a positive pole in the fault.</p> Full article ">Figure 12
<p>The <span class="html-italic">i</span><sub>10</sub> fault currents, influenced by arm inductances in negative healthy poles.</p> Full article ">
18 pages, 949 KiB  
Article
Accelerating Pattern Recognition with a High-Precision Hardware Divider Using Binary Logarithms and Regional Error Corrections
by Dat Ngo, Suhun Ahn, Jeonghyeon Son and Bongsoon Kang
Electronics 2025, 14(6), 1066; https://doi.org/10.3390/electronics14061066 - 7 Mar 2025
Viewed by 81
Abstract
Pattern recognition applications involve extensive arithmetic operations, including additions, multiplications, and divisions. When implemented on resource-constrained edge devices, these operations demand dedicated hardware, with division being the most complex. Conventional hardware dividers, however, incur substantial overhead in terms of resource consumption and latency. [...] Read more.
Pattern recognition applications involve extensive arithmetic operations, including additions, multiplications, and divisions. When implemented on resource-constrained edge devices, these operations demand dedicated hardware, with division being the most complex. Conventional hardware dividers, however, incur substantial overhead in terms of resource consumption and latency. To address these limitations, we employ binary logarithms with regional error correction to approximate division operations. By leveraging approximation errors at boundary regions to formulate logarithm and antilogarithm offsets, our approach effectively reduces hardware complexity while minimizing the inherent errors of binary logarithm-based division. Additionally, we propose a six-stage pipelined hardware architecture, synthesized and validated on a Zynq UltraScale+ FPGA platform. The implementation results demonstrate that the proposed divider outperforms conventional division methods in terms of resource utilization and power savings. Furthermore, its application in image dehazing and object detection highlights its potential for real-time, high-performance computing systems. Full article
(This article belongs to the Special Issue Biometrics and Pattern Recognition)
►▼ Show Figures

Figure 1

Figure 1
<p>Block diagram of binary logarithm-based division. The red-dashed blocks require approximation techniques that introduce errors into the quotient.</p> Full article ">Figure 2
<p>Illustration of errors introduced by Mitchell’s algorithm. (<b>a</b>) Error resulting from the approximation <math display="inline"><semantics> <mrow> <msub> <mi>log</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>≈</mo> <mi>x</mi> </mrow> </semantics></math>. (<b>b</b>) Distribution of division errors when applying Mitchell’s algorithm.</p> Full article ">Figure 3
<p>Comparison of methods improving upon Mitchell’s algorithm. (<b>a</b>) Approximation lines used in each method, with the region <math display="inline"><semantics> <mrow> <mn>0.8</mn> <mo>≤</mo> <mi>x</mi> <mo>≤</mo> <mn>0.9</mn> </mrow> </semantics></math> enlarged for better visualization. (<b>b</b>) Corresponding approximation errors.</p> Full article ">Figure 4
<p>Approximation lines corresponding to different offset definitions. (<b>a</b>) <math display="inline"><semantics> <msub> <mo>Δ</mo> <mi>right</mi> </msub> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <msub> <mo>Δ</mo> <mi>center</mi> </msub> </semantics></math>. (<b>c</b>) <math display="inline"><semantics> <msub> <mo>Δ</mo> <mi>avg</mi> </msub> </semantics></math>. The fraction is divided into four regions, with an enlarged view of the third region for clarity.</p> Full article ">Figure 5
<p>Approximation error analysis of the proposed method. (<b>a</b>) Comparison of errors among different methods. (<b>b</b>) Approximation errors of the proposed method for varying values of <span class="html-italic">M</span>.</p> Full article ">Figure 6
<p>Hardware architecture of the proposed divider. REG, MSB, and LSB denote register, most significant bit, and least significant bit, respectively. The “…” symbol indicates that the data path for the divisor is identical to that of the dividend.</p> Full article ">Figure 7
<p>YOLOv9 object detection results on aerial images under varying haze levels using IFDH. Yellow labels represent airplanes, and blue labels represent birds.</p> Full article ">
19 pages, 5614 KiB  
Article
Research on Speed Control of Switched Reluctance Motors Based on Improved Super-Twisting Sliding Mode and Linear Active Disturbance Rejection Control
by Jingyuan Zhang, Cheng Liu, Siyu Chen and Lianpeng Zhang
Electronics 2025, 14(6), 1065; https://doi.org/10.3390/electronics14061065 - 7 Mar 2025
Viewed by 56
Abstract
An improved super-twisting sliding mode and linear active disturbance rejection control strategy is proposed to improve the dynamic response performance and immunity performance in switched reluctance motor speed control systems. Firstly, the linear extended state observer in linear active disturbance rejection control is [...] Read more.
