Journal Description
Electronics
Electronics
is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2(Electrical and Electronic Engineering) CiteScore - Q2 (Electrical and Electronic Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Electronics include: Magnetism, Signals, Network and Software.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling
Electronics 2024, 13(9), 1783; https://doi.org/10.3390/electronics13091783 (registering DOI) - 05 May 2024
Abstract
Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots.
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Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. This paper presents a metaheuristic-based approach parallelized on multicore processors (CPU) and graphics processing units (GPU) to optimize the scheduling of EV charging in a single smart parking lot. The proposed method uses a particle swarm optimization algorithm that takes as input the arrival time, the departure time, and the power demand of the vehicles and produces an optimized charging schedule for all vehicles in the parking lot, which minimizes the overall charging cost while respecting the chargers’ capacity and the parking lot feeder capacity. The algorithm exploits task-level parallelism for the multicore CPU implementation and data-level parallelism for the GPU implementation. The proposed algorithm is tested in simulation on parking lots containing 20 to 500 EVs. The parallel implementation on CPUs provides a speedup of 7.1x, while the implementation on a GPU provides a speedup of up to 247.6x. The parallel implementation on a GPU is able to optimize the charging schedule for a 20-EV parking lot in 0.87 s and a 500-EV lot in just under 30 s. These runtimes allow for real-time computation when a vehicle arrives at the parking lot or when the electricity cost profile changes.
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(This article belongs to the Special Issue Vehicle Technologies for Sustainable Smart Cities and Societies)
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Sound Source Localization Method Based on Time Reversal Operator Decomposition in Reverberant Environments
by
Huiying Ma, Tao Shang, Gufeng Li and Zhaokun Li
Electronics 2024, 13(9), 1782; https://doi.org/10.3390/electronics13091782 (registering DOI) - 05 May 2024
Abstract
Predicting sound sources in reverberant environments is a challenging task because reverberation causes reflection and scattering of sound waves, making it difficult to accurately determine the position of the sound source. Due to the characteristics of overcoming multipath effects and adaptive focusing of
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Predicting sound sources in reverberant environments is a challenging task because reverberation causes reflection and scattering of sound waves, making it difficult to accurately determine the position of the sound source. Due to the characteristics of overcoming multipath effects and adaptive focusing of the time reversal technology, this paper focuses on the application of the time reversal operator decomposition method for sound source localization in reverberant environments and proposes the image-source time reversal multiple signals classification (ISTR-MUSIC) method. Firstly, the time reversal operator is derived, followed by the proposal of a subspace method to achieve sound source localization. Meanwhile, the use of the image-source method is proposed to calculate and construct the transfer matrix. To validate the effectiveness of the proposed method, simulations and real-data experiments were performed. In the simulation experiments, the performance of the proposed method under different array element numbers, signal-to-noise ratios, reverberation times, frequencies, and numbers of sound sources were studied and analyzed. A comparison was also made with the traditional time reversal method and the MUSIC algorithm. The experiment was conducted in a reverberation chamber. Simulation and experimental results show that the proposed method has good localization performance and robustness in reverberant environments.
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Open AccessArticle
An Improved Lightweight Deep Learning Model and Implementation for Track Fastener Defect Detection with Unmanned Aerial Vehicles
by
Qi Yu, Ao Liu, Xinxin Yang and Weimin Diao
Electronics 2024, 13(9), 1781; https://doi.org/10.3390/electronics13091781 (registering DOI) - 05 May 2024
Abstract
Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate,
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Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate, and more intelligent. With the widespread use of unmanned aerial vehicles (UAVs), conducting railway inspections using lightweight, low-power devices carried by UAVs has become a future trend. In this paper, we address the characteristics of track fastener detection tasks by improving the YOLOv4-tiny object detection model. We improved the model to output single-scale features and used the K-means++ algorithm to cluster the dataset, obtaining anchor boxes that were better suited to the dataset. Finally, we developed the FPGA platform and deployed the transformed model on this platform. The experimental results demonstrated that the improved model achieved an mAP of 95.1% and a speed of 295.9 FPS on the FPGA, surpassing the performance of existing object detection models. Moreover, the lightweight and low-powered FPGA platform meets the requirements for UAV deployment.
Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
Open AccessArticle
Highly Fault-Tolerant Systolic-Array-Based Matrix Multiplication
by
Hsin-Chen Lu, Liang-Ying Su and Shih-Hsu Huang
Electronics 2024, 13(9), 1780; https://doi.org/10.3390/electronics13091780 (registering DOI) - 05 May 2024
Abstract
Matrix multiplication plays a crucial role in various engineering and scientific applications. Cannon’s algorithm, executed within two-dimensional systolic arrays, significantly enhances computational efficiency through parallel processing. However, as the matrix size increases, reliability issues become more prominent. Although the previous work has proposed
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Matrix multiplication plays a crucial role in various engineering and scientific applications. Cannon’s algorithm, executed within two-dimensional systolic arrays, significantly enhances computational efficiency through parallel processing. However, as the matrix size increases, reliability issues become more prominent. Although the previous work has proposed a fault-tolerant mechanism, it is only suitable for scenarios with a limited number of faulty processing elements (PEs). This paper introduces a pair-matching mechanism, assigning a fault-free PE as a proxy for each faulty PE to execute its tasks. Our fault-tolerant mechanism comprises two stages: in the first stage, each fault-free PE completes its designated computations; in the second stage, computations intended for each faulty PE are executed by its assigned fault-free PE proxy. The experimental results demonstrate that compared to the previous work, our approach not only significantly improves the fault tolerance of systolic arrays (applicable to scenarios with a higher number of faulty PEs) but also reduces circuit areas. Therefore, the proposed approach proves effective in practical applications.
Full article
(This article belongs to the Special Issue System-on-Chip (SoC) and Field-Programmable Gate Array (FPGA) Design)
Open AccessArticle
Enhancement of Phase Dynamic Range in Design of Reconfigurable Metasurface Reflect Array Antenna Using Two Types of Unit Cells for E Band Communication
by
Daniel Rozban, Asaf Barom, Gil Kedar, Ariel Etinger, Tamir Rabinovitz and Amir Abramovich
Electronics 2024, 13(9), 1779; https://doi.org/10.3390/electronics13091779 (registering DOI) - 04 May 2024
Abstract
The deployment of wireless communication networks in the E band (60–90 GHz) requires highly flexible, real-time, and precise tunability to optimize power transmission amidst diffraction, obstacles, and scattering challenges. This paper proposes an innovative reconfigurable metasurface reflect array design capable of achieving a
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The deployment of wireless communication networks in the E band (60–90 GHz) requires highly flexible, real-time, and precise tunability to optimize power transmission amidst diffraction, obstacles, and scattering challenges. This paper proposes an innovative reconfigurable metasurface reflect array design capable of achieving a dynamic phase range of 312 degrees with less than 1 dB of loss. The design integrates two types of unit cells and employs piezoelectric crystal as the tuning element. Simulation results illustrate the feasibility of beam focusing and accurate beam steering within a range of ±3 degrees. Furthermore, the proposed reconfigurable metasurface reflector demonstrates an antenna gain comparable to that of a dish antenna with the same aperture size.
Full article
(This article belongs to the Special Issue Microwave Devices: Analysis, Design, and Application)
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An Improvement of Adam Based on a Cyclic Exponential Decay Learning Rate and Gradient Norm Constraints
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Yichuan Shao, Jiapeng Yang, Wen Zhou, Haijing Sun, Lei Xing, Qian Zhao and Le Zhang
Electronics 2024, 13(9), 1778; https://doi.org/10.3390/electronics13091778 (registering DOI) - 04 May 2024
Abstract
Aiming at a series of limitations of the Adam algorithm, such as hyperparameter sensitivity and unstable convergence, in this paper, an improved optimization algorithm, the Cycle-Norm-Adam (CN-Adam) algorithm, is proposed. The algorithm integrates the ideas of a cyclic exponential decay learning rate (CEDLR)
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Aiming at a series of limitations of the Adam algorithm, such as hyperparameter sensitivity and unstable convergence, in this paper, an improved optimization algorithm, the Cycle-Norm-Adam (CN-Adam) algorithm, is proposed. The algorithm integrates the ideas of a cyclic exponential decay learning rate (CEDLR) and gradient paradigm constraintsand accelerates the convergence speed of the Adam model and improves its generalization performance by dynamically adjusting the learning rate. In order to verify the effectiveness of the CN-Adam algorithm, we conducted extensive experimental studies. The CN-Adam algorithm achieved significant performance improvementsin both standard datasets. The experimental results show that the CN-Adam algorithm achieved 98.54% accuracy in the MNIST dataset and 72.10% in the CIFAR10 dataset. Due to the complexity and specificity of medical images, the algorithm was tested in a medical dataset and achieved an accuracy of 78.80%, which was better than the other algorithms. The experimental results show that the CN-Adam optimization algorithm provides an effective optimization strategy for improving model performance and promoting medical research.
