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
Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining
Electronics 2024, 13(6), 1117; https://doi.org/10.3390/electronics13061117 (registering DOI) - 19 Mar 2024
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The meticulously engineered powertrain mounting system of hybrid electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to
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The meticulously engineered powertrain mounting system of hybrid electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—to assess their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.
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Open AccessArticle
Crafting Creative Melodies: A User-Centric Approach for Symbolic Music Generation
by
Shayan Dadman and Bernt Arild Bremdal
Electronics 2024, 13(6), 1116; https://doi.org/10.3390/electronics13061116 (registering DOI) - 18 Mar 2024
Abstract
Composing coherent and structured music is one of the main challenges in symbolic music generation. Our research aims to propose a user-centric framework design that promotes a collaborative environment between users and knowledge agents. The primary objective is to improve the music creation
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Composing coherent and structured music is one of the main challenges in symbolic music generation. Our research aims to propose a user-centric framework design that promotes a collaborative environment between users and knowledge agents. The primary objective is to improve the music creation process by actively involving users who provide qualitative feedback and emotional assessments. The proposed framework design constructs an abstract format in which a musical piece is represented as a sequence of musical samples. It consists of multiple agents that embody the dynamics of musical creation, emphasizing user-driven creativity and control. This user-centric approach can benefit individuals with different musical backgrounds, encouraging creative exploration and autonomy in personalized, adaptive environments. To guide the design of this framework, we investigate several key research questions, including the optimal balance between system autonomy and user involvement, the extraction of rhythmic and melodic features through musical sampling, and the effectiveness of topological and hierarchical data representations. Our discussion will highlight the different aspects of the framework in relation to the research questions, expected outcomes, and its potential effectiveness in achieving objectives. Through establishing a theoretical foundation and addressing the research questions, this work has laid the groundwork for future empirical studies to validate the framework and its potential in symbolic music generation.
Full article
(This article belongs to the Special Issue Applications of Soft Computing)
Open AccessArticle
Input Voltage-Level Driven Split-Input Inverter Level Shifter for Nanoscale Applications
by
Srinivasulu Gundala, Mohammed Mahaboob Basha, Virupakshi Madhurima and Ovidiu Petru Stan
Electronics 2024, 13(6), 1115; https://doi.org/10.3390/electronics13061115 (registering DOI) - 18 Mar 2024
Abstract
A level shifter (LS) appears to be highly efficient and effective in solving voltage contentions between deep sub-threshold and core voltage levels. An input voltage-level driven split-input inverter that can create common unconnected PMOS and NMOS transistors for the input inverter is proposed,
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A level shifter (LS) appears to be highly efficient and effective in solving voltage contentions between deep sub-threshold and core voltage levels. An input voltage-level driven split-input inverter that can create common unconnected PMOS and NMOS transistors for the input inverter is proposed, which is powered and used at the input stage to achieve maximum conversion efficiency. Layout and simulation results across different corners have demonstrated that the proposed LS is highly useful for cutting-edge nanoscale applications. It can up-convert voltage from 0.2 V to 1.2 V and down-convert from 1.2 V to 0.2 V @ 1 MHz input pulse, with a level-up or level-down mean switching delay of 1.3 ns, and a power of 9.5 nW. Moreover, the LS occupies an area of 8 μm2, which is a reasonably compact size compared to the typical LS designs. Overall, the proposed voltage LS design is an efficient and effective solution that could have an ample range of applications in IoT and biomedical, wireless sensor networks.