An improved super-twisting sliding mode and linear active disturbance rejection control strategy is proposed to improve the dynamic response performance and immunity performance in switched reluctance motor speed control systems. Firstly, the linear extended state observer in linear active disturbance rejection control is improved by using the super-twisting sliding mode (STSM) control algorithm in order to improve the performance of the observer and thus enhance the controller’s immunity to disturbances. Secondly, the STSM control algorithm is used to replace the original linear state error feedback control law to improve the dynamic response performance of the controller, and the sigmoid function is used to replace the sign function in the STSM algorithm to further weaken the inherent chattering of the sliding mode and improve the stability of the system. Finally, the proposed control strategy is verified using the MATLAB/Simulink simulation platform. The simulation results show that the proposed control strategy has a better dynamic response and disturbance immunity performance. Full article
(This article belongs to the Special Issue Control and Optimization of Power Converters and Drives)
►▼ Show Figures

Figure 1

Figure 1
<p>Operating principle diagram of a three-phase 12/8 pole switched reluctance motor (SRM) (only one phase shown).</p> Full article ">Figure 2
<p>Nonlinear inductance characteristic curves obtained from finite element analysis.</p> Full article ">Figure 3
<p>Nonlinear torque characteristic curves derived from finite element analysis.</p> Full article ">Figure 4
<p>Block diagram of three-phase 12/8 SRM control with conventional torque sharing function.</p> Full article ">Figure 5
<p>Block diagram of the structure of a conventional first-order linear active disturbance rejection control (LADRC) controller.</p> Full article ">Figure 6
<p>Comparison of sign function and sigmoid function graphs.</p> Full article ">Figure 7
<p>Block diagram of improved super-twisting sliding mode and linear extended state observer (ISTSM-LESO) structure.</p> Full article ">Figure 8
<p>Block diagram of improved super-twisting sliding mode and linear state error feedback (ISTSM-LSEF) structure.</p> Full article ">Figure 9
<p>Block diagram of the structure of the ISTSM-LADRC speed controller.</p> Full article ">Figure 10
<p>Speed waveforms under three control strategies at a given speed of 500 rpm: (<b>a</b>) velocity waveforms; (<b>b</b>) local enlargement of velocity waveform I; (<b>c</b>) local enlargement of velocity waveform II; and (<b>d</b>) local enlargement of velocity waveform III.</p> Full article ">Figure 11
<p>Speed waveforms under three control strategies at a given speed of 1000 rpm: (<b>a</b>) velocity waveforms; (<b>b</b>) local enlargement of velocity waveform I; (<b>c</b>) local enlargement of velocity waveform II; and (<b>d</b>) local enlargement of velocity waveform III.</p> Full article ">Figure 12
<p>The waveforms of the motor under a sudden speed change with a 5 Nm load are shown. (<b>a</b>) Velocity waveforms; (<b>b</b>) local enlargement of velocity waveform I; and (<b>c</b>) local enlargement of velocity waveform II.</p> Full article ">Figure 13
<p>The waveforms of the motor under a sudden speed change with a 10 Nm load are shown. (<b>a</b>) Velocity waveforms; (<b>b</b>) local enlargement of velocity waveform I; and (<b>c</b>) local enlargement of velocity waveform II.</p> Full article ">Figure 14
<p>Steady-state waveforms at 500 r/min under 10 Nm: (<b>a</b>) ISTM-LADRC strategy; (<b>b</b>) Sliding mode control (SMC) strategy; and (<b>c</b>) Portional integral (PI) control strategy.</p> Full article ">Figure 15
<p>Steady-state waveforms at 1000 r/min under 10 Nm: (<b>a</b>) ISTM-LADRC strategy; (<b>b</b>) SMC strategy; and (<b>c</b>) PI control strategy.</p> Full article ">
9 pages, 520 KiB  
Article
Research on Approximate Computation of Signal Processing Algorithms for AIoT Processors Based on Deep Learning
by Yingzhe Liu, Fangfa Fu and Xuejian Sun
Electronics 2025, 14(6), 1064; https://doi.org/10.3390/electronics14061064 - 7 Mar 2025
Viewed by 54
Abstract
In the post-Moore era, the excessive amount of information brings great challenges to the performance of computing systems. To cope with these challenges, approximate computation has developed rapidly, which enhances the system performance with minor degradation in accuracy. In this paper, we investigate [...] Read more.
In the post-Moore era, the excessive amount of information brings great challenges to the performance of computing systems. To cope with these challenges, approximate computation has developed rapidly, which enhances the system performance with minor degradation in accuracy. In this paper, we investigate the utilization of an Artificial Intelligence of Things (AIoT) processor for approximate computing. Firstly, we employed neural architecture search (NAS) to acquire the neural network structure for approximate computation, which approximates the functions of FFT, DCT, FIR, and IIR. Subsequently, based on this structure, we quantized and trained a neural network implemented on the AI accelerator of the MAX78000 development board. To evaluate the performance, we implemented the same functions using the CMSIS-DSP library. The results demonstrate that the computational efficiency of the approximate computation on the AI accelerator is significantly higher compared to traditional DSP implementations. Therefore, the approximate computation based on AIoT devices can be effectively utilized in real-time applications. Full article
(This article belongs to the Special Issue The Progress in Application-Specific Integrated Circuit Design)
►▼ Show Figures

Figure 1

Figure 1
<p>Scatter plot of evaluation results. (<b>a</b>) FFT; (<b>b</b>) FIR; (<b>c</b>) DCT; (<b>d</b>) IIR.</p> Full article ">
50 pages, 5064 KiB  
Systematic Review
Design, Algorithms, and Applications of Microstrip Antennas for Image Acquisition: Systematic Review
by Luis Fernando Guerrero-Vásquez, Nathalia Alexandra Chacón-Reino, Byron Steven Sigüenza-Jiménez, Felipe Tomas Zeas-Loja, Jorge Osmani Ordoñez-Ordoñez and Paúl Andrés Chasi-Pesantez
Electronics 2025, 14(6), 1063; https://doi.org/10.3390/electronics14061063 - 7 Mar 2025
Viewed by 173
Abstract
This systematic literature review investigates microstrip antenna applications in image acquisition, focusing on their design characteristics, reconstruction algorithms, and application areas. We applied the PRISMA methodology for article selection. From selected studies, classifications were identified based on antenna patch geometry, substrate types, and [...] Read more.