Full article
(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)
Open AccessArticle
A Novel Multi-LiDAR-Based Point Cloud Stitching Method Based on a Constrained Particle Filter
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Gaofan Ji, Yunhan He, Chuanxiang Li, Li Fan, Haibo Wang and Yantong Zhu
Electronics 2024, 13(9), 1777; https://doi.org/10.3390/electronics13091777 (registering DOI) - 04 May 2024
Abstract
In coal-fired power plants, coal piles serve as the fundamental management units. Acquiring point clouds of coal piles facilitates the convenient measurement of daily coal consumption and combustion efficiency. When using servo motors to drive Light Detection and Ranging (LiDAR) scanning of large-scale
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In coal-fired power plants, coal piles serve as the fundamental management units. Acquiring point clouds of coal piles facilitates the convenient measurement of daily coal consumption and combustion efficiency. When using servo motors to drive Light Detection and Ranging (LiDAR) scanning of large-scale coal piles, the motors are subject to rotational errors due to gravitational effects. As a result, the acquired point clouds often contain significant noise. To address this issue, we proposes a Rapid Point Cloud Stitching–Constrained Particle Filter (RPCS-CPF) method. By introducing random noise to simulate servo motor rotational errors, both local and global point clouds are sequentially subjected to RPCS-CPF operations, resulting in smooth and continuous coal pile point clouds. Moreover, this paper presents a coal pile boundary detection method based on gradient region growing clustering. Experimental results demonstrate that our proposed RPCS-CPF method can generate smooth and continuous coal pile point clouds, even in the presence of servo motor rotational errors.
Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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Improving Scanning Performance of Patch Phased Array Antenna by Using a Modified SIW Cavity and Sequential Rotation Technique
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Hao Liu, Tianci Guan, Chunsen Fu, Shuqi Zhang, Xin Xu, Ziqiang Xu, Anyong Qing and Xianqi Lin
Electronics 2024, 13(9), 1776; https://doi.org/10.3390/electronics13091776 (registering DOI) - 04 May 2024
Abstract
A novel patch phased array antenna with improved scanning performance is presented in this paper. The active element pattern is changed as desired through a modified SIW cavity, resulting in an extension of the phased array’s 3 dB scanning range. Furthermore, sequential rotation
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A novel patch phased array antenna with improved scanning performance is presented in this paper. The active element pattern is changed as desired through a modified SIW cavity, resulting in an extension of the phased array’s 3 dB scanning range. Furthermore, sequential rotation is used to reduce the cross-polarization level of the array, which also improves the scanning gain at ±45°. Without altering the element size or profile, the array has the merits of low cost, low complexity, and a simple feed structure. The presented phased array antenna (PAA) exhibits a gain fluctuation of less than 2.2 dB when steering to 45°. Furthermore, the cross-polarization levels are below −68.1 dB when scanning to 45° in a E-/H-plane over the whole working band. To validate the proposed design, a prototype of a 24 × 16 active PAA is designed, fabricated, and measured. A good agreement between the simulated and measured results is achieved, Thus, this paper offers a viable solution to enhance the scanning performance of a PAA with fixed interelement spacing.
