Electronics doi: 10.3390/electronics13061116
Authors: Shayan Dadman Bernt Arild Bremdal
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.
]]>Electronics doi: 10.3390/electronics13061115
Authors: Gundala Basha Madhurima Stan
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.
]]>Electronics doi: 10.3390/electronics13061114
Authors: Jaeeun Lee Hongseok Choi Jongnam Kim
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.
]]>Electronics doi: 10.3390/electronics13061113
Authors: Vitor Fernão Pires Armando Cordeiro Daniel Foito Carlos Roncero-Clemente Enrique Romero-Cadaval José Fernando Silva
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.
]]>Electronics doi: 10.3390/electronics13061112
Authors: Yuanchao Chen Yuliang Lu Zulie Pan Juxing Chen Fan Shi Yang Li Yonghui Jiang
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%.
]]>Electronics doi: 10.3390/electronics13061111
Authors: Chiang Liang Kok Yuwei Dai Teck Kheng Lee Yit Yan Koh Tee Hui Teo Jian Ping Chai
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.
]]>Electronics doi: 10.3390/electronics13061110
Authors: Kyunbyoung Ko Hanho Wang
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.
]]>Electronics doi: 10.3390/electronics13061109
Authors: Jie Wang Shengbao Wang Kang Wen Bosen Weng Xin Zhou Kefei Chen
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.
]]>Electronics doi: 10.3390/electronics13061108
Authors: Rehab H. Serag Mohamed S. Abdalzaher Hussein Abd El Atty Elsayed M. Sobh Moez Krichen Mahmoud M. Salim
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.
]]>Electronics doi: 10.3390/electronics13061107
Authors: Zhaopeng Deng Shuangyang Han Zeqi Liu Jian Wang Haoran Zhao
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.
]]>Electronics doi: 10.3390/electronics13061106
Authors: Jingwen Li Jianyi Liu Ru Zhang
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.
]]>Electronics doi: 10.3390/electronics13061105
Authors: Haisong Chen Linlin Yang Aili Wang
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.
]]>Electronics doi: 10.3390/electronics13061104
Authors: Hongzhi Li Lin Tang Shengwei Chen Libin Zheng Shaohong Zhong
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.
]]>Electronics doi: 10.3390/electronics13061103
Authors: Chenjing Sun Yi Zhou Xin Huang Jichen Yang Xianhua Hou
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.
]]>Electronics doi: 10.3390/electronics13061102
Authors: Wei Xu Lu Bai Pingping Huang Weixian Tan Yifan Dong
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.
]]>Electronics doi: 10.3390/electronics13061101
Authors: Kyungah Kim Duc M. Tran Joon-Young Choi
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.
]]>Electronics doi: 10.3390/electronics13061100
Authors: Soyeon Choi Hoyoung Yoo
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.
]]>Electronics doi: 10.3390/electronics13061099
Authors: Jialiang Gu Yang Yi Min Wang
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.
]]>Electronics doi: 10.3390/electronics13061098
Authors: Nakhoon Choi Heeyoul Kim
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.
]]>Electronics doi: 10.3390/electronics13061097
Authors: Liyuan Zheng Weiming Liu
To comprehensively investigate the key features of lane-changing (LC) risk for different vehicle types during left and right LC, and to improve the accuracy of LC risk recognition, this paper proposes a key feature selection and risk recognition model based on vehicle trajectory data. Based on a HighD high-precision vehicle trajectory dataset, the trajectory data of LC vehicles and surrounding vehicles of each vehicle type are extracted. SDI (stop distance index) and CI (crash index) are selected as surrogate indicators to calculate the risk exposure level (REL) and risk severity level (RSL). The K-means algorithm is used to cluster the REL and RSL to obtain the LC risk level, which is divided into three levels. The combination of basic features and interaction features of LC vehicles and surrounding vehicles with LC risk levels is constructed as the LC risk feature dataset. Based on the LightGBM (light gradient boosting machine) algorithm, the importance of features is sorted. Finally, a CNN-BiLSTM-Attention model is established to recognize the LC risk of each vehicle type during left and right LC. The results indicate that significant differences exist among different vehicle types and LC directions. Compared with CNNs (convolutional neural networks), LSTM (long short-term memory), and BiLSTM (bi-directional long short-term memory), CNN-BiLSTM-Attention performs best in recognizing the risk of LC in all cases. Moreover, the key feature groups that have the optimal result of recognizing the risk of LC in different cases are obtained.
]]>Electronics doi: 10.3390/electronics13061096
Authors: Mengmin He Gaofeng Cui Weidong Wang Xinzhou Cheng Lexi Xu
Recently, the low earth orbit (LEO) mega-constellation faces serious time-varying interferences due to spectrum sharing, dense deployment, and high mobility. Therefore, it is important to study the interference avoidance techniques for the dense LEO satellite system. In this paper, the interference situational aware beam pointing optimization technique is proposed. Firstly, the angle of departure (AoD) and angle of arrival (AoA) of the interfering links are obtained to represent the time-varying interference. Then, the interference avoidance problem for dense LEO satellite systems is modeled as a non-convex optimization problem, and a particle swarm optimization (PSO) based method is proposed to obtain the optimal beam pointing of the user terminal (UT). Simulations show that the relative error of the mean signal-to-interference plus noise ratio (SINR) obtained by the proposed method is 0.51%, so the co-channel interference can be effectively mitigated for the dense LEO satellite communication system.
]]>Electronics doi: 10.3390/electronics13061095
Authors: Michalina Kotyla Aleksandra Banasiewicz Pavlo Krot Paweł Śliwiński Radosław Zimroz
The mining industry faces persistent challenges related to hazardous gas emissions. Diesel engine-powered wheeled vehicles are commonly used during work shifts and are a primary source of nitrogen oxides (NOx) in underground mines. Despite diesel engine manufacturers providing gas generation data, mining companies need to predict NOx emissions from numerous load-haul-dumping (LHD) vehicles operating under dynamic conditions and not always equipped with gas sensors. This study focused on two ensemble methods: bootstrap aggregation (bagging) and least-square boosting (boosting) to predict NOx emissions. These approaches combine multiple weaker statistical models to yield a robust result. The innovation of this research is in the statistical analysis and selection of LHD vehicles’ working parameters, which are most suitable for NOx emission prediction; development of the procedure of source data cleaning and processing, model building and analyzing factors, which may influence the accuracy; and the comparison of two ensemble methods and showing their advantages and limitations for this specific engineering application, which was not previously reported in the literature. For datasets obtained from the same LHD vehicle and different operators, the more efficient bagging method gave a coefficient of determination R2 > 0.79 and the RMSE (root mean square error) was under 30 ppm, which is comparable with the measurement accuracy for transient regimes of physical NOx sensors available in the market. The obtained insights can be utilized as input for mine ventilation systems, enhancing mining transport management, reducing workplace air pollution, improving work planning, and enhancing personnel safety.
]]>Electronics doi: 10.3390/electronics13061094
Authors: Xiaoming Li Hui Xu Yabin An Xiting Feng
The high precision and low power consumption of the clock generator are critical in passive RFID transponders and passive IoT chips, but fluctuations in PVT can cause considerable degradation in the precision of the chip’s internal clocks. This paper proposes a high-precision clock circuit with a single-shot calibration method to addresses this issue in a low-power clock solution. Based on the reference timespan in the preamble of the down-link RF envelope, a TDIF (Time-digital to current-frequency) calibration method was implemented with both a streamlined procedure and customized circuits. By computing the difference between the time counts and applying it to an ultra-low-power, current-starved oscillator, the current change ratio can be linearly controlled. Compared to the traditional integer frequency division scheme used by passive tags for a 160 k bits up-link data rate, the required frequency for the clock generator was reduced from 960 kHz to 320 kHz, the calibration error was reduced from ±10% to ±3% for ±25% frequency deviation, the calibration time was 133.3 μs for a single shot in this work, and the power consumption was 158 nW after the calibration was completed. This leads to an excellent power efficiency of 0.59 nW/kHz and meets the requirements of low power, low cost, and PVT robustness in the RF-powered passive IoT chips. By appropriately increasing the number of calibration digits and the duration, this calibration approach could also be used for other ultra-low-power passive IoT chips that require higher-precision clocking without the use of off-chip crystals.