This systematic literature review investigates microstrip antenna applications in image acquisition, focusing on their design characteristics, reconstruction algorithms, and application areas. We applied the PRISMA methodology for article selection. From selected studies, classifications were identified based on antenna patch geometry, substrate types, and image reconstruction algorithms. According to inclusion criteria, a significant increase in publications on this topic has been observed since 2013. Considering this trend, our study focuses on a 10-year publication range, including articles up to 2023. Results indicate that medical applications, particularly breast cancer detection, dominate this field. However, emerging areas are gaining attention, including stroke detection, bone fracture monitoring, security surveillance, avalanche radars, and weather monitoring. Our study highlights the need for more efficient algorithms, system miniaturization, and improved models to achieve precise medical imaging. Visual tools such as heatmaps and box plots are used to provide a deeper analysis, identify knowledge gaps, and offer valuable insights for future research and development in this versatile technology. Full article
(This article belongs to the Section Microwave and Wireless Communications)
►▼ Show Figures

Figure 1

Figure 1
<p>PRISMA method scheme.</p> Full article ">Figure 2
<p>Publication scheme of articles by year.</p> Full article ">Figure 3
<p>Distribution of studies by countries.</p> Full article ">Figure 4
<p>Article classification by geometry design.</p> Full article ">Figure 5
<p>Types of antenna designs, including (<b>a</b>) Slot insertion, (<b>b</b>) Basic geometry, (<b>c</b>) Array antenna, (<b>d</b>) Slit insertion, (<b>e</b>) Vivaldi geometry, (<b>f</b>) Bowtie slot, (<b>g</b>) Fractal slot (<b>h</b>) Fractal array (<b>i</b>) Spiral (<b>j</b>) Bowtie Array. Each design has unique characteristics related to image adquisition applications. These are representative figures of antennas, intended as a visual reference for the design type, but not necessarily functional with the current dimensions.</p> Full article ">Figure 6
<p>Article classification by substrates used in microstrip antennas.</p> Full article ">Figure 7
<p>Article classification by image reconstruction algorithms.</p> Full article ">Figure 8
<p>Article classification by application type.</p> Full article ">Figure 9
<p>Article classification by application type and antenna bandwidth.</p> Full article ">Figure 10
<p>Article classification by application type and antenna operating frequency.</p> Full article ">Figure 11
<p>Article classification by application type and antenna size.</p> Full article ">Figure 12
<p>Article classification by reconstruction algorithms and antenna bandwidth.</p> Full article ">Figure 13
<p>Article classification by reconstruction algorithms and antenna operating frequency.</p> Full article ">Figure 14
<p>Article classification by reconstruction algorithm and antenna area.</p> Full article ">Figure 15
<p>Standard deviation values of different dimensions of our review.</p> Full article ">Figure A1
<p>Article classification by application and image reconstruction algorithm.</p> Full article ">Figure A2
<p>Article classification by applications and frequency bands.</p> Full article ">Figure A3
<p>Article classification by applications and antenna geometry.</p> Full article ">Figure A4
<p>Article classification by application and substrate.</p> Full article ">Figure A5
<p>Classification of frequency bands and image reconstruction algorithms.</p> Full article ">Figure A6
<p>Article classification by antenna geometry and frequency band.</p> Full article ">Figure A7
<p>Article classification by antenna geometry and substrate.</p> Full article ">Figure A8
<p>Article classification by substrate and image reconstruction algorithms.</p> Full article ">Figure A9
<p>Article classification by substrate and frequency band.</p> Full article ">Figure A10
<p>Article classification by antenna geometry and image reconstruction algorithm.</p> Full article ">
13 pages, 3705 KiB  
Article
Multi-Agent Reinforcement Learning-Based Control Method for Pedestrian Guidance Using the Mojiko Fireworks Festival Dataset
by Masato Kiyama, Motoki Amagasaki and Toshiaki Okamoto
Electronics 2025, 14(6), 1062; https://doi.org/10.3390/electronics14061062 - 7 Mar 2025
Viewed by 159
Abstract
With increasing incidents due to congestion at events, effective pedestrian guidance has become a critical safety concern. Recent research has explored the application of reinforcement learning to crowd simulation, where agents learn optimal actions through trial and error to maximize rewards based on [...] Read more.