Full article
(This article belongs to the Special Issue Recent Advances in Antenna Arrays and Millimeter-Wave Components)
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Heat Dissipation Capability of Stagger-Stacked Double Data Rate Module
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Haiyan Sun, Dongqing Cang, Qi Zhang, Jicong Zhao and Zhikuang Cai
Electronics 2024, 13(9), 1775; https://doi.org/10.3390/electronics13091775 (registering DOI) - 04 May 2024
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In this study, we introduce a stagger-stacked DDR module that comprises one IPD chip (top die) along with four memory chips initially. The steady-state thermal characteristics of this configuration were empirically assessed using a dedicated thermal test vehicle. The purpose of this research
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In this study, we introduce a stagger-stacked DDR module that comprises one IPD chip (top die) along with four memory chips initially. The steady-state thermal characteristics of this configuration were empirically assessed using a dedicated thermal test vehicle. The purpose of this research is to investigate the module’s junction temperature by adjusting four factors: the thermal conductivity of the molding plastic, chip thickness, chip misalignment length, and the thermal conductivity of the adhesive film. We observed that the junction temperature decreases with an increase in the chip staggered length. An improved orthogonal experimental method was utilized to achieve the optimal design of the module. The optimal junction temperature has decreased by 4.74% compared to the initial value. Additionally, three alternative packaging technologies—cantilever, pyramid, and a combination of cantilever and pyramid—were evaluated for the benchmarking of the thermal performance. Ultimately, the stagger-stacked package demonstrated a reduction in the junction temperature by 3.62%, 7.95%, and 5.63%, respectively, when compared to the three traditional stacked packages.
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Open AccessArticle
A 6 Mbps 7 pJ/bit CMOS Integrated Wireless Simultaneous Lightwave Information and Power Transfer System for Biomedical Implants
by
Andrea De Marcellis, Guido Di Patrizio Stanchieri, Marco Faccio, Elia Palange and Timothy G. Constandinou
Electronics 2024, 13(9), 1774; https://doi.org/10.3390/electronics13091774 (registering DOI) - 04 May 2024
Abstract
This paper presents a Simultaneous Lightwave Information and Power Transfer (SLIPT) system for implantable biomedical applications composed of an external and internal (i.e., implantable) unit designed at a transistor level in TMSC 0.18 µm standard CMOS Si technology, requiring Si areas of 200
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This paper presents a Simultaneous Lightwave Information and Power Transfer (SLIPT) system for implantable biomedical applications composed of an external and internal (i.e., implantable) unit designed at a transistor level in TMSC 0.18 µm standard CMOS Si technology, requiring Si areas of 200 × 260 µm2 and 615 × 950 µm2, respectively. The SLIPT external unit employs a semiconductor laser to transmit data and power to the SLIPT internal unit, which contains an Optical Wireless Power Transfer (OWPT) module to supply its circuitry and, in particular, the data receiver module. To enable these operations, the transmitter module of the SLIPT external unit uses a novel reverse multilevel synchronized pulse position modulation technique based on dropping the laser driving current to zero so it produces laser pulses with a reversed intensity profile. This modulation technique allows: (i) the SLIPT external unit to code and transmit data packages of 6-bit symbols received and decoded by the SLIPT internal unit; and (ii) to supply the OWPT module also in the period between the transmission of two consecutive data packages. The receiver module operates for a time window of 12.5 µs every 500 µs, this being the time needed for the OWPT module to fully recover the energy to power the SLIPT internal unit. Post-layout simulations demonstrate that the proposed SLIPT system provides a final data throughput of 6 Mbps, an energy efficiency of 7 pJ/bit, and an OWPT module power transfer efficiency of 40%.
Full article
(This article belongs to the Special Issue Section Collection Series: Recent Advances in Optoelectronics from Lab to Industry)
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Disturbance Observer-Based Tracking Controller for n-Link Flexible-Joint Robots Subject to Time-Varying State Constraints
by
Zhongcai Zhang, Xueli Hu and Peng Huang
Electronics 2024, 13(9), 1773; https://doi.org/10.3390/electronics13091773 (registering DOI) - 04 May 2024
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This paper addresses the tracking control for an n-link flexible-joint robot system with full-state constraints and external disturbances. First, a nonlinear disturbance observer (NDO) is introduced to asymptotically estimate and suppress the influence of the related disturbances. Next, the constrained system under
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This paper addresses the tracking control for an n-link flexible-joint robot system with full-state constraints and external disturbances. First, a nonlinear disturbance observer (NDO) is introduced to asymptotically estimate and suppress the influence of the related disturbances. Next, the constrained system under consideration is transformed into a new unconstrained system using state-dependent function (SDF) transformations. Subsequently, a NDO-based tracking controller that combines the backstepping method and filter technique is proposed in this work. Based on stability analysis, it can be proven that the tracking error converges to a predefined compact set, which can be arbitrarily small without violating the full-state constraints. Finally, simulation results are presented to demonstrate the validity of the suggested control algorithm.