Full article
(This article belongs to the Special Issue Design of Low-Voltage and Low-Power Integrated Circuits)
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Open AccessArticle
Classification Method of 3D Pattern Film Images Using MLP Based on the Optimal Widths of Histogram
by
Jaeeun Lee, Hongseok Choi and Jongnam Kim
Electronics 2024, 13(6), 1114; https://doi.org/10.3390/electronics13061114 - 18 Mar 2024
Abstract
3D pattern film is a film that makes a 2D pattern appear 3D depending on the amount and angle of light. However, since the 3D pattern film image was developed recently, there is no established method for classifying and verifying defective products, and
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3D pattern film is a film that makes a 2D pattern appear 3D depending on the amount and angle of light. However, since the 3D pattern film image was developed recently, there is no established method for classifying and verifying defective products, and there is little research in this area, making it a necessary field of study. Additionally, 3D pattern film has blurred contours, making it difficult to detect the outlines and challenging to classify. Recently, many machine learning methods have been published for analyzing product quality. However, when there is a small amount of data and most images are similar, using deep learning can easily lead to overfitting. To overcome these limitations, this study proposes a method that uses an MLP (Multilayer Perceptron) model to classify 3D pattern films into genuine and defective products. This approach entails inputting the widths derived from specific points’ heights in the image histogram of the 3D pattern film into the MLP, and then classifying the product as ‘good’ or ‘bad’ using optimal hyper-parameters found through the random search method. Although the contours of the 3D pattern film are blurred, this study can detect the characteristics of ‘good’ and ‘bad’ by using the image histogram. Moreover, the proposed method has the advantage of reducing the likelihood of overfitting and achieving high accuracy, as it reflects the characteristics of a limited number of similar images and builds a simple model. In the experiment, the accuracy of the proposed method was 98.809%, demonstrating superior performance compared to other models.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Image and Video Processing)
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Open AccessArticle
Improved Carrier-Based Modulation for the Single-Phase T-Type qZ Source Inverter
by
Vitor Fernão Pires, Armando Cordeiro, Daniel Foito, Carlos Roncero-Clemente, Enrique Romero-Cadaval and José Fernando Silva
Electronics 2024, 13(6), 1113; https://doi.org/10.3390/electronics13061113 - 18 Mar 2024
Abstract
The Quasi-Impedance-Source Inverter (Quasi-Z inverter) is an interesting DC-AC converter topology that can be used in applications such as fuel cells and photovoltaic generators. This topology allows for both boost capability and DC-side continuous input current. Another very interesting feature is its reliability,
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The Quasi-Impedance-Source Inverter (Quasi-Z inverter) is an interesting DC-AC converter topology that can be used in applications such as fuel cells and photovoltaic generators. This topology allows for both boost capability and DC-side continuous input current. Another very interesting feature is its reliability, as it limits the current when two switches on one leg are conducting simultaneously. This is due to an extra conduction state, specifically the shoot-through state. However, the shoot-through state also causes a loss of performance, increasing electromagnetic interference and harmonic distortion. To address these issues, this work proposes a modified carrier-based control method for the T-Type single-phase quasi-Z inverter. The modified carrier-based method introduces the use of two additional states to replace the standard shoot-through state. The additional states are called the upper shoot-through and the lower shoot-through. An approach to minimize the number of switches that change state during transitions will also be considered to reduce switching losses, improving the converter efficiency. The proposed modified carrier-based control strategy will be tested using computer simulations and laboratory experiments. From the obtained results, the theoretical considerations are confirmed. In fact, through the presented results, it is possible to understand important improvements that can be obtained in the THD of the output voltage and load current. In addition, it is also possible to verify that the modified carrier method also reduces the input current ripple.
Full article
(This article belongs to the Special Issue State-of-the-Art and New Trends of Power Electronics Technologies and Applications, Volume 2)
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Open AccessArticle
APIMiner: Identifying Web Application APIs Based on Web Page States Similarity Analysis
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Yuanchao Chen, Yuliang Lu, Zulie Pan, Juxing Chen, Fan Shi, Yang Li and Yonghui Jiang
Electronics 2024, 13(6), 1112; https://doi.org/10.3390/electronics13061112 - 18 Mar 2024
Abstract
Modern web applications offer various APIs for data interaction. However, as the number of these APIs increases, so does the potential for security threats. Essentially, more APIs in an application can lead to more detectable vulnerabilities. Thus, it is crucial to identify APIs
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Modern web applications offer various APIs for data interaction. However, as the number of these APIs increases, so does the potential for security threats. Essentially, more APIs in an application can lead to more detectable vulnerabilities. Thus, it is crucial to identify APIs as comprehensively as possible in web applications. However, this task faces challenges due to the increasing complexity of web development techniques and the abundance of similar web pages. In this paper, we propose APIMiner, a framework for identifying APIs in web applications by dynamically traversing web pages based on web page state similarity analysis. APIMiner first builds a web page model based on the HTML elements of the current web page. APIMiner then uses this model to represent the state of the page. Then, APIMiner evaluates each element’s similarity in the page model and determines the page state similarity based on these similarity values. From the different states of the page, APIMiner extracts the data interaction APIs on the page. We conduct extensive experiments to evaluate APIMiner’s effectiveness. In the similarity analysis, our method surpasses state-of-the-art methods like NDD and mNDD in accurately distinguishing similar pages. We compare APIMiner with state-of-the-art tools (e.g., Enemy of the State, Crawlergo, and Wapiti3) for API identification. APIMiner excels in the number of identified APIs (average 1136) and code coverage (average 28,470). Relative to these tools, on average, APIMiner identifies 7.96 times more APIs and increases code coverage by 142.72%.