With increasing incidents due to congestion at events, effective pedestrian guidance has become a critical safety concern. Recent research has explored the application of reinforcement learning to crowd simulation, where agents learn optimal actions through trial and error to maximize rewards based on environmental states. This study investigates the use of reinforcement learning and simulation techniques to mitigate pedestrian congestion through improved guidance systems. We employ the Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG), a multi-agent reinforcement learning approach, and propose an enhanced method for learning the Q-function for actors within the MA-DDPG framework. Using the Mojiko Fireworks Festival dataset as a case study, we evaluated the effectiveness of our proposed method by comparing congestion levels with existing approaches. The results demonstrate that our method successfully reduces congestion, with agents exhibiting superior cooperation in managing crowd flow. This improvement in agent coordination suggests the potential for practical applications in real-world crowd management scenarios. Full article
(This article belongs to the Special Issue AI-Based Pervasive Application Services)
►▼ Show Figures

Figure 1

Figure 1
<p>Relationship between the agent and the simulator. The simulator sends <math display="inline"><semantics> <msup> <mi>S</mi> <mi>t</mi> </msup> </semantics></math> and <math display="inline"><semantics> <msup> <mi>R</mi> <mi>t</mi> </msup> </semantics></math> to the agent. The agent then outputs the guidance and Q-function.</p> Full article ">Figure 2
<p>Diagram of the actor and critic in MA-DDPG. The actor receives local information and outputs actions. The critic receives global information and outputs a Q-function.</p> Full article ">Figure 3
<p>Overview of the decision-making process in MAT. An agent determines its action based on the current observed state and the previously determined actions of other agents.</p> Full article ">Figure 4
<p>Diagram of how MAT functions [<a href="#B21-electronics-14-01062" class="html-bibr">21</a>]. The agent makes a guidance decision with reference to guidance from <math display="inline"><semantics> <msup> <mi>a</mi> <msub> <mi>i</mi> <mn>0</mn> </msub> </msup> </semantics></math> to <math display="inline"><semantics> <msup> <mi>a</mi> <msub> <mi>i</mi> <mi>m</mi> </msub> </msup> </semantics></math>.</p> Full article ">Figure 5
<p>The actor–critic model structure used in our method. This is the same actor–critic structure used in MA-DDPG. However, it differs in that the actor outputs a Q-function for each action.</p> Full article ">Figure 6
<p>Topographic information of Mojiko, Fukuoka Prefecture. Pedestrians take one of three routes to the end point, and guidance is administered at nine locations.</p> Full article ">Figure 7
<p>Classification of LOS. The area is divided into 12 spatial divisions called Sections. The area numbered 1 is referred to as Section 1.</p> Full article ">Figure 8
<p>Roads observed by each agent. Agent 1 is shown as A1.</p> Full article ">
21 pages, 2775 KiB  
Article
Effects of Wide Bandgap Devices on the Inverter Performance and Efficiency for Residential PV Applications
by Saleh S. Alharbi, Salah S. Alharbi, Abdullah Bubshait, Hisham Alharbi and Abdulaziz Alateeq
Electronics 2025, 14(6), 1061; https://doi.org/10.3390/electronics14061061 - 7 Mar 2025
Viewed by 178
Abstract
With power demands continuously growing, the penetration of renewable energy resources, particularly solar photovoltaic (PV) systems, across the residential sector has been extensive. A voltage source inverter (VSI) is the key element for efficiently processing energy conversion and connecting PV systems to home [...] Read more.