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Open AccessArticle
FedSKF: Selective Knowledge Fusion via Optimal Transport in Federated Class Incremental Learning
by
Minghui Zhou and Xiangfeng Wang
Electronics 2024, 13(9), 1772; https://doi.org/10.3390/electronics13091772 (registering DOI) - 04 May 2024
Abstract
Federated learning has been a hot topic in the field of artificial intelligence in recent years due to its distributed nature and emphasis on privacy protection. To better align with real-world scenarios, federated class incremental learning (FCIL) has emerged as a new research
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Federated learning has been a hot topic in the field of artificial intelligence in recent years due to its distributed nature and emphasis on privacy protection. To better align with real-world scenarios, federated class incremental learning (FCIL) has emerged as a new research trend, but it faces challenges such as heterogeneous data, catastrophic forgetting, and inter-client interference. However, most existing methods enhance model performance at the expense of privacy, such as uploading prototypes or samples, which violates the basic principle of only transmitting models in federated learning. This paper presents a novel selective knowledge fusion (FedSKF) model to address data heterogeneity and inter-client interference without sacrificing any privacy. Specifically, this paper introduces a PIT (projection in turn) module on the server side to indirectly recover client data distribution information through optimal transport. Subsequently, to reduce inter-client interference, knowledge of the global model is selectively absorbed via knowledge distillation and an incomplete synchronization classifier at the client side, namely an SKS (selective knowledge synchronization) module. Furthermore, to mitigate global catastrophic forgetting, a global forgetting loss is proposed to distill knowledge from the old global model. Our framework can easily integrate various CIL methods, allowing it to adapt to application scenarios with varying privacy requirements. We conducted extensive experiments on CIFAR100 and Tiny-ImageNet datasets, and the performance of our method surpasses existing works.
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(This article belongs to the Special Issue Recent Trends and Applications of Artificial Intelligence)
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Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data
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Ghulam Mustafa, Youngsup Hwang and Seong-Je Cho
Electronics 2024, 13(9), 1771; https://doi.org/10.3390/electronics13091771 - 03 May 2024
Abstract
Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge
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Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.
Full article
(This article belongs to the Special Issue The Future of IoT: Advanced AI Based IoT Technologies and Applications)
Open AccessArticle
Multispectral Pedestrian Detection Based on Prior-Saliency Attention and Image Fusion
by
Jiaren Guo, Zihao Huang and Yanyun Tao
Electronics 2024, 13(9), 1770; https://doi.org/10.3390/electronics13091770 - 03 May 2024
Abstract
Detecting pedestrians in varying illumination conditions poses a significant challenge, necessitating the development of innovative solutions. In response to this, we introduce Prior-AttentionNet, a pedestrian detection model featuring a Prior-Attention mechanism. This model leverages the stark contrast between thermal objects and their backgrounds
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Detecting pedestrians in varying illumination conditions poses a significant challenge, necessitating the development of innovative solutions. In response to this, we introduce Prior-AttentionNet, a pedestrian detection model featuring a Prior-Attention mechanism. This model leverages the stark contrast between thermal objects and their backgrounds in far-infrared (FIR) images by employing saliency attention derived from FIR images via UNet. However, extracting salient regions of diverse scales from FIR images poses a challenge for saliency attention. To address this, we integrate Simple Linear Iterative Clustering (SLIC) superpixel segmentation, embedding the segmentation feature map as prior knowledge into UNet’s decoding stage for comprehensive end-to-end training and detection. This integration enhances the extraction of focused attention regions, with the synergy of segmentation prior and saliency attention forming the core of Prior-AttentionNet. Moreover, to enrich pedestrian details and contour visibility in low-light conditions, we implement multispectral image fusion. Experimental evaluations were conducted on the KAIST and OTCBVS datasets. Applying Prior-Attention mode to FIR-RGB images significantly improves the delineation and focus on multi-scale pedestrians. Prior-AttentionNet’s general detector demonstrates the capability of detecting pedestrians with minimal computational resources. The ablation studies indicate that the FIR-RGB+ Prior-Attention mode markedly enhances detection robustness over other modes. When compared to conventional multispectral pedestrian detection models, Prior-AttentionNet consistently surpasses them by achieving higher mean average precision and lower miss rates in diverse scenarios, during both day and night.