Full article
(This article belongs to the Special Issue Advances in Data Science: Methods, Systems, and Applications)
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Open AccessArticle
A Novel Low-Cost Capacitance Sensor Solution for Real-Time Bubble Monitoring in Medical Infusion Devices
by
Chiang Liang Kok, Yuwei Dai, Teck Kheng Lee, Yit Yan Koh, Tee Hui Teo and Jian Ping Chai
Electronics 2024, 13(6), 1111; https://doi.org/10.3390/electronics13061111 - 18 Mar 2024
Abstract
In the present day, IoT technology is widely applied in the field of medical devices to facilitate real-time monitoring and management by medical staff, thereby better-ensuring patient safety. In IoT intravenous infusion monitoring sensors, it is particularly important to ensure that air bubbles
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In the present day, IoT technology is widely applied in the field of medical devices to facilitate real-time monitoring and management by medical staff, thereby better-ensuring patient safety. In IoT intravenous infusion monitoring sensors, it is particularly important to ensure that air bubbles are not infused into the patient’s body. The most common method for bubble detection during intravenous infusions is the use of infrared or laser sensors, which can usually meet design requirements at a relatively low cost. Another method is the use of ultrasonic detection of bubbles, which achieves high accuracy but has not been widely promoted in the market due to higher costs. This proposed work introduces a new type of sensor that detects bubbles by monitoring changes in capacitance between two electrodes installed at the surface of the infusion pipe. If this sensor is deployed on the ESP32 platform, which is widely used in embedded IoT devices, it can achieve 35 μL bubble detection precision with an average power consumption of 5.18 mW and a mass production cost of $0.022. Although the precision of this sensor is significantly lower than the low-cost IR bubble sensor, it still satisfies the design requirement of the IV infusion IoT sensor.
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(This article belongs to the Section Circuit and Signal Processing)
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Noise-Canceling Channel Estimation Schemes Based on the CIR Length Estimation for IEEE 802.11p/OFDM Systems
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Kyunbyoung Ko and Hanho Wang
Electronics 2024, 13(6), 1110; https://doi.org/10.3390/electronics13061110 - 18 Mar 2024
Abstract
This paper investigates methods for noise-canceling channel estimation (NC-CE) to track rapid time-varying channels in IEEE 802.11p/orthogonal frequency division multiplexing (OFDM) systems. To this end, we introduce a novel three-step channel estimation technique based on the estimated length of the channel impulse response
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This paper investigates methods for noise-canceling channel estimation (NC-CE) to track rapid time-varying channels in IEEE 802.11p/orthogonal frequency division multiplexing (OFDM) systems. To this end, we introduce a novel three-step channel estimation technique based on the estimated length of the channel impulse response (CIR). This approach aims to surpass the performance of conventional designs that rely on constructed data pilots (CDPs). In the first step, we not only eliminate noise components but also estimate the channel frequency responses (CFRs) of virtual subcarriers for long preamble parts. Moving on to the second step, we incorporate a modified CDP method without a frequency-domain reliability test and interpolation, taking into account the CFRs of virtual subcarriers obtained at the previous OFDM symbol time. The final step can be implemented as the operation of the inverse fast Fourier transform (IFFT)/nulling/FFT to reduce noise components from the CFRs obtained in the second step and generate CFRs for virtual subcarriers to be used in the next symbol time. The results of our simulations validate the effectiveness of our proposed channel estimation schemes.
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(This article belongs to the Special Issue Advances in Wireless and Optical Communication Systems)
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Open AccessArticle
An ECC-Based Authentication Protocol for Dynamic Charging System of Electric Vehicles
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Jie Wang, Shengbao Wang, Kang Wen, Bosen Weng, Xin Zhou and Kefei Chen
Electronics 2024, 13(6), 1109; https://doi.org/10.3390/electronics13061109 (registering DOI) - 18 Mar 2024
Abstract
Dynamic wireless charging emerges as a promising technology, effectively alleviating range anxiety for electric vehicles in transit. However, the communication between the system’s various components, conducted over public channels, raises concerns about vulnerability to network attacks and message manipulation. Addressing data security and
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Dynamic wireless charging emerges as a promising technology, effectively alleviating range anxiety for electric vehicles in transit. However, the communication between the system’s various components, conducted over public channels, raises concerns about vulnerability to network attacks and message manipulation. Addressing data security and privacy protection in dynamic charging systems thus becomes a critical challenge. In this article, we present an authentication protocol tailored for dynamic charging systems. This protocol ensures secure and efficient authentication between vehicles and roadside devices without the help of a trusted center. We utilize a physical unclonable function (PUF) to resist physical capture attacks and employ the elliptic curve discrete logarithm problem (ECDLP) to provide forward security protection for session keys. We validated the security of our proposed scheme through comprehensive informal analyses, and formal security analysis using the ROR model and formal analysis tool ProVerif. Furthermore, comparative assessments reveal that our scheme outperforms other relevant protocols in terms of efficiency and security.
Full article
(This article belongs to the Special Issue Recent Advances and Applications of Network Security and Cryptography)
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Machine-Learning-Based Traffic Classification in Software-Defined Networks
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Rehab H. Serag, Mohamed S. Abdalzaher, Hussein Abd El Atty Elsayed, M. Sobh, Moez Krichen and Mahmoud M. Salim
Electronics 2024, 13(6), 1108; https://doi.org/10.3390/electronics13061108 - 18 Mar 2024
Abstract
Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data
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Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.
Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)
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Open AccessArticle
Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation
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Zhaopeng Deng, Shuangyang Han, Zeqi Liu, Jian Wang and Haoran Zhao
Electronics 2024, 13(6), 1107; https://doi.org/10.3390/electronics13061107 - 18 Mar 2024
Abstract
The use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence
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The use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence of interference noise regions in the images and consequently impacting the classification accuracy. To enhance the analytical efficacy of in-hole images, this paper employs the proposed optimal non-concentric ring segmentation method to establish a new database. This method establishes the transformation function based on the Ansel Adams Zone System and the fluctuation values of the grayscale mean, adjusting the gray-level distribution of images to extract two visual blind spots of different scales. Thus, the inner and outer circles are located with these blind spots to achieve the adaptive acquisition of the optimal ring. Finally, we use the optimal non-concentric ring segmentation method to traverse all original images to obtain the borehole image classification database. To validate the effectiveness of this method, we conduct experiments using various segmentation and classification evaluation metrics. The results show that the Jaccard and Dice of the optimal non-concentric ring segmentation approach are 88.43% and 98.55%, respectively, indicating superior segmentation performance compared to other methods. Furthermore, after employing four commonly used classification models to validate the performance of the new classification database, the results demonstrate a significant improvement in accuracy and macro-average compared to the original database, with the highest increase in accuracy reaching 4.2%. These results fully demonstrate the effectiveness of the proposed optimal non-concentric ring segmentation method.
Full article
(This article belongs to the Topic Future Internet Architecture: Difficulties and Opportunities)
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Open AccessArticle
Advanced Persistent Threat Group Correlation Analysis via Attack Behavior Patterns and Rough Sets
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Jingwen Li, Jianyi Liu and Ru Zhang
Electronics 2024, 13(6), 1106; https://doi.org/10.3390/electronics13061106 - 18 Mar 2024
Abstract
In recent years, advanced persistent threat (APT) attacks have become a significant network security threat due to their concealment and persistence. Correlation analysis of APT groups is vital for understanding the global network security landscape and accurately attributing threats. Current studies on threat
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In recent years, advanced persistent threat (APT) attacks have become a significant network security threat due to their concealment and persistence. Correlation analysis of APT groups is vital for understanding the global network security landscape and accurately attributing threats. Current studies on threat attribution rely on experts or advanced technology to identify evidence linking attack incidents to known APT groups. However, there is a lack of research focused on automatically discovering potential correlations between APT groups. This paper proposes a method using attack behavior patterns and rough set theory to quantify APT group relevance. It extracts two types of features from threat intelligence: APT attack objects and behavior features. To address the issues of inconsistency and limitations in threat intelligence, this method uses rough set theory to model APT group behavior and designs a link prediction method to infer correlations among APT groups. Experimental results on publicly available APT analysis reports show a correlation precision of 90.90%. The similarity coefficient accurately reflects the correlation strength, validating the method’s efficacy and accuracy.
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(This article belongs to the Section Computer Science & Engineering)
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Open AccessArticle
Efficient Cross-Project Software Defect Prediction Based on Federated Meta-Learning
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Haisong Chen, Linlin Yang and Aili Wang
Electronics 2024, 13(6), 1105; https://doi.org/10.3390/electronics13061105 - 18 Mar 2024
Abstract
Software defect prediction is an important part of software development, which aims to use existing historical data to predict future software defects. Focusing on the model performance and communication efficiency of cross-project software defect prediction, this paper proposes an efficient communication-based federated meta-learning
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Software defect prediction is an important part of software development, which aims to use existing historical data to predict future software defects. Focusing on the model performance and communication efficiency of cross-project software defect prediction, this paper proposes an efficient communication-based federated meta-learning (ECFML) algorithm. The lightweight MobileViT network is used as the meta-learner of the Model Agnostic Meta-Learning (MAML) algorithm. By learning common knowledge on the local data of multiple clients, and then fine-tuning the model, the number of unnecessary iterations is reduced, and communication efficiency is improved while reducing the number of parameters. The gradient information model is encrypted using the differential privacy of the Laplace mechanism, and the optimal privacy budget is determined through experiments. Experiments on three public datasets (AEEEM, NASA, and Relink) verified the effectiveness of ECFML in terms of parameter quantity, convergence, and model performance of cross-project software defect prediction.
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(This article belongs to the Special Issue Machine Learning Methods in Software Engineering)
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AoI-Aware Resource Scheduling for Industrial IoT with Deep Reinforcement Learning
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Hongzhi Li, Lin Tang, Shengwei Chen, Libin Zheng and Shaohong Zhong
Electronics 2024, 13(6), 1104; https://doi.org/10.3390/electronics13061104 - 18 Mar 2024
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Effective resource scheduling methods in certain scenarios of Industrial Internet of Things are pivotal. In time-sensitive scenarios, Age of Information is a critical indicator for measuring the freshness of data. This paper considers a densely deployed time-sensitive Industrial Internet of Things scenario. The
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Effective resource scheduling methods in certain scenarios of Industrial Internet of Things are pivotal. In time-sensitive scenarios, Age of Information is a critical indicator for measuring the freshness of data. This paper considers a densely deployed time-sensitive Industrial Internet of Things scenario. The industrial wireless device transmits data packets to the base station with limited channel resources under the constraints of Age of Information. It is assumed that each device has the capacity to store the packets it generates. The device will discard the data to alleviate the data queue backlog when the Age of Information of the data packet exceeds the threshold. We developed a new system utility equation to represent the scheduling problem and the problem is expressed as a trade-off between minimizing the average Age of Information and maximizing network throughput. Inspired by the success of reinforcement learning in decision-processing problems, we attempt to obtain an optimal scheduling strategy via deep reinforcement learning. In addition, a reward function is constructed to enable the agent to achieve improved convergence results. Compared with the baseline, our proposed algorithm can achieve better system utility and lower Age of Information violation rate.
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Open AccessArticle
Combining wav2vec 2.0 Fine-Tuning and ConLearnNet for Speech Emotion Recognition
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Chenjing Sun, Yi Zhou, Xin Huang, Jichen Yang and Xianhua Hou
Electronics 2024, 13(6), 1103; https://doi.org/10.3390/electronics13061103 - 17 Mar 2024
Abstract
Speech emotion recognition poses challenges due to the varied expression of emotions through intonation and speech rate. In order to reduce the loss of emotional information during the recognition process and to enhance the extraction and classification of speech emotions and thus improve
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Speech emotion recognition poses challenges due to the varied expression of emotions through intonation and speech rate. In order to reduce the loss of emotional information during the recognition process and to enhance the extraction and classification of speech emotions and thus improve the ability of speech emotion recognition, we propose a novel approach in two folds. Firstly, a feed-forward network with skip connections (SCFFN) is introduced to fine-tune wav2vec 2.0 and extract emotion embeddings. Subsequently, ConLearnNet is employed for emotion classification. ConLearnNet comprises three steps: feature learning, contrastive learning, and classification. Feature learning transforms the input, while contrastive learning encourages similar representations for samples from the same category and discriminative representations for different categories. Experimental results on the IEMOCAP and the EMO-DB datasets demonstrate the superiority of our proposed method compared to state-of-the-art systems. We achieve a WA and UAR of 72.86% and 72.85% on IEMOCAP, and 97.20% and 96.41% on the EMO-DB, respectively.
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(This article belongs to the Special Issue New Advances in Affective Computing)
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A Space-Borne SAR Azimuth Multi-Channel Quantization Method
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Wei Xu, Lu Bai, Pingping Huang, Weixian Tan and Yifan Dong
Electronics 2024, 13(6), 1102; https://doi.org/10.3390/electronics13061102 - 17 Mar 2024
Abstract
The space-borne synthetic aperture radar (SAR) azimuth multi-channel system has extensive applications because it can achieve high-resolution and wide-swath radar imaging. The thermal noise generated by the radar receiver of each channel during operation will cause an imbalance between channels. If the echoes
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The space-borne synthetic aperture radar (SAR) azimuth multi-channel system has extensive applications because it can achieve high-resolution and wide-swath radar imaging. The thermal noise generated by the radar receiver of each channel during operation will cause an imbalance between channels. If the echoes of each channel are quantized with the same number of bits without considering the influence of thermal noise, false targets will appear in the imaging consequences. Considering that the thermal noise generated in the receiver will affect the quantization process of the space-borne SAR azimuth multi-channel system, a new space-borne SAR azimuth multi-channel quantization method is proposed to improve this problem. Firstly, the pure noise power of the receiver is calculated without transmitting the radar signal. The signal power is estimated by subtracting the pure noise power from the total power. Then, the average value of the radar echo signal minus k times the standard deviation is used as the left endpoint of the original data amplitude range, and the average value of the radar echo signal plus k times the standard deviation is used as the right endpoint of the original data amplitude range. The original echo data after adjusting the amplitude range is quantified. This method can effectively reduce the influence of thermal noise and random outliers in the receiver on quantization and suppress the appearance of false targets. Finally, simulation is used to confirm the viability of the suggested quantization approach.
Full article
(This article belongs to the Special Issue SAR Image and Signal Processing)
Open AccessArticle
Implementation of EnDat Interface Master Using Configurable Logic Block in MCU
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Kyungah Kim, Duc M. Tran and Joon-Young Choi
Electronics 2024, 13(6), 1101; https://doi.org/10.3390/electronics13061101 - 17 Mar 2024
Abstract
In this study, we propose an implementation method of the Encoder Data (EnDat) interface master for slave encoders using only a configurable logic block (CLB) and a serial peripheral interface (SPI) integrated into microcontroller units. By programming the CLB device to execute logic
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In this study, we propose an implementation method of the Encoder Data (EnDat) interface master for slave encoders using only a configurable logic block (CLB) and a serial peripheral interface (SPI) integrated into microcontroller units. By programming the CLB device to execute logic functions and finite state machines designed for the EnDat interface master operation, we realize the EnDat and SPI clocks that are required for the EnDat interface master operation. This approach is cost-efficient because additional hardware components, such as a field-programmable gate array or a complex programmable logic device, are unnecessary for the master implementation. We build a one-axis feed drive system that is powered by an AC motor and equipped with an EnDat linear encoder for measuring table speed and position. By performing various experiments for table position and speed control based on the built feed drive system, we verify the performance and practical usefulness of the implemented EnDat interface master. The maximum EnDat clock frequency without the propagation delay compensation is achieved by 2 MHz, which can cope with 16 kHz control cycle frequency. The usefulness is demonstrated by showing the table speed and position control performance that are acceptable in real applications.
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(This article belongs to the Special Issue Design and Development of Digital Embedded Systems)
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Approaches to Extend FPGA Reverse-Engineering Technology from ISE to Vivado
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Soyeon Choi and Hoyoung Yoo
Electronics 2024, 13(6), 1100; https://doi.org/10.3390/electronics13061100 - 16 Mar 2024
Abstract
SRAM-based FPGA(Field Programmable Logic Arrays) requires external memory since its internal memory gets erased when power is cut off. The process of transmitting the circuit netlist in bitstream from external memory during power-up in FPGA is vulnerable to malicious attacks such as bitstream
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SRAM-based FPGA(Field Programmable Logic Arrays) requires external memory since its internal memory gets erased when power is cut off. The process of transmitting the circuit netlist in bitstream from external memory during power-up in FPGA is vulnerable to malicious attacks such as bitstream theft and tampering. Previous FPGA reverse-engineering methods focus on FPGAs, supported by ISE (ISE Design Suite). This is because ISE provides XDLRC (Xilinx Design Language Routing Configurable logic) and XDL (Xilinx Design language) files, which are essential for reverse engineering. However, Vivado Design Suite (Vivado) does not offer those files, making it impossible to extend the coverage of reverse engineering to the FPGAs supported by Vivado. In this paper, we propose a method to generate XDLRC and XDL through Vivado. According to experimental results, the XDLRC and XDL generated through Vivado, respectively, match 99% and 75% with those generated in ISE for Artix-7 100T. As a result, this paper has expanded the scope of reverse engineering from being mainly focused on ISE to now also include Vivado. It is important to note that this paper does not encourage bitstream attacks through reverse engineering but rather highlights the risk associated with malicious attacks and emphasizes the importance of security.
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(This article belongs to the Section Semiconductor Devices)
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Open AccessArticle
Advancing Temporal Action Localization with a Boundary Awareness Network
by
Jialiang Gu, Yang Yi and Min Wang
Electronics 2024, 13(6), 1099; https://doi.org/10.3390/electronics13061099 - 16 Mar 2024
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Temporal action localization (TAL) is crucial in video analysis, yet presents notable challenges. This process focuses on the precise identification and categorization of action instances within lengthy, raw videos. A key difficulty in TAL lies in determining the exact start and end points
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Temporal action localization (TAL) is crucial in video analysis, yet presents notable challenges. This process focuses on the precise identification and categorization of action instances within lengthy, raw videos. A key difficulty in TAL lies in determining the exact start and end points of actions, owing to the often unclear boundaries of these actions in real-world footage. Existing methods tend to take insufficient account of changes in action boundary features. To tackle these issues, we propose a boundary awareness network (BAN) for TAL. Specifically, the BAN mainly consists of a feature encoding network, coarse pyramidal detection to obtain preliminary proposals and action categories, and fine-grained detection with a Gaussian boundary module (GBM) to get more valuable boundary information. The GBM contains a novel Gaussian boundary pooling, which serves to aggregate the relevant features of the action boundaries and to capture discriminative boundary and actionness features. Furthermore, we introduce a novel approach named Boundary Differentiated Learning (BDL) to ensure our model’s capability in accurately identifying action boundaries across diverse proposals. Comprehensive experiments on both the THUMOS14 and ActivityNet v1.3 datasets, where our BAN model achieved an increase in mean Average Precision (mAP) by 1.6% and 0.2%, respectively, over existing state-of-the-art methods, illustrate that our approach not only improves upon the current state of the art but also achieves outstanding performance.
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Open AccessArticle
Decentralized Exchange Transaction Analysis and Maximal Extractable Value Attack Identification: Focusing on Uniswap USDC3
by
Nakhoon Choi and Heeyoul Kim
Electronics 2024, 13(6), 1098; https://doi.org/10.3390/electronics13061098 - 16 Mar 2024
Abstract
With the advancement of blockchain technology and growing concerns about the vulnerabilities and mistrust in centralized financial services, decentralized finance (DeFi) and decentralized exchanges (DEXs) have emerged as promising alternatives. This paper delves into the challenges and issues within DeFi, with a particular
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With the advancement of blockchain technology and growing concerns about the vulnerabilities and mistrust in centralized financial services, decentralized finance (DeFi) and decentralized exchanges (DEXs) have emerged as promising alternatives. This paper delves into the challenges and issues within DeFi, with a particular focus on Uniswap. We highlight the susceptibility to Maximal Extractable Value (MEV) attacks, providing a background on the current state of DeFi and DEXs. Our approach includes a detailed transaction analysis on Uniswap to identify and analyze MEV attack patterns, alongside a method for detecting bots. The results offer critical insights into the nature of various attacks in DEXs and the correlation between internal and external blockchain events and MEV attack patterns. This research provides valuable guidelines for enhancing DEX security and mitigating MEV risks, serving as an essential resource for stakeholders in the DeFi ecosystem.
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(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications)
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