With power demands continuously growing, the penetration of renewable energy resources, particularly solar photovoltaic (PV) systems, across the residential sector has been extensive. A voltage source inverter (VSI) is the key element for efficiently processing energy conversion and connecting PV systems to home loads or utility grids. The operation of this inverter relies heavily on power-switching devices, which suffer from larger power losses due to the conventional semiconductors used based on silicon (Si) material. The new materials of wide bandgap (WBG) semiconductors, for example, gallium nitride (GaN) and silicon carbide (SiC), provide remarkably distinct characteristics of semiconductor devices to minimize power loss and boost the inverter’s operational capabilities. This research paper assesses the effects of integrating SiC-MOSFET devices into VSIs in order to improve the switching behavior and efficiency level. An experimental double-pulse testing (DPT) circuit was configured and set up for investigating the switching characterization of SiC-MOSFETs compared to the widely used Si-IGBTs. Under various operating circumstances, the switching behavior of two different types of power transistors was tested while their turning-on and turning-off losses were measured. The VSI based on SiC and Si transistors was simulated to examine the performance of the inverter. The results reveal that incorporating SiC-MOSFETs into the VSI substantially enhances the switching operation and reduces total power losses while increasing the efficiency compared to the inverter based on Si-IGBTs. Full article
(This article belongs to the Special Issue Power Electronic Circuits and Systems for Emerging Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Off-grid and on-grid solar power systems.</p> Full article ">Figure 2
<p>Schematic diagram of three-phase voltage source inverter used in the PV application.</p> Full article ">Figure 3
<p>Ideal waveforms of the switching device.</p> Full article ">Figure 4
<p>Circuit schematic of the DPT setup.</p> Full article ">Figure 5
<p>DPT bench setup for switching characterization.</p> Full article ">Figure 6
<p>Experimental turn-on switching waveform of Si-IGBT at a DC-link voltage of 400 V and switch current of 16 A.</p> Full article ">Figure 7
<p>Experimental turn-off switching waveform of Si-IGBT at a DC-link voltage of 400 V and switch current of 16 A.</p> Full article ">Figure 8
<p>Experimental turn-on switching waveform of SiC-MOSFET at a DC-link voltage of 400 V and switch current of 16 A.</p> Full article ">Figure 9
<p>Experimental turn-off switching waveform of SiC-MOSFET at a DC-link voltage of 400 V and switch current of 16 A.</p> Full article ">Figure 10
<p>Total switching energy losses for Si-IGBT and SiC-MOSFET under different switch currents and at junction temperatures of 25 °C and 150 °C.</p> Full article ">Figure 11
<p>Turn-on, turn-off, and total switching energy losses for Si-IGBT and SiC-MOSFET at different input voltages.</p> Full article ">Figure 12
<p>Simulated three-phase VSI connected to the grid.</p> Full article ">Figure 13
<p>Three-phase inverter simulation results: (<b>a</b>) switching signal; (<b>b</b>) DC-Link voltage; (<b>c</b>) grid current.</p> Full article ">Figure 14
<p>Efficiency of the inverter at different switching frequency values.</p> Full article ">Figure 15
<p>Efficiency of the inverter at different input voltage values under two switching frequencies of 20 kHz and 50 kHz.</p> Full article ">
12 pages, 10551 KiB  
Article
On the Use of Ridge Waveguides to Synthesize Impedances
by Juan J. Flórez Rodríguez and Luis F. Herrán
Electronics 2025, 14(6), 1060; https://doi.org/10.3390/electronics14061060 - 7 Mar 2025
Viewed by 136
Abstract
This work examines the feasibility of designing an impedance synthesis network based on a double-ridge waveguide (DRW). This design is based on the concept of the stepped-impedance line transformer as a cascade of transmission lines with different characteristic impedances, but using, in this [...] Read more.
This work examines the feasibility of designing an impedance synthesis network based on a double-ridge waveguide (DRW). This design is based on the concept of the stepped-impedance line transformer as a cascade of transmission lines with different characteristic impedances, but using, in this particular case, a stepped-ridge waveguide. It is shown that this structure is able to synthesize not only real impedances but an arbitrary impedance, following some restrictions explained in this paper. An impedance synthesis network based on DRW can have numerous applications, like being used in designing amplifiers, which would eventually make possible to integrate amplifiers in waveguide technology. Full article
(This article belongs to the Special Issue Microwave Devices: Analysis, Design, and Application)
►▼ Show Figures

Figure 1

Figure 1
<p>Front section of a double-ridge waveguide (DRW) with its main parameters.</p> Full article ">Figure 2
<p>Generic model equivalence between the profile section of a DRW with n step variations (<b>bottom</b>) and a circuit made of an n transmission lines cascade (<b>top</b>).</p> Full article ">Figure 3
<p>Ridge discontinuity model based on a three−lumped elements T−circuit.</p> Full article ">Figure 4
<p>Step in the junction between two ridges.</p> Full article ">Figure 5
<p>S-parameter simulation results for the DRW discontinuity.</p> Full article ">Figure 6
<p>Extracted impedance results for the T-circuit model: (<b>a</b>) Imaginary part of <math display="inline"><semantics> <msub> <mi>Z</mi> <mn>1</mn> </msub> </semantics></math>. (<b>b</b>) Imaginary part of <math display="inline"><semantics> <msub> <mi>Z</mi> <mn>2</mn> </msub> </semantics></math>. (<b>c</b>) Imaginary part of <math display="inline"><semantics> <msub> <mi>Z</mi> <mn>3</mn> </msub> </semantics></math>.</p> Full article ">Figure 7
<p>Dispersion diagrams obtained from a parametric simulation: (<b>a</b>) Sweep of <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>/</mo> <mi>a</mi> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>d</mi> <mo>/</mo> <mi>b</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>). (<b>b</b>) Sweep of <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>/</mo> <mi>b</mi> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>s</mi> <mo>/</mo> <mi>a</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>).</p> Full article ">Figure 8
<p>Analysis of operation bandwidth of a DRW for different combinations of <span class="html-italic">s</span> and <span class="html-italic">d</span>.</p> Full article ">Figure 9
<p>Ridge impedance analysis: (<b>a</b>) Maximum <math display="inline"><semantics> <msub> <mi>Z</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> </msub> </semantics></math> possible values of a DRW for different combinations of <span class="html-italic">s</span> and <span class="html-italic">d</span> between 25 and 32 GHz. (<b>b</b>) Minimum <math display="inline"><semantics> <msub> <mi>Z</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> </msub> </semantics></math> possible values of a DRW for different combinations of <span class="html-italic">s</span> and <span class="html-italic">d</span> between 25 and 32 GHz.</p> Full article ">Figure 10
<p>Transmission lines model of a two-ridge DRW.</p> Full article ">Figure 11
<p>Constant-reflection coefficient circle transformation by change on the reference impedance.</p> Full article ">Figure 12
<p>Synthesis of an arbitrary impedance in the Smith chart.</p> Full article ">Figure 13
<p>Manufactured impedance synthesis networks: (<b>a</b>) Near-center-located impedance. (<b>b</b>) Near-edge-located impedance. (<b>c</b>) Impedance synthesis network measurement setup.</p> Full article ">Figure 14
<p>Equivalent ideal model circuit.</p> Full article ">Figure 15
<p>Simulated and measured results of reflection coefficient: (<b>a</b>) Near-center-located impedance. (<b>b</b>) Near-edge-located impedance.</p> Full article ">
21 pages, 10373 KiB  
Article
A 75 kW Medium-Frequency Transformer Design Based in Inductive Power Transfer (IPT) for Medium-Voltage Solid-State Transformer Applications
by Juan Blanco-Ortiz, Eduardo García-Martínez, Ignacio González-Prieto and Mario J. Duran
Electronics 2025, 14(6), 1059; https://doi.org/10.3390/electronics14061059 - 7 Mar 2025
Viewed by 192
Abstract
Solid-State Transformers (SSTs) enable significant improvements in size and functionality compared to conventional power transformers. However, one of the key challenges in Solid-State Transformer design is achieving reliable insulation between the high-voltage and low-voltage sections. This proposal presents the design and optimization of [...] Read more.
Solid-State Transformers (SSTs) enable significant improvements in size and functionality compared to conventional power transformers. However, one of the key challenges in Solid-State Transformer design is achieving reliable insulation between the high-voltage and low-voltage sections. This proposal presents the design and optimization of a high-insulation Medium-Frequency Transformer (MFT) for 66 kV grids operating at 50 kHz and delivering up to 75 kW for SST applications using Inductive Power Transfer (IPT) technology. A fixed 50 mm gap between the primary and secondary windings is filled with dielectric oil to enhance insulation. The proposed IPT system employs a double-D coil design developed through iterative 2D and 3D finite element method simulations to optimize the magnetic circuit, thereby significantly reducing stray flux and losses. Notably, the double-D configuration reduces enclosure losses from 269.6 W, observed in a rectangular coil design, to 4.38 W, resulting in an overall system loss reduction of 42.4% while maintaining the electrical parameters required for zero-voltage switching operation. These advancements address the critical limitations in conventional Medium-Frequency Transformers by providing enhanced insulation and improved thermal management. The proposed IPT-based design offers a low-loss solution with easy thermal management for solid-state transformer applications in high-voltage grids. Full article
(This article belongs to the Special Issue Advanced Control Techniques for Power Electronics: Addressing Challenges in Renewable Energy System Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Results of the comparison between the diameter of the coil and the coupling coefficient. (<b>b</b>) Representation of the efficiency Equation (<a href="#FD1-electronics-14-01059" class="html-disp-formula">1</a>), with different values of <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Q</mi> <mo>)</mo> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Description of the algorithm followed to evaluate each IPT geometry between 330 mm and 550 mm of the ferrite plane length.</p> Full article ">Figure 3
<p>Electrical diagram of the resonant tank in a CLLLC configuration and the two full bridges with SiC MOSFETs.</p> Full article ">Figure 4
<p>Losses results and maximum voltage over resonant capacitors with the different IPT variations.</p> Full article ">Figure 5
<p>Volumetric loss distribution in the IPT enclosure with a rectangular coil design, totaling 269.9 W. The highest losses occur near the edges of the ferrite plane, where an increased stray flux intensifies energy losses in the enclosure.</p> Full article ">Figure 6
<p>Flux distribution in the double-D arrangement. The purple areas denote the concatenated flux linking the primary and secondary coils, labeled as <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mrow> <mi>m</mi> <mn>1</mn> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mrow> <mi>m</mi> <mn>2</mn> </mrow> </msub> </semantics></math>. The green areas correspond to the leakage flux confined within the generating coil, identified as <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mrow> <mi>l</mi> <mn>1</mn> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mrow> <mi>l</mi> <mn>2</mn> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mrow> <mi>l</mi> <mn>3</mn> </mrow> </msub> </semantics></math>. The purple arrow marks the direction of the concatenated flux.</p> Full article ">Figure 7
<p>Qualitative reluctance model. The reluctance model represents the different magnetic flux paths shown in <a href="#electronics-14-01059-f006" class="html-fig">Figure 6</a>. The colors of the reluctances correspond to the respective magnetic flux region, facilitating a clear correlation between the flux distribution and its associated reluctances.</p> Full article ">Figure 8
<p>Coupling and magnetizing inductance for several coil diameters.</p> Full article ">Figure 9
<p>Volumetric losses in the IPT enclosure for double-D coil construction. Total losses equals to 4.38 W. The distribution of volumetric losses is an order of magnitude lower compared to the rectangular coil type.</p> Full article ">Figure 10
<p>Magnetic flux density (<span class="html-italic">B</span>) in the primary coils of the double-D coil configuration. The flux density is higher in the central region of the ferrite compared to its surroundings. This increase occurs because the coils in the same plane have currents flowing in the same direction, causing their generated flux to combine and increase the overall flux density.</p> Full article ">Figure 11
<p>Section view of the IPT with double-D construction and all the different materials associated. The wooden coil former, aluminum plate below the ferrites, ferrite plane, coil structure, and wooden base legs inside the steel enclosure.</p> Full article ">Figure 12
<p>Section view of the IPT with rectangular construction and all the different materials associated. Same materials as double-D construction.</p> Full article ">Figure 13
<p>Section view of the IPT with temperature distribution for the double-D construction.</p> Full article ">Figure 14
<p>Section view of the IPT with temperature distribution for the rectangular construction.</p> Full article ">
15 pages, 3702 KiB  
Article
Multiple Differential Convolution and Local-Variation Attention UNet: Nucleus Semantic Segmentation Based on Multiple Differential Convolution and Local-Variation Attention
by Xiaoming Sun, Shilin Li, Yongji Chen, Junxia Chen, Hao Geng, Kun Sun, Yuemin Zhu, Bochao Su and Hu Zhang
Electronics 2025, 14(6), 1058; https://doi.org/10.3390/electronics14061058 - 7 Mar 2025
Viewed by 81
Abstract
Nucleus accurate segmentation is a crucial task in biomedical image analysis. While convolutional neural networks (CNNs) have achieved notable progress in this field, challenges remain due to the complexity and heterogeneity of cell images, especially in overlapping regions of nuclei. To address the [...] Read more.
Nucleus accurate segmentation is a crucial task in biomedical image analysis. While convolutional neural networks (CNNs) have achieved notable progress in this field, challenges remain due to the complexity and heterogeneity of cell images, especially in overlapping regions of nuclei. To address the limitations of current methods, we propose a mechanism of multiple differential convolution and local-variation attention in CNNs, leading to the so-called multiple differential convolution and local-variation attention U-Net (MDLA-UNet). The multiple differential convolution employs multiple differential operators to capture gradient and direction information, improving the network’s capability to detect edges. The local-variation attention utilizes Haar discrete wavelet transforms for level-1 decomposition to obtain approximate features, and then derives high-frequency features to enhance the global context and local detail variation of the feature maps. The results on the MoNuSeg, TNBC, and CryoNuSeg datasets demonstrated superior segmentation performance of the proposed method for cells having complex boundaries and details with respect to existing methods. The proposed MDLA-UNet presents the ability of capturing fine edges and details in feature maps and thus improves the segmentation of nuclei with blurred boundaries and overlapping regions. Full article
(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The network architecture of the MDLA-UNet.</p> Full article ">Figure 2
<p>The multiple differential convolution block.</p> Full article ">Figure 3
<p>HD operators for horizontal differential convolution blocks.</p> Full article ">Figure 4
<p>Illustration of Haar DWT.</p> Full article ">Figure 5
<p>The local-variation attention block.</p> Full article ">Figure 6
<p>The result of the comparison experiments with state-of-the-art models conducted on the MoNuSeg, TNBC, and CryoNuSeg datasets.</p> Full article ">Figure 7
<p>The findings from the ablation experiments conducted on the MoNuSeg, TNBC, and CryoNuSeg datasets.</p> Full article ">
22 pages, 4433 KiB  
Article
FCCA: Fast Center Consistency Attention for Facial Expression Recognition
by Rui Sun, Zhaoli Zhang and Hai Liu
Electronics 2025, 14(6), 1057; https://doi.org/10.3390/electronics14061057 - 7 Mar 2025
Viewed by 129
Abstract
Given the critical requirements for both speed and accuracy in facial expression recognition, this paper presents a novel deep-learning architecture named Fast Central Consistency Attention (FCCA). With FasterNet-s as its backbone network, FCCA is designed to recognize facial expressions. Firstly, we leverage partial [...] Read more.
Given the critical requirements for both speed and accuracy in facial expression recognition, this paper presents a novel deep-learning architecture named Fast Central Consistency Attention (FCCA). With FasterNet-s as its backbone network, FCCA is designed to recognize facial expressions. Firstly, we leverage partial convolution to extract features from specific channels, thereby reducing frequent memory access and substantially boosting training speed. Secondly, we enhance recognition accuracy by introducing an additional pointwise convolution on the partial features, focusing on the central facial position using weighted mechanisms. Lastly, we integrate flip consistency loss to tackle uncertainty challenges inherent in facial expression recognition (FER) tasks, further improving the overall model performance. Our approach yielded superior results: we achieved recognition accuracies of 91.30% on RAF-DB and 65.51% on AffectNet datasets, along with 56.61% UAR and 69.66% WAR on the DFEW dataset. The FCCA method has demonstrated state-of-the-art performance across multiple datasets, underscoring its robustness and capability for generalization. Full article
(This article belongs to the Section Artificial Intelligence)
►▼ Show Figures

Figure 1

Figure 1
<p>Visualization results of the feature maps in an intermediate layer of the pretrained ResNet-50, where the image in the (<b>top-left</b>) corner is the input image (selected from the AffectNet dataset). Qualitatively, we can observe a high degree of redundancy across different channels.</p> Full article ">Figure 2
<p>The proposed FCCA network has three main parts: backbone, dual-branch, and FACL. Fasternet-s as the backbone extracts features from input data. The dual-branch works as follows: in Branch 1, the original image yields output <math display="inline"><semantics> <msub> <mi>M</mi> <mi>i</mi> </msub> </semantics></math>, and in Branch 2, its flipped version gives <math display="inline"><semantics> <msubsup> <mi>M</mi> <mrow> <mi>i</mi> </mrow> <mo>′</mo> </msubsup> </semantics></math> for data augmentation and capturing diverse features. Finally, <math display="inline"><semantics> <msub> <mi>M</mi> <mi>i</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msubsup> <mi>M</mi> <mrow> <mi>i</mi> </mrow> <mo>′</mo> </msubsup> </semantics></math> are inputs to FACL. After softmax, they aid the jointly supervised network to learn better features and optimize performance.</p> Full article ">Figure 3
<p>Comparison of Conv variants. PConv (<b>a</b>) uses filters to extract essential &amp; preserve residual channel features, with the former carrying key input info and the latter capturing details. PWConv (<b>b</b>), an enhanced PConv, forms a T-shaped structure via pointwise convolution. It focuses more on central info than PConv (<b>a</b>), potentially performing better in central-data-reliant tasks.</p> Full article ">Figure 4
<p>Details of the training and test set partitions of the RAF-DB and AffectNet datasets. (<b>a</b>) and (<b>c</b>) represent the sample quantities and distributions in the training set and test set of the RAF-DB dataset, respectively, while (<b>b</b>) and (<b>d</b>) represent the sample quantities and distributions in the training set and test set of the AffectNet dataset, respectively.</p> Full article ">Figure 5
<p>Confusion matrices for comparing the recognition performance of CNN (ResNet-50), FCA, and FCCA on the RAF-DB and AffectNet datasets, where (<b>a</b>–<b>c</b>) denote confusion matrices of CNN (ResNet-50), FCA, and FCCA on the RAF-DB dataset, respectively, and (<b>d</b>–<b>f</b>) denote confusion matrices of CNN (ResNet-50), FCA, and FCCA on the AffectNet dataset, respectively.</p> Full article ">Figure 6
<p>Visualization of the spatial distribution of feature vectors using T-SNE is shown. The first row represents the results on the RAF-DB dataset and the second row represents the results on the AffectNet dataset. The first column (<b>a</b>,<b>d</b>) indicates the spatial distribution of feature vectors obtained by the CNN (ResNet-50) method; the second column (<b>b</b>,<b>e</b>) indicates the spatial distribution of feature vectors generated by the FCA method; and the third column (<b>c</b>,<b>f</b>) indicates the spatial distribution of feature vectors from the FCCA method.</p> Full article ">Figure 7
<p>The feature contribution regions for emotion classification on the AffectNet dataset are shown. Each row presents the sample’s top seven predicted labels, highlighting the positive and negative feature regions. The predicted probabilities decrease sequentially from (<b>left</b>) to (<b>right</b>).</p> Full article ">Figure 8
<p>The feature extraction capabilities of the CNN (ResNet-50) and FCA methods for samples in the AffectNet dataset are compared. A single column shows the feature extraction capabilities of different methods for the same sample, and a single row shows the feature extraction capabilities of the same method for samples of seven different emotion categories.</p> Full article ">Figure 9
<p>Visualization results of GRAD-CAM for different methods on the AffectNet dataset: CNN (ResNet-50), FCA, and FCCA methods. A single column shows the attention regions focused on by the corresponding method. A single row shows the attention regions focused on for seven different expressions under the same method.</p> Full article ">Figure 10
<p>Ablation studies on the influence of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> values on the performance of the FCCA method. The experimental results on the RAF-DB and AffectNet datasets are represented by blue and orange lines, respectively.</p> Full article ">Figure 11
<p>The training details of the FCCA method on the RAF-DB and AffectNet datasets, including the iteration curves that highlight when the optimal performance was achieved. (<b>a</b>) This shows the accuracy iteration curves for 60 epochs of training on the RAF-DB dataset (including the four methods in the ablation study). (<b>b</b>) This represents the accuracy iteration curves for 60 epochs of training on the AffectNet dataset (including the four methods in the ablation study). (<b>c</b>) This depicts the loss iteration curves for 60 epochs of training on the RAF-DB dataset. (<b>d</b>) This illustrates the loss iteration curves for 60 epochs of training on the AffectNet dataset.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-28
Previous Issue
Back to TopTop