Full article
(This article belongs to the Section Computer Science & Engineering)
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TXAI-ADV: Trustworthy XAI for Defending AI Models against Adversarial Attacks in Realistic CIoT
by
Stephn Ojo, Moez Krichen, Meznah A. Alamro and Alaeddine Mihoub
Electronics 2024, 13(9), 1769; https://doi.org/10.3390/electronics13091769 - 03 May 2024
Abstract
Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT
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Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT and countering emerging risks require constant updates and monitoring of these devices. Machine learning (ML), in combination with Explainable Artificial Intelligence (XAI), has become an essential component of the CIoT ecosystem due to its rapid advancement and impressive results across several application domains for attack detection, prevention, mitigation, and providing explanations of such decisions. These attacks exploit and steal sensitive data, disrupt the devices’ functionality, or gain unauthorized access to connected networks. This research generates a novel dataset by injecting adversarial attacks into the CICIoT2023 dataset. It presents an adversarial attack detection approach named TXAI-ADV that utilizes deep learning (Mutli-Layer Perceptron (MLP) and Deep Neural Network (DNN)) and machine learning classifiers (K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), ensemble voting, and Meta Classifier) to detect attacks and avert such situations rapidly in a CIoT. This study utilized Shapley Additive Explanations (SHAP) techniques, an XAI technique, to analyze the average impact of each class feature on the proposed models and select optimal features for the adversarial attacks dataset. The results revealed that, with a 96% accuracy rate, the proposed approach effectively detects adversarial attacks in a CIoT.
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(This article belongs to the Special Issue Recent Trends and Applications of Artificial Intelligence)
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Optimizing the Timeliness of Hybrid OFDMA-NOMA Sensor Networks with Stability Constraints
by
Wei Wang, Yunquan Dong and Chengsheng Pan
Electronics 2024, 13(9), 1768; https://doi.org/10.3390/electronics13091768 - 03 May 2024
Abstract
In this paper, we analyze the timeliness of a multi-user system in terms of the age of information (AoI) and the corresponding stability region in which the packet rates of users lead to finite queue lengths. Specifically, we consider a hybrid OFDMA-NOMA system
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In this paper, we analyze the timeliness of a multi-user system in terms of the age of information (AoI) and the corresponding stability region in which the packet rates of users lead to finite queue lengths. Specifically, we consider a hybrid OFDMA-NOMA system where the users are partitioned into several groups. While users in each group share the same resource block using non-orthogonal multiple access (NOMA), different groups access the fading channel using orthogonal frequency division multiple access (OFDMA). For this system, we consider three decoding schemes at the service terminals: interfering decoding, which treats signals from other users as interference; serial interference cancellation, which removes signals from other users once they have been decoded; and the enhanced SIC strategy, where the receiver attempts to decode for another user if decoding for a previous user fails. We present the average AoI for each of the three decoding schemes in closed form. Under the constraint of the stable region, we find the minimum AoI of each decoding scheme efficiently. The numerical results show that by optionally choosing the decoding scheme and transmission rate, the hybrid OFDMA-NOMA outperforms conventional OFDMA in terms of both system timeliness and stability.
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(This article belongs to the Special Issue Featured Advances in Real-Time Networks)
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Fast Coding Unit Partitioning Algorithm for Video Coding Standard Based on Block Segmentation and Block Connection Structure and CNN
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Nana Li, Zhenyi Wang and Qiuwen Zhang
Electronics 2024, 13(9), 1767; https://doi.org/10.3390/electronics13091767 - 02 May 2024
Abstract
The recently introduced Video Coding Standard, VVC, presents a novel Quadtree plus Nested Multi-Type Tree (QTMTT) block structure. This structure enables a more flexible block partition and demonstrates enhanced compression performance compared to its predecessor, HEVC. However, The introduction of the new structure
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The recently introduced Video Coding Standard, VVC, presents a novel Quadtree plus Nested Multi-Type Tree (QTMTT) block structure. This structure enables a more flexible block partition and demonstrates enhanced compression performance compared to its predecessor, HEVC. However, The introduction of the new structure has led to a more complex partition search process, resulting in a considerable increase in time complexity. The QTMTT structure yields diverse Coding Unit (CU) block sizes, posing challenges for CNN model inference. In this study, we propose a representation structure termed Block Segmentation and Block Connection (BSC), rooted in texture features. This ensures that partial CU blocks are uniformly represented in size. To address different-sized CUs, various levels of CNN models are designed for prediction. Moreover, we introduce a post-processing method and a multi-thresholding scheme to further mitigate errors introduced by CNNs. This allows for flexible and adjustable acceleration, achieving a trade-off between coding time complexity and performance. Experimental results indicate that, in comparison to VTM-10.0, our “Fast” scheme reduces the average complexity by 57.14% with a 1.86% increase in BDBR. Meanwhile, the “Moderate” scheme reduces average complexity by 50.14% with only a 1.39% increase in BDBR.