]]>Electronics doi: 10.3390/electronics13061093
Authors: Chenyang Li Long Zhang Qiusheng Zheng
Diffusion models have achieved tremendous success in modeling continuous data modalities, such as images, audio, and video, yet their application in discrete data domains (e.g., natural language) has been limited. Existing methods primarily represent discrete text in a continuous diffusion space, incurring significant computational overhead during training and resulting in slow sampling speeds. This paper introduces LaDiffuSeq, a latent diffusion-based text generation model incorporating an encoder–decoder structure. Specifically, it first employs a pretrained encoder to map sequences composed of attributes and corresponding text into a low-dimensional latent vector space. Then, without the guidance of a classifier, it performs the diffusion process for the sequence’s corresponding latent space. Finally, a pretrained decoder is used to decode the newly generated latent vectors, producing target texts that are relevant to themes and possess multiple emotional granularities. Compared to the benchmark model, DiffuSeq, this model achieves BERTScore improvements of 0.105 and 0.009 on two public real-world datasets (ChnSentiCorp and a debate dataset), respectively; perplexity falls by 3.333 and 4.562; and it effectively quadruples the text generation sampling speed.
]]>Electronics doi: 10.3390/electronics13061092
Authors: Nige Li Ziang Lu Yuanyuan Ma Yanjiao Chen Jiahan Dong
To address the issue of low accuracy in detecting malicious program behaviors in new power system edge-side applications, we present a detection model based on API call sequences that combines rule matching and deep learning techniques in this paper. We first use the PrefixSpan algorithm to mine frequent API call sequences in different threads of the same program within a malicious program dataset to create a rule base for malicious behavior sequences. The API call sequences to be examined are then matched using the malicious behavior sequence matching model, and those that do not match are fed into the TextCNN deep learning detection model for additional detection. The two models collaborate to accomplish program behavior detection. Experimental results demonstrate that the proposed detection model can effectively identify malicious samples and discern malicious program behaviors.
]]>Electronics doi: 10.3390/electronics13061091
Authors: Xin Che Zelong Ma Xinda Qi Wenxian Li Haipeng Niu Changxiang Yan
Barrier-function-based adaptive fast-terminal sliding-mode control approaches have been devised to enhance the precision of speed regulation of permanent magnet synchronous motors (PMSMs). Firstly, the speed loop utilizes fast-terminal sliding-mode control, which contributes to a faster convergence rate and enhances the robustness of the system. By adopting this control technique, the system can quickly reach the desired speed setpoint and effectively handle disturbances. Secondly, an adaptive law based on the barrier function is employed to adjust the control gain adaptively. The proposed adaptive law considers the magnitude of the disturbance and effectively mitigates chattering resulting from excessive switching gain. Unlike conventional control methods, the design of the adaptive fast-terminal sliding-mode control does not require attaining the upper limit of the lumped disturbances. Experimental results are presented to validate the proposed approach. These results demonstrate that the proposed method outperforms the conventional terminal sliding mode control technique in terms of handling both external and internal disturbances.
]]>Electronics doi: 10.3390/electronics13061090
Authors: Chien-Yi Huang Pei-Xuan Tsai
Machine vision systems use industrial cameras’ digital sensors to collect images and use computers for image pre-processing, analysis, and the measurements of various features to make decisions. With increasing capacity and quality demands in the electronic industry, incoming quality control (IQC) standards are becoming more and more stringent. The industry’s incoming quality control is mainly based on manual sampling. Although it saves time and costs, the miss rate is still high. This study aimed to establish an automatic defect detection system that could quickly identify defects in the gold finger on printed circuit boards (PCBs) according to the manufacturer’s standard. In the general training iteration process of deep learning, parameters required for image processing and deductive reasoning operations are automatically updated. In this study, we discussed and compared the object detection networks of the YOLOv3 (You Only Look Once, Version 3) and Faster Region-Based Convolutional Neural Network (Faster R-CNN) algorithms. The results showed that the defect classification detection model, established based on the YOLOv3 network architecture, could identify defects with an accuracy of 95%. Therefore, the IQC sampling inspection was changed to a full inspection, and the surface mount technology (SMT) full inspection station was canceled to reduce the need for inspection personnel.
]]>Electronics doi: 10.3390/electronics13061089
Authors: Bowen Zhang Wei Du Nengwu Liu Guang Fu
In this paper, we propose the design and simulation of a dielectric horn antenna using the metamaterial method; this antenna has a double-layer dielectric waveguide transmission structure. With the continuous development of microwave feed sources, the traditional waveguide systems are no longer able to meet today’s antenna requirements. Consequently, dielectric-loading technology is gradually being applied to design horns. A key advantage of this antenna is its significantly expanded bandwidth of 163.3%. Furthermore, when compared to ridge horns, this dielectric-loaded horn demonstrates superior radiation properties across the entire frequency band, including aperture efficiency (95% in the L band, 65.4% in the S band, 41.5% in the C band, and 28.7% in the X band) and cross-polarization isolation (≥51.6 dB). In addition, before researching the theory of dielectric-loading technology, the modes of the horns should be analyzed. This helps us to better control the hybrid mode. The metamaterial method was applied to achieve stable dielectric properties. We finally conducted experiments on the antenna to validate the relevant theories and feasibility.
]]>Electronics doi: 10.3390/electronics13061088
Authors: Geunmin Lee Wonha Kim
This paper proposes a radial image processing method performed in an L1-norm-based discrete polar coordinate system. For this purpose, we address the problem that polar coordinates based on the L2-norm cannot exist in discrete systems and then develop a method for converting Cartesian coordinates to L1-norm-based discrete polar coordinates. The proposed method greatly reduces the directional variance occurring in the Cartesian coordinate system and so processes radial directional images along the directions of the local image signal flows. To verify the usages of the proposed method, it was applied to the stabilization of mass-type breast cancer images, a segmentation of extremely deformable objects such as biomedical objects. In all cases, the proposed method produced superior results compared to the processing in the Cartesian coordinate systems. The proposed method is useful for processing or analyzing diffusing and deformable images such as bio-cell and smoke images.
]]>Electronics doi: 10.3390/electronics13061087
Authors: Zhiyuan Ma Jiwei Qin Meiqi Pan Song Tang Jinpeng Mi Dan Liu
Natural language understanding is a crucial aspect of task-oriented dialogue systems, encompassing intent detection (ID) and slot filling (SF). Conventional approaches for ID and SF solve the problems in a separate manners, while recent studies are now leaning toward joint modeling to tackle multi-intent detection and SF. Although the advancements in prompt learning offer a unified framework for ID and SF, current prompt-based methods fail to fully exploit the semantics of intent and slot labels. Additionally, the potential of using prompt learning to model the correlation between ID and SF in multi-intent scenarios remains unexplored. To address the issue, we propose a text-generative framework that unifies ID and SF. The prompt templates are constructed with label semantical descriptions. Moreover, we introduce an auxiliary task to explicitly capture the correlation between ID and SF. The experimental results on two benchmark datasets show that our method achieves an overall accuracy improvement of 0.4–1.5% in a full-data scenario and 1.4–2.7% in a few-shot setting compared with a prior method, establishing it as a new state-of-the-art approach.
]]>Electronics doi: 10.3390/electronics13061086
Authors: Mingzhu Xun Yudong Li Mingyu Liu
In this paper, the effects of proton and gamma irradiation on reach-through single-photon avalanche diodes (SPADs) are investigated. The I–V characteristics, gain and spectral response of SPAD devices under proton and gamma irradiation were measured at different proton energies and irradiation bias conditions. Comparison experiments of proton and gamma irradiation were performed in the radiation environment of geosynchronous transfer orbit (GTO) with two different radiation shielding designs at the same total ionizing dose (TID). The results show that after 30 MeV and 60 MeV proton irradiation, the leakage current and gain increase, while the spectral response decreases slightly. The leakage current degradation is more severe under the “ON”-bias condition compared to the “OFF”-bias condition, and it is more sensitive to the displacement radiation damage caused by protons compared to gamma rays under the same TID. Further analysis reveals that the non-elastic and elastic cross-section of protons in silicon is 1.05 × 105 times greater than that of gamma rays. This results in SPAD devices being more sensitive to displacement radiation damage than ionizing radiation damage. Under the designed shielding conditions, the leakage current, gain and spectral response parameters of SPADs do not show significant performance degradation in the orbit.
]]>Electronics doi: 10.3390/electronics13061085
Authors: Semih Bal Zoltán Ádám Tamus
The distribution grid comprises cables with diverse constructions. The insulating material used in low-voltage (LV) distribution cables is predominantly PVC. Furthermore, the presence of cables with different structures in the grid poses challenges in detecting the aging of the cable network. Finding a universal and dependable condition-monitoring technique that can be applied to various types of cables is indeed a challenge. The diverse construction and materials used in different cables make it difficult to identify a single monitoring approach that can effectively assess the condition of all cables. To address this issue, this study aims to compare the thermal aging behavior of different LV distribution cables with various structures, i.e., one cable contains a PVC belting layer, while the other contains filler material. The growing adoption of distributed generation sources, electric vehicles, and new consumer appliances in low-voltage distribution grids can lead to short, repetitive overloads on the low-voltage cable network. Hence, these cable samples were exposed to short-term cyclic accelerated aging in the climate chamber at 110 °C. The cable’s overall behavior under thermal stress was evaluated through frequency and time domain electrical measurements (including tan δ and extended voltage response) and a mechanical measurement (Shore D). The tan δ was measured in the frequency range of 20 Hz–500 kHz by using the Wayne-Kerr impedance analyzer. The extended voltage response measurement was conducted using a C# application developed in-house specifically for laboratory measurements in the .NET environment. The study observed a strong correlation between the different measurement methods used, indicating that electrical methods have the potential to be adopted as a non-destructive condition-monitoring technique.
]]>Electronics doi: 10.3390/electronics13061084
Authors: Juncheol Kim Neungin Jeon Wonkyu Do Euihoon Jung Hongjin Kim Hojin Park Young-Chan Jang
A second-order delta-sigma modulator (DSM) is proposed for readout integrated circuits of sensor applications requiring a small area and low-power consumption. The proposed second-order CIFF DSM with the architecture of cascaded-of-integrator feedforward (CIFF) basically consists of two integrators, a 3-bit quantizer, data-weighted averaging (DWA) circuit, and clock generator. The use of the 3-bit quantizer instead of the single-bit quantizer reduces the size of the feedback capacitor in the first integrator. The 3-bit quantizer is designed based on a successive approximation register analog-to-digital converter for small area and low power implementation. Furthermore, the proposed second-order CIFF DSM has a single supply without an additional reference driver while having a wide analog input voltage range with rail to rail. The proposed second-order CIFF DSM, implemented using a 130 nm 1-poly 6-metal CMOS process with a supply of 1.5 V, has an area of 0.096 mm2. It has a sampling frequency of 500 kHz for the implementation of an input bandwidth of 2 kHz and an oversampling ratio of 125. The measured peak signal-to-noise and distortion ratio is approximately 90 dB when the differential analog input signal has a frequency of 353 Hz and an amplitude of 1.2 Vpp. The measured dynamic range is approximately 96.3 dB.
]]>Electronics doi: 10.3390/electronics13061083
Authors: Pedro Paiva Rui Castro
To achieve an energy sector independent from fossil fuels, a significant increase in the penetration of variable renewable energy sources, such as solar and wind power, is imperative. However, these sources lack the inertia provided by conventional thermo-electric power stations, which is essential for maintaining grid frequency stability. In this study, a grid resembling Madeira Island’s power generation mix was modeled using the Matlab/Simulink platform. The model included solar, wind, hydro, and thermo-electric generation to accurately represent the energy landscape of Madeira Island. Three scenarios were examined: one reflecting the current power generation on Madeira Island, a future scenario with a substantial rise in the percentage of photovoltaic (PV) generation, and the same future scenario but incorporating a battery energy storage system (BESS). Various analyses were conducted to assess the impact on frequency stability during a ground fault and rapid load/generation changes. In the future scenario without a BESS, the thermoelectric power plant generator desynchronized, leading to system collapse in several simulations. However, with the addition of a BESS, a significant improvement in frequency stability was observed. The thermoelectric power plant generator could return to a steady state after each disturbance. Furthermore, both the maximum frequency deviation and the absolute value of the Rate of Change of Frequency (ROCOF) were reduced, indicating enhanced system performance and stability.
]]>Electronics doi: 10.3390/electronics13061082
Authors: Minoru Sasaki Yuki Tsuda Kojiro Matsushita
In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot’s position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments.
]]>Electronics doi: 10.3390/electronics13061081
Authors: Wenbao Jiang Yongpan Wang Shuai Ye
The traditional Internet has many security problems. It is difficult to guarantee the authenticity, integrity, and synchronization of message transmission, and it lacks a message-traceability mechanism, which is caused by its performance-oriented design. To address these problems, this paper proposes a memorable communication method based on cryptographic accumulators. In this method, both parties in the communication can verify the message data sent and received arbitrarily by virtue of the memory value. As long as a simple memory value comparison is performed, the strong consistency of all message data can be ensured. This method has the security advantages of synchronization, verification, traceability, and non-tamperability, as well as the performance advantages brought by batch signature and verification. In this paper, the memorable communication model, the memory function, and the memorable communication process are designed, and theoretical analysis shows that the memorable communication method has synchronization and traceability and can realize batch signature and authentication. In addition, a chain-key can be constructed based on a memory value to achieve key per-packet updating. Comparative analysis shows the transmission efficiency, traceability efficiency, and security performance of the memorable communication method.
]]>Electronics doi: 10.3390/electronics13061080
Authors: Yu Bai Pengpeng Li Zhipeng Cui Peng Yang Weihua Li
Herein, to address the challenges faced by Automatic Guided Vehicles (AGVs) in construction site environments, including heavy vehicle loads, extensive road search areas, and randomly distributed obstacles, this paper presents a hierarchical trajectory planning algorithm that combines coarse planning and precise planning. In the first-level coarse planning, lateral and longitudinal sampling is performed based on road environment constraints. A multi-criteria cost function is designed, taking into account factors such as deviation from the road centerline, shortest path cost, and obstacle collision safety cost. An efficient dynamic programming algorithm is used to obtain the optimal path. Considering nonholonomic constraints of vehicles, eliminating inflection points using improved B-Spline path fitting, and a quadratic programming algorithm is proposed to enhance path smoothness, completing the coarse planning algorithm. In the second-level precise planning, the coarse planning path is used as a reference line, and small-range sampling is conducted based on AGV motion constraints, including lateral displacement and longitudinal velocity. Lateral and longitudinal polynomials are constructed. To address the impact of randomly appearing obstacles on vehicle stability and safety, an evaluation function is designed, considering factors such as jerk and acceleration. The optimal trajectory is determined through collision detection, ensuring both safe obstacle avoidance and AGV smoothness. Experimental results demonstrate the effectiveness of this method in solving the path planning challenges faced by AGVs in construction site environments characterized by heavy vehicle loads, extensive road search areas, and randomly distributed obstacles.
]]>Electronics doi: 10.3390/electronics13061079
Authors: Sang Mun Shin Asad Rasheed Park Kil-Heum Kalyana C. Veluvolu
Short-term electric load forecasting (STLF) plays a pivotal role in modern power system management, bolstering forecasting accuracy and efficiency. This enhancement assists power utilities in formulating robust operational strategies, consequently fostering economic and social advantages within the systems. Existing methods employed for STLF either exhibit poor forecasting performance or require longer computational time. To address these challenges, this paper introduces a hybrid learning approach comprising variational mode decomposition (VMD) and random vector functional link network (RVFL). The RVFL network, serving as a universal approximator, showcases remarkable accuracy and fast computation, owing to the randomly generated weights connecting input and hidden layers. Additionally, the direct links between hidden and output layers, combined with the availability of a closed-form solution for parameter computation, further contribute to its efficiency. The effectiveness of the proposed VMD-RVFL was assessed using electric load datasets obtained from the Australian Energy Market Operator (AEMO). Moreover, the effectiveness of the proposed method is demonstrated by comparing it with existing benchmark forecasting methods using two performance indices such as root mean square error (RMSE) and mean absolute percentage error (MAPE). As a result, our proposed method requires less computational time and yielded accurate and robust prediction performance when compared with existing methods.
]]>Electronics doi: 10.3390/electronics13061078
Authors: Hai Tang Weilin Xu Haiou Li Baolin Wei Xueming Wei
This paper presents a level-crossing successive-approximation-register (LC-SAR) hybrid analog-to-digital converter (ADC) that combines an LC ADC with an SAR ADC, which may be used for Internet of Things (IoT) random sparse event scenarios. The sampling frequency of a traditional LC ADC is usually proportional to the maximum instantaneous rate of change of the input signal; therefore, a higher input signal frequency inevitably leads to higher system power consumption. However, the proposed hybrid ADC uses the input level difference between the two moments before and after level-crossing detection, thereby ensuring a higher conversion precision and lower power consumption, even at higher input signal frequencies. Compared with traditional LC ADC or SAR ADC, the proposed hybrid ADC combines the ultralow-power advantage of LC ADC with the high-precision advantage of SAR ADC in converting IoT data with sparse characteristics such as ECG, EEG, and brain potential. The LC-SAR hybrid ADC is designed with a 0.18 μm CMOS process and consumes 4.34 μW at a 1.8 V supply voltage, achieving an SNDR of 67.41 dB and a bandwidth of 20 kHz. The spectrum analysis result was 10.85 ENOB when the input sinusoidal signal was 14.975 kHz. When inputted with an ECG signal, the system power consumption was as low as 0.49 μW. Furthermore, the proposed hybrid ADC obtained a good figure of merit, with FoMw and FoMs reaching 58.8 fJ/conv.steps and 164 dB, respectively. Compared to a conventional uniform sampling ADC, approximately 80% of the power savings and an 8x compression ratio can be achieved in physiological signal acquisition applications.
]]>Electronics doi: 10.3390/electronics13061077
Authors: Peter Baumann Oliver Kotte Lars Mikelsons Dieter Schramm
Currently, innovations in mechatronic products often occur at the system level, requiring consideration of component interactions throughout the entire development process. In the earlier phases of development, this is accomplished by coupling virtual prototypes such as simulation models. As the development progresses and real prototypes of certain system components become available, real-virtual prototypes (RVPs) are established with the help of network communication. However, network effects—all of which can be interpreted as latencies in simplified terms—distort the system behavior of RVPs. To reduce these distortions, we propose a coupling method for RVPs that compensates for latencies. We present an easily applicable approach by introducing a generic coupling algorithm based on error space extrapolation. Furthermore, we enable online learning by transforming coupling algorithms into feedforward neural networks. Additionally, we conduct a frequency domain analysis to assess the impact of coupling faults and algorithms on the system behavior of RVPs and derive a method for optimally designing coupling algorithms. To demonstrate the effectiveness of the coupling method, we apply it to a hybrid vehicle that is productively used as an RVP in the industry. We show that the optimally designed and trained coupling algorithm significantly improves the credibility of the RVP.
]]>Electronics doi: 10.3390/electronics13061076
Authors: Jisoo Park Jihyun Shin Hocheon Yoo
The concept of neuromorphic devices, aiming to process large amounts of information in parallel, at low power, high speed, and high efficiency, is to mimic the functions of human brain by emulating biological neural behavior. Optoelectronic neuromorphic devices are particularly suitable for neuromorphic applications with their ability to generate various pulses based on wavelength and to control synaptic stimulation. Each wavelength (ultraviolet, visible, and infrared) has specific advantages and optimal applications. Here, the heterostructure-based optoelectronic neuromorphic devices are explored across the full wavelength range (ultraviolet to infrared) by categorizing them on the basis of irradiated wavelength and structure (two-terminal and three-terminal) with respect to emerging optoelectrical materials. The relationship between neuromorphic applications, light wavelength, and mechanism is revisited. Finally, the potential and challenging aspects of next-generation optoelectronic neuromorphic devices are presented, which can assist in the design of suitable materials and structures for neuromorphic-based applications.
]]>Electronics doi: 10.3390/electronics13061075
Authors: Aiying Guo Kai Shen Jingjing Liu
Transformers have performed better than traditional convolutional neural networks (CNNs) for image super-resolution (SR) reconstruction in recent years. Currently, shifted window multi-head self-attention based on the swin transformer is a typical method. Specifically, the multi-head self-attention is used to extract local features in each window, and then a shifted window strategy is used to discover information interaction between different windows. However, this information interaction method needs to be more efficient and include some global feature information, which limits the model’s performance to a certain extent. Furthermore, optimizing the utilization of shallow features, which exhibit significant energy reserves and invaluable low-frequency information, is critical for advancing the efficacy of super-resolution techniques. In order to solve the above issues, we propose the feature-enhanced fused attention (FE-FAIR) method for image super-resolution. Specifically, we design the multi-scale feature extraction module (MSFE) as a shallow feature extraction layer to extract rich low-frequency information from different scales. In addition, we propose the fused attention block (FAB), which introduces channel attention in the form of residual connection based on shifted window self-attention, effectively achieving the fusion of global and local features. Simultaneously, we also discuss other methods to enhance the performance of the FE-FAIR method, such as optimizing the loss function, increasing the window size, and using pre-training strategies. Compared with state-of-the-art SR methods, our proposed method demonstrates better performance. For instance, FE-FAIR outperforms SwinIR by over 0.9 dB when evaluated on the Urban100 (×4) dataset.
]]>Electronics doi: 10.3390/electronics13061074
Authors: Samuel López-Asunción Pablo Ituero
Spiking neural networks (SNNs) promise to perform tasks currently performed by classical artificial neural networks (ANNs) faster, in smaller footprints, and using less energy. Neuromorphic processors are set out to revolutionize computing at a large scale, but the move to edge-computing applications calls for finely-tuned custom implementations to keep pushing towards more efficient systems. To that end, we examined the architectural design space for executing spiking neuron models on FPGA platforms, focusing on achieving ultra-low area and power consumption. This work presents an efficient clock-driven spiking neuron architecture used for the implementation of both fully-connected cores and 2D convolutional cores, which rely on deep pipelines for synaptic processing and distributed memory for weight and neuron states. With them, we developed an accelerator for an SNN version of the LeNet-5 network trained on the MNIST dataset. At around 5.5 slices/neuron and only 348 mW, it is able to use 33% less area and four times less power per neuron as current state-of-the-art implementations while keeping low simulation step times.
]]>Electronics doi: 10.3390/electronics13061073
Authors: Yaqing Chi Chang Cai Li Cai
Research on the effects of radiation on advanced electronic devices and integrated circuits has experienced rapid growth over the last few years, resulting in many approaches being developed for the modeling of radiation’s effects and the design of advanced radiation-hardened electronic devices and integrated circuits [...]
]]>Electronics doi: 10.3390/electronics13061072
Authors: Minxiao Wang Ning Yang Yanhui Guo Ning Weng
In an era marked by the escalating architectural complexity of the Internet, network intrusion detection stands as a pivotal element in cybersecurity. This paper introduces Learn-IDS, an innovative framework crafted to bridge existing gaps between datasets and the training process within deep learning (DL) models for Network Intrusion Detection Systems (NIDS). To elevate conventional DL-based NIDS methods, which are frequently challenged by the evolving cyber threat landscape and exhibit limited generalizability across various environments, Learn-IDS works as a potent and adaptable platform and effectively tackles the challenges associated with datasets used in deep learning model training. Learn-IDS takes advantage of the raw data to address three challenges of existing published datasets, which are (1) the provided tabular format is not suitable for the diversity of DL models; (2) the fixed traffic instances are not suitable for the dynamic network scenarios; (3) the isolated published datasets cannot meet the cross-dataset requirement of DL-based NIDS studies. The data processing results illustrate that the proposed framework can correctly process and label the raw data with an average of 90% accuracy across three published datasets. To demonstrate how to use Learn-IDS for a DL-based NIDS study, we present two simple case studies. The case study on cross-dataset sampling function reports an average of 30.3% OOD accuracy improvement. The case study on data formatting function shows that introducing temporal information can enhance the detection accuracy by 4.1%.The experimental results illustrate that the proposed framework, through the synergistic fusion of datasets and DL models, not only enhances detection precision but also dynamically adapts to emerging threats within complex scenarios.
]]>Electronics doi: 10.3390/electronics13061069
Authors: Jinfa Ge Dongsheng Zhu Lijuan Sun Chong Han Jian Guo
Indoor target localization is pivotal across various applications, encompassing security monitoring, behavioral analysis, and elderly care. This work proposes an advanced target localization algorithm that harnesses antennas endowed with sensing capabilities to capture the phase change in the signal ratio, derived from the signal amplitude. This phase change, indicative of the target’s movement direction, is analyzed alongside the front and rear arrival angle information and signal amplitude characteristics obtained from LoRa signals. The algorithm, through a comprehensive examination of the phase change patterns, amalgamated with arrival angle data and signal amplitude characteristics, effectively estimates the precise location of the target. Experimental validations underscore the algorithm’s efficacy in determining the target’s location during continuous walking activity. Conducted within a 6 m × 12 m open platform, the algorithm achieves an average localization error of 48.5 cm, underscoring its superior performance compared to existing methodologies.
]]>Electronics doi: 10.3390/electronics13061071
Authors: Xiaocen Xue Jiejie Huang Shun Sang
Frequency regulation and droop control of doubly fed induction generators (DFIGs) can quickly respond to frequency changes and reduce the maximum rate of frequency (MROFF) in power systems. However, due to real-time dynamic changes in the MPPT control loop, the ability to improve the lowest frequency point is limited. Therefore, this article first describes an in-depth analysis of the dynamic characteristics of the incremental power of frequency regulation with droop control using an equivalent linear model. The limitations of improving the lowest frequency point under the influence of dynamic changes in the MPPT control loop are revealed. Secondly, to address the impact of these dynamics, an improved decoupling frequency regulation (IDFR) strategy based on power tracking is proposed, aiming to increase the maximum frequency deviation (MFD) and MROCOF. Then, in order to overcome the difficulty of adjusting control coefficients in the IDFR strategy, an adaptive control coefficient tuning fuzzy control method based on frequency deviation and ROCOF was proposed to flexibly adjust control requirements under various working conditions, thereby improving the control stability and performance of the system and effectively solving the problem of control coefficient allocation. Finally, to verify the frequency regulation performance of the proposed IDFR strategy under various operating conditions, simulations were conducted based on different disturbances and wind conditions. The results show that the proposed IDFR strategy significantly improves the system MFD and MROCOF improvement ability under various conditions.
]]>Electronics doi: 10.3390/electronics13061070
Authors: Huichu Fu Yiming Lai Chunrong Pan Siwei Zhang Liping Bai Jie Li
For semiconductor manufacturing, automatic optical inspections (AOIs) are important for chip quality inspection. An AOI system contains a robot arm, an industrial camera, a x-y platform, and a visual inspection module. Using the industrial camera, a wafer map can be obtained and then sent to the visual inspection module to compare with qualified chip features. There is a baseline in the x-y platform. Due to the limitations of the robot arm flexibility, it is difficult for the robot arm to control the angles between the chip orientation and the baseline every time, which decreases the defect recognition accuracy. This work aims to improve the defect recognition accuracy and efficiency of the AOI system. Specifically, an efficient method is presented to calculate the angle between the baseline and chip orientation. Then, the wafer map can be rotated, such that the angle equals to zero. Further, a powerful system is established to recode the rotated chip coordinate, such that the unqualified chip positions can be located efficiently. This method is called a central array method. The central array method with deep learning methods forms an AI-based AOI system. Extensive experiments demonstrate that our proposed method performs well in improving the chip quality inspection efficiency and accuracy. Nevertheless, the proposed method still has challenges in implementation since it requires integration with the manufacturing line.
]]>Electronics doi: 10.3390/electronics13061068
Authors: Yating Hu Qijin Wang Chao Wang Yu Qian Ying Xue Hongqiang Wang
Pest detection: This process is essential for the early warning of pests in the agricultural sector. However, the challenges posed by agricultural pest datasets include but are not limited to species diversity, small individuals, high concentration, and high similarity, which greatly increase the difficulty of pest detection and control. To effectively solve these problems, this paper proposes an innovative object detection model named MACNet. MACNet is optimized based on YOLOv8s, introducing a content-based feature sampling strategy to obtain richer object feature information, and adopts distribution shifting convolution technology, which not only improves the accuracy of detection but also successfully reduces the size of the model, making it more suitable for deployment in the actual environment. Finally, our test results on the Pest24 dataset verify the good performance of MACNet; its detection accuracy reaches 43.1 AP which is 0.5 AP higher than that of YOLOv8s, and the computational effort is reduced by about 30%. This achievement not only demonstrates the efficiency of MACNet in agricultural pest detection, but also further confirms the great potential and practical value of deep learning technology in complex application scenarios.
]]>Electronics doi: 10.3390/electronics13061067
Authors: Yuefei Zuo Shushu Zhu Yebing Cui Chuang Liu Xiaogang Lin
In this paper, to achieve auto-setting of PI controller gains when mechanical parameters are unknown, two adaptive PI controllers for speed control of electric drives are developed based on model reference adaptive identification. The adaptive linear neuron (ADALINE) neural network is used to interpret the proposed adaptive PI controller. The effect of the low-pass filter used for the feedback speed and the Coulomb friction torque on parameter identification is analysed, and a new motion equation using filtered speed is given. Additionally, a parameter identification method based on unipolar speed reference is provided. The two proposed adaptive PI controllers and the conventional PI controller are compared based on the high-precision digital simulation using MATLAB/Simulink (version R2023a). The simulation results show that both of the two proposed adaptive PI controllers are able to identify mechanical parameters, but the adaptive PI-1 controller outperforms the adaptive PI-2 controller due to its better noise attenuation performance.
]]>Electronics doi: 10.3390/electronics13061063
Authors: Bingxuan Yu Xiang Lei Ziyun Shao Linni Jian
Accurate carbon emission accounting for electric vehicles (EVs) is particularly important, especially for those participating in the carbon market. However, the participation of numerous EVs in vehicle-to-grid (V2G) scheduling complicates the precise accounting of individual EV emissions. This paper presents a novel approach to carbon accounting and benefits distribution for EVs. It includes a low-carbon dispatch model for a distribution system (DS), aimed at reducing total emissions through strategic EV charging scheduling. Further, an improved carbon emission flow accounting model is proposed to calculate the carbon reduction of EVs before and after low-carbon dispatch. It enables real-time carbon flow tracking during EV charging and discharging, then accurately quantifies the carbon reduction amount. Additionally, it employs the Shapley value method to ensure equitable distribution of carbon revenue, balancing low-carbon operation costs and carbon reduction contributions. A case study based on a 31-node campus distribution network demonstrated that effective scheduling of 1296 EVs can significantly reduce system carbon emissions. This method can accurately account for the carbon emissions of EVs under different charging states, and provides a balanced analysis of EV carbon reduction contributions and costs, advocating for fair revenue allocation.
]]>Electronics doi: 10.3390/electronics13061066
Authors: Wei Guo Xiaoyang Liu Chenghong Lu Lei Jing
Falls among the elderly are a significant public health issue, resulting in about 684,000 deaths annually. Such incidents often lead to severe consequences including fractures, contusions, and cranial injuries, immensely affecting the quality of life and independence of the elderly. Existing fall detection methods using cameras and wearable sensors face challenges such as privacy concerns, blind spots in vision and being troublesome to wear. In this paper, we propose PIFall, a Pressure Insole-Based Fall Detection System for the Elderly, utilizing the ResNet3D algorithm. Initially, we design and fabricate a pair of insoles equipped with low-cost resistive films to measure plantar pressure, arranging 5×9 pressure sensors on each insole. Furthermore, we present a fall detection method that combines ResNet(2+1)D with an insole-based sensor matrix, utilizing time-series ‘stress videos’ derived from pressure map data as input. Lastly, we collect data on 12 different actions from five subjects, including fall risk activities specifically designed to be easily confused with actual falls. The system achieves an overall accuracy of 91% in detecting falls and 94% in identifying specific fall actions. Additionally, feedback is gathered from eight elderly individuals using a structured questionnaire to assess user experience and satisfaction with the pressure insoles.
]]>Electronics doi: 10.3390/electronics13061065
Authors: Józef Lisowski
The analysis of the state of the literature in the field of methods of perception and control of the movement of autonomous vehicles shows the possibilities of improving them by using an artificial neural network to generate domains of prohibited maneuvers of passing objects, contributing to increasing the safety of autonomous driving in various real conditions of the surrounding environment. This article concerns radar perception, which involves receiving information about the movement of many autonomous objects, then identifying and assigning them a collision risk and preparing a maneuvering response. In the identification process, each object is assigned a domain generated by a previously trained neural network. The size of the domain is proportional to the risk of collisions and distance changes during autonomous driving. Then, an optimal trajectory is determined from among the possible safe paths, ensuring control in a minimum of time. The presented solution to the radar perception task was illustrated with a computer simulation of autonomous driving in a situation of passing many objects. The main achievements presented in this article are the synthesis of a radar perception algorithm mapping the neural domains of autonomous objects characterizing their collision risk and the assessment of the degree of radar perception on the example of multi-object autonomous driving simulation.
]]>Electronics doi: 10.3390/electronics13061064
Authors: Shihao Wang Xiaoyu Liu Siquan Yu Xinghua Zhu Bingbing Chen Xiaoyu Sun
Underwater object detection is an important task in marine exploration. The existing autonomous underwater vehicle (AUV) designs typically lack an integrated object detection module and are constrained by communication limitations in underwater environments. This results in a situation where AUV, when tasked with object detection missions, require real-time transmission of underwater sensing data to shore-based stations but are unable to do so. Consequently, the task is divided into two discontinuous phases: AUV acquisition of underwater data and shore-based object detection, leading to limited autonomy and intelligence for the AUV. In this paper, we propose a novel autonomous online underwater object detection system for AUV based on side-scan sonar (SSS). This system encompasses both hardware and software components and enables AUV to perform simultaneous data acquisition and object detection for underwater objects, thereby providing guidance for coherent AUV underwater operations. Firstly, this paper outlines the hardware design and layout of a portable integrated AUV for reconnaissance and strike missions, achieving online object detection through the integration of an acoustic processing computer. Subsequently, a modular design for the software architecture and a multi-threaded parallel design for the software workflow are developed, along with the integration of the YOLOv7 intelligent detection model, addressing three key technological challenges: real-time data processing, autonomous object detection, and intelligent online detection. Finally, lake experiments show that the system can meet the autonomy and real-time requirements of predefined object detection on AUV, and the average positioning error is better than 5 m, which verifies the feasibility and effectiveness of the system. This provides a new solution for underwater object detection in AUV.
]]>Electronics doi: 10.3390/electronics13061062
Authors: Stefan Popa Mihai Ivanovici Radu-Mihai Coliban
The 8b/10b IBM encoding scheme is used in a plethora of communication technologies, including USB, Gigabit Ethernet, and Serial ATA. We propose two primitive-based structural designs of an 8b/10b encoder and two of an 8b/10b decoder, all targeted at modern AMD FPGA architectures. Our aim is to reduce the amount of resources used for the implementations. We compare our designs with implementations resulting from behavioral models as well as with state-of-the-art solutions from the literature. The implementation results show that our solutions provide the lowest resource utilization with comparable maximum operating frequency and power consumption. The proposed structural designs are suitable for resource-constrained data communication protocol implementations that employ the IBM 8b/10b encoding scheme. This paper is an extended version of our paper published at the 2022 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, 10–11 November 2022.
]]>Electronics doi: 10.3390/electronics13061061
Authors: Aili Wang Kang Zhang Haibin Wu Yuji Iwahori Haisong Chen
Aiming to solve the problems of different spectral bands and spatial pixels contributing differently to hyperspectral image (HSI) classification, and sparse connectivity restricting the convolutional neural network to a globally dependent capture, we propose a HSI classification model combined with multi-scale residual spectral–spatial attention and an improved transformer in this paper. First, in order to efficiently highlight discriminative spectral–spatial information, we propose a multi-scale residual spectral–spatial feature extraction module that preserves the multi-scale information in a two-layer cascade structure, and the spectral–spatial features are refined by residual spectral–spatial attention for the feature-learning stage. In addition, to further capture the sequential spectral relationships, we combine the advantages of Cross-Attention and Re-Attention to alleviate computational burden and attention collapse issues, and propose the Cross-Re-Attention mechanism to achieve an improved transformer, which can efficiently alleviate the heavy memory footprint and huge computational burden of the model. The experimental results show that the overall accuracy of the proposed model in this paper can reach 98.71%, 99.33%, and 99.72% for Indiana Pines, Kennedy Space Center, and XuZhou datasets, respectively. The proposed method was verified to have high accuracy and effectiveness compared to the state-of-the-art models, which shows that the concept of the hybrid architecture opens a new window for HSI classification.
]]>Electronics doi: 10.3390/electronics13061060
Authors: Junjie Mi Wenxiang Deng Jianyong Yao Xianglong Liang
Manipulators are multi-rigid-body systems composed of multiple moving joints. During movement, the Coriolis force, centrifugal force, and gravity of the system undergo significant changes. The last three degrees of freedom (DOFs) of the wrist joint of a manipulator control the end attitude. Improving the command tracking accuracy of the wrist joint is a key challenge in controlling the end attitude of manipulators. In this study, a dynamics model of the mechanical arm–wrist joint is established based on the Lagrange method. An adaptive continuous robust integral of the sign of the error (ARISE) controller is designed using the reverse step method. Additionally, a linear extended state observer (LESO) is employed to estimate the time-varying interference existing in the system and compensate for it in the designed control rate. The stability of the Lyapunov function and the boundedness of the observer are proven. The proposed control method for the wrist joint is compared with other controllers on an experimental platform of multi-DOF hydraulic manipulators. The results demonstrate that the proposed method improves the control performance of hydraulic manipulators. The application of this method offers a new strategy and idea for achieving high-performance tracking control in hydraulic manipulators.
]]>Electronics doi: 10.3390/electronics13061059
Authors: Lei Xian Yansu Wang
Protein–protein interactions (PPIs) are pivotal in various physiological processes inside biological entities. Accurate identification of PPIs holds paramount significance for comprehending biological processes, deciphering disease mechanisms, and advancing medical research. Given the costly and labor-intensive nature of experimental approaches, a multitude of computational methods have been devised to enable swift and large-scale PPI prediction. This review offers a thorough examination of recent strides in computational methodologies for PPI prediction, with a particular focus on the utilization of deep learning techniques within this domain. Alongside a systematic classification and discussion of relevant databases, feature extraction strategies, and prominent computational approaches, we conclude with a thorough analysis of current challenges and prospects for the future of this field.
]]>Electronics doi: 10.3390/electronics13061058
Authors: Wei Zhong Yemin Dong Lili Lang Wei Xiong Lin Sun Yu Liu Haijing Liu Zhenwei Zhang
This paper proposes an all-digital calibration algorithm that utilizes a reference channel to suppress the timing mismatch in the Time-Interleaved Analog-to-Digital Converter (TIADC). The output of the reference channel is aligned with each sub-channel in turn, therefore enabling the simultaneous sampling and conversion of the same input signal. First, the statistical characteristics across the channels are employed for estimating the timing mismatch; then, by comparing the output difference between the reference channel and the sub-channels that are sampled simultaneously, the deviation of the derivator can be calibrated. Finally, combining both calibration results yields an accurate final output. This proposed algorithm provides an effective solution to improve TIADC performance in high-speed data acquisition systems. The proposed architecture is applied to a 12-bit 2.4 GS/s four-channel TIADC model, and then its effectiveness is verified. The simulation results exhibit that the Effective Number Of Bits (ENOB) at an input signal frequency of 984 MHz shows a remarkable improvement from 6.88 bits to 11.92 bits. The effectiveness of this technique is also demonstrated through the off-chip calibration of a commercial 12-bit four-channel 2 GS/s TIADC using a 680 MHz input signal that is based on the actual chip results.
]]>Electronics doi: 10.3390/electronics13061057
Authors: Mohd. Hasan Ali Sultana Razia Akhter
Cyber-attacks have adverse impacts on DC microgrid systems. Existing literature shows plenty of attack detection methods but lacks appropriate mitigation and prevention approaches for cyber-attacks in DC microgrids. To overcome this limitation, this paper proposes a novel solution based on a nonlinear controller to mitigate the adverse effects of various cyber-attacks, such as distributed denial of service attacks and false data injection attacks, on various components of a DC microgrid system consisting of a photovoltaic power source, a permanent magnet synchronous generator-based variable speed wind generator, a fuel cell, battery energy storage, and loads. To demonstrate the effectiveness of the proposed solution, single and repetitive cyber-attacks on specific components of the microgrid have been considered. An index-based quantitative improvement analysis for the proposed control method has been made. Extensive simulations have been performed by the MATLAB/Simulink V9 software. Simulation results demonstrate the effectiveness of the proposed nonlinear controller-based method in mitigating the adverse effects of cyber-attacks. Moreover, the performance of the proposed method is better than that of the proportional-integral controller. Due to the simplicity of the proposed solution, it can easily be implemented in real practice.
]]>Electronics doi: 10.3390/electronics13061056
Authors: Abdulrahman Alamer Basem Assiri
Blockchain technology is a decentralized and secure paradigm for data processing, sharing, and storing. It relies on consensus protocol for all decisions, which focuses on computational and resource capability. For example, proof of work (PoW) and proof of stake (PoS) are the most famous consensus protocols that are currently used. However, these current consensus protocols are required to recruit a node with a high computational or a large amount of cryptocurrency to act as a miner node and to generate a new block. Unfortunately, these PoW and PoS protocols could be impractical for adoption in today’s technological fields, such as the Internet of Things and healthcare. In addition, these protocols are susceptible to flexibility, security, and fairness issues, as they are discussed in detail in this work. Therefore, this paper introduces a proof of fairness (PoF) as a dynamic and secure consensus protocol for enhancing the mining selection process. The selection of the miner node is influenced by numerous factors, including the time required to generate a block based on the transaction’s sensitivity. Firstly, a reverse auction mechanism is designed as an incentive mechanism to encourage all nodes to participate in the miner selection process. In a reverse auction, each node will draw its strategy based on its computational capability and claimed cost. Secondly, an expressive language is developed to categorize transaction types based on their sensitivity to processing time, ensuring compatibility with our miner selection process. Thirdly, a homomorphic concept is designed as a security and privacy scheme to protect the bidder’s data confidentiality. Finally, an extensive evaluation involving numerical analysis was carried out to assess the efficiency of the suggested PoF protocol, which confirms that the proposed PoF is dynamic and more efficient than current PoW and PoS consensus protocols.
]]>Electronics doi: 10.3390/electronics13061055
Authors: Fabrizio Messina Corrado Santoro Federico Fausto Santoro
The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic’s dynamic mesh networking capabilities and LoRa’s low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices’ measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges.
]]>Electronics doi: 10.3390/electronics13061054
Authors: Yudi Chen Xiangyu Liu Changqing Li Jiao Zhu Min Wu Xiang Su
When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing.
]]>Electronics doi: 10.3390/electronics13061053
Authors: Sami Yaras Murat Dener
The most significant threat that networks established in IoT may encounter is cyber attacks. The most commonly encountered attacks among these threats are DDoS attacks. After attacks, the communication traffic of the network can be disrupted, and the energy of sensor nodes can quickly deplete. Therefore, the detection of occurring attacks is of great importance. Considering numerous sensor nodes in the established network, analyzing the network traffic data through traditional methods can become impossible. Analyzing this network traffic in a big data environment is necessary. This study aims to analyze the obtained network traffic dataset in a big data environment and detect attacks in the network using a deep learning algorithm. This study is conducted using PySpark with Apache Spark in the Google Colaboratory (Colab) environment. Keras and Scikit-Learn libraries are utilized in the study. ‘CICIoT2023’ and ‘TON_IoT’ datasets are used for training and testing the model. The features in the datasets are reduced using the correlation method, ensuring the inclusion of significant features in the tests. A hybrid deep learning algorithm is designed using one-dimensional CNN and LSTM. The developed method was compared with ten machine learning and deep learning algorithms. The model’s performance was evaluated using accuracy, precision, recall, and F1 parameters. Following the study, an accuracy rate of 99.995% for binary classification and 99.96% for multiclassification is achieved in the ‘CICIoT2023’ dataset. In the ‘TON_IoT’ dataset, a binary classification success rate of 98.75% is reached.
]]>Electronics doi: 10.3390/electronics13061052
Authors: Grzegorz Góra Maciej Petko Konrad Gac Jakub Górski Joanna Iwaniec Michał Mańka Wojciech Zabierowski
One of the main aspects of the control system development process for direct drives is the selection of the required computational accuracy while establishing its impact on the quality of the control. Understanding this relationship allows designers to consciously determine the system structure at the early stages of controller development, which enables the optimal usage of hardware resources. This paper analyzes the results of experimental research on the influence of computational accuracy on the quality of control of direct drives. During the carried-out research, several vector controllers with different computational precision were implemented using Field-Programmable Gate Arrays (FPGAs). The experiments were carried out on a dedicated research stand for testing direct drives. Test scenarios included position and trajectory monitoring under various torque loads. To assess the control quality, the measures based on the deviations from the value set by the controller were proposed. In this paper, the results of experiments have been presented in the form of values of the measures in relation to the computational accuracy. The obtained results proved that satisfactory drive operation parameters can be obtained despite the relatively low accuracy of calculations in the control algorithm.
]]>Electronics doi: 10.3390/electronics13061051
Authors: Ruxia Yang Hongchao Gao Fangyuan Si Jun Wang
In the context of virtual power plants (VPPs), the one-size-fits-all approach of traditional static desensitization methods proves inadequate due to the diverse and dynamic operational scenarios encountered. These methods fail to provide the necessary flexibility for varying data privacy requirements across different scenarios. To address this shortcoming, our research introduces a dynamic desensitization method specifically designed for VPPs. Leveraging machine learning for adaptive scene recognition, the method adjusts data privacy levels intelligently according to each unique scenario. A novel similarity utility function and a Gaussian processes-based differential privacy algorithm ensure tailored and efficient privacy protection. Experimental results highlight an 87.5% accuracy in scene recognition, validating our method’s capability to adapt to diverse scenarios effectively. This study contributes to the field by providing a nuanced approach to data protection, effectively addressing the specific needs of complex VPP environments.
]]>Electronics doi: 10.3390/electronics13061049
Authors: Kelin Wang Zhiyong Li Chengyi Wang Bing Guo Juntai Li Zhengchao Lv Xiaoling Ding
This thesis introduces a nondestructive inspection and weight grading device for chicken wings to replace the traditional manual grading operation. A two-sided quality nondestructive inspection model of chicken wings based on the YOLO v7-tiny target detection algorithm is designed and deployed in a Jetson Xavier NX embedded platform. An STM32 microcontroller is used as the main control platform, and a wing turning device adapting to the conveyor belt speed, dynamic weighing, and a high-efficiency intelligent grading unit are developed, and the prototype is optimized and verified in experiments. Experiments show that the device can grade four chicken wings per second, with a comprehensive accuracy rate of 98.4%, which is better than the traditional grading methods in terms of efficiency and accuracy.
]]>Electronics doi: 10.3390/electronics13061050
Authors: Jinzhong He Ming Zhang Jian Xu Lina Yu Weijun Li
Convolutional neural network (CNN) hardware acceleration is critical to improve the performance and facilitate the deployment of CNNs in edge applications. Due to its efficiency and simplicity, channel group parallelism has become a popular method for CNN hardware acceleration. However, when processing data involving small channels, there will be a mismatch between feature data and computing units, resulting in a low utilization of the computing units. When processing the middle layer of the convolutional neural network, the mismatch between the feature-usage order and the feature-loading order leads to a low input feature cache hit rate. To address these challenges, this paper proposes an innovative method inspired by data reordering technology, aiming to achieve CNN hardware acceleration that reuses the same multiplier resources. This method focuses on transforming the hardware acceleration process into feature organization, feature block scheduling and allocation, and feature calculation subtasks to ensure the efficient mapping of continuous loading and the calculation of feature data. Specifically, this paper introduces a convolutional algorithm mapping strategy and a configurable vector operation unit to enhance multiplier utilization for different feature map sizes and channel numbers. In addition, an off-chip address mapping and on-chip cache management mechanism is proposed to effectively improve the feature access efficiency and on-chip feature cache hit rate. Furthermore, a configurable feature block scheduling policy is proposed to strike a balance between weight reuse and feature writeback pressure. Experimental results demonstrate the effectiveness of this method. When using 512 multipliers and accelerating VGG16 at 100 MHz, the actual computing performance reaches 102.3 giga operations per second (GOPS). Compared with other CNN hardware acceleration methods, the average computing array utilization is as high as 99.88% and the computing density is higher.
]]>Electronics doi: 10.3390/electronics13061048
Authors: Mislav Matić Mirko Poljak
Hafnium disulfide (HfS2) monolayer is one of the most promising two-dimensional (2D) materials for future nanoscale electronic devices, and patterning it into quasi-one-dimensional HfS2 nanoribbons (HfS2NRs) enables multi-channel architectures for field-effect transistors (FETs). Electronic, transport and ballistic device characteristics are studied for sub-7 nm-wide and ~15 nm-long zigzag HfS2NR FETs using non-equilibrium Green’s functions (NEGF) formalism with density functional theory (DFT) and maximally localized Wannier functions (MLWFs). We provide an in-depth analysis of quantum confinement effects on ON-state performance. We show that bandgap and hole transport mass are immune to downscaling effects, while the ON-state performance is boosted by up to 53% but only in n-type devices. Finally, we demonstrate that HfS2NR FETs can fulfill the industry requirements for future technology nodes, which makes them a promising solution for FET architectures based on multiple nanosheets or nanowires.
]]>Electronics doi: 10.3390/electronics13061047
Authors: Jaime Aranda Victor Guerra Jose Rabadan Rafael Perez-Jimenez
Event cameras are bio-inspired devices that have revolutionized the acquisition of visual information by mimicking the neural architecture of the eye. These cameras respond asynchronously to changes in scene illumination at the pixel level, providing high-precision time information with low latency, typically in the order of microseconds. In this work, we experimentally evaluate an optical camera communication (OCC) link using an LED-based transmitter and an event camera as the receiver. We propose n-pulse modulation to encode data, adapting the system to the specific characteristics and operational principles of event cameras. The proposed scheme significantly reduces the demodulation complexity compared to other alternatives found in the literature. Furthermore, a set of experiments considering different camera bias sensitivities, encoding duty cycles, and LED radiant fluxes were carried out. The results showed that the BER performance was strongly dependent on the received optical power and the bias sensitivity. In addition, duty cycles between 0.3 and 0.7 at a 200 Hz transmission frequency presented the best performance, with a BER below 1.25×10−4, which is under the forward error correction (FEC) limit. This work showcases the cutting-edge capabilities of event-camera-based OCC technology and contributes to the ongoing revolution in optical wireless communication (OWC).
]]>Electronics doi: 10.3390/electronics13061046
Authors: Feng Wang Gang Wang Baoli Lu
In the field of multimodal robotics, achieving comprehensive and accurate perception of the surrounding environment is a highly sought-after objective. However, current methods still have limitations in motion keypoint detection, especially in scenarios involving small target detection and complex scenes. To address these challenges, we propose an innovative approach known as YOLOv8-PoseBoost. This method introduces the Channel Attention Module (CBAM) to enhance the network’s focus on small targets, thereby increasing sensitivity to small target individuals. Additionally, we employ multiple scale detection heads, enabling the algorithm to comprehensively detect individuals of varying sizes in images. The incorporation of cross-level connectivity channels further enhances the fusion of features between shallow and deep networks, reducing the rate of missed detections for small target individuals. We also introduce a Scale Invariant Intersection over Union (SIoU) redefined bounding box regression localization loss function, which accelerates model training convergence and improves detection accuracy. Through a series of experiments, we validate YOLOv8-PoseBoost’s outstanding performance in motion keypoint detection for small targets and complex scenes. This innovative approach provides an effective solution for enhancing the perception and execution capabilities of multimodal robots. It has the potential to drive the development of multimodal robots across various application domains, holding both theoretical and practical significance.
]]>Electronics doi: 10.3390/electronics13061045
Authors: Fabio Cacciotto Alessandro Cannone Emanuele Cassarà Santi Agatino Rizzo
This paper presents a high-efficiency GaN-based 65 W Quasi-Resonant (QR) Flyback converter. The converter is characterized by a wide input voltage range and a variable output voltage, and it is designed as a Switch Mode Power Supply (SMPS) for high power density USP-Power Delivery (USB-PD) applications. To increase the efficiency and power density, a regenerative snubber clamp solution has been used to limit the excursion of the drain voltage during the power switch turn-off. The activity involved the modeling of the converter, the sizing of the regenerative snubber, and the design of the flyback transformer. Furthermore, a dedicated test application board was used to verify the effectiveness of the solution. The results were compared with those obtained using a flyback converter with an RCD snubber.
]]>Electronics doi: 10.3390/electronics13061044
Authors: S. M. Mizanoor Rahman
The objective was to investigate the impacts of the robot’s dynamic affective expressions in task-related scenarios on human–robot collaboration (HRC) and performance in human–robot collaborative assembly tasks in flexible manufacturing. A human–robot hybrid cell was developed to facilitate a human co-worker and a robot to collaborate to assemble a few parts into a final product. The collaborative robot was a