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(This article belongs to the Special Issue Recent Advances in Image/Video Compression and Coding)
Open AccessArticle
No Pain Device: Empowering Personal Safety with an Artificial Intelligence-Based Nonviolence Embedded System
by
Agostino Giorgio
Electronics 2024, 13(9), 1766; https://doi.org/10.3390/electronics13091766 - 02 May 2024
Abstract
This paper presents the development of a novel anti-violence device titled “no pAIn” (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal
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This paper presents the development of a novel anti-violence device titled “no pAIn” (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal safety benefits for individuals across diverse demographics. Operating autonomously, it necessitates no user interaction post-activation. The AI engine conducts real-time speech recognition and effectively discerns genuine instances of aggression from non-violent disputes or conversations. Facilitated by its Internet connectivity, in the event of detected aggression, the device promptly issues assistance requests with real-time precise geolocation tracking to predetermined recipients for immediate assistance. Its compact size enables discreet concealment within commonplace items like candy wrappers, purpose-built casings, or wearable accessories. The device is battery-operated. The prototype was developed using a microcontroller board (Arduino Nano RP2040 Connect), incorporating an omnidirectional microphone and Wi-Fi module, all at a remarkably low cost. Subsequent functionality testing, performed in debug mode using the Arduino IDE serial monitor, yielded successful results. The AI engine exhibited exceptional accuracy in word recognition, complemented by a robust logic implementation, rendering the device highly reliable in discerning genuine instances of aggression from non-violent scenarios.
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(This article belongs to the Special Issue High-Performance Embedded Systems)
Open AccessArticle
Enhancing the Safety of Autonomous Vehicles in Adverse Weather by Deep Learning-Based Object Detection
by
Biwei Zhang, Murat Simsek, Michel Kulhandjian and Burak Kantarci
Electronics 2024, 13(9), 1765; https://doi.org/10.3390/electronics13091765 - 02 May 2024
Abstract
Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle.
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Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle. The suggested approach improves the effectiveness of single-stage object detectors, aiming to advance the performance in perceiving autonomous racing environments and minimizing instances of false detection and low recognition rates. The improved framework is based on the one-stage object-detection model, incorporating multiple lightweight backbones. Additionally, attention mechanisms are integrated to refine the object-detection process further. Our proposed model demonstrates superior performance compared to the state-of-the-art method on the DAWN dataset, achieving a mean average precision (mAP) of 99.1%, surpassing the previous result of 84.7%.
Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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Open AccessArticle
Exploring the Odd–Even Effect, Current Stabilization, and Negative Differential Resistance in Carbon-Chain-Based Molecular Devices
by
Lijun Wang, Liping Zhou, Xuefeng Wang and Wenlong You
Electronics 2024, 13(9), 1764; https://doi.org/10.3390/electronics13091764 - 02 May 2024
Abstract
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Show Figures
The transport properties of molecular devices based on carbon chains are systematically investigated using a combination of non-equilibrium Green’s function (NEGF) and density functional theory (DFT) first-principle methods. In single-carbon-chain molecular devices, a distinct even–odd behavior of the current emerges, primarily influenced by
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The transport properties of molecular devices based on carbon chains are systematically investigated using a combination of non-equilibrium Green’s function (NEGF) and density functional theory (DFT) first-principle methods. In single-carbon-chain molecular devices, a distinct even–odd behavior of the current emerges, primarily influenced by the density of states (DOS) within the chain channel. Additionally, linear, monotonic currents exhibit Ohmic contact characteristics. In ladder-shaped carbon-chain molecular devices, a notable current stabilization behavior is observed, suggesting their potential utility as current stabilizers within circuits. We provide a comprehensive analysis of the transport properties of molecular devices featuring ladder-shaped carbon chains connecting benzene-ring molecules. The occurrence of negative differential resistance (NDR) in the low-bias voltage region is noted, with the possibility of manipulation by adjusting the position of the benzene-ring molecule. These findings offer a novel perspective on the potential applications of atom chains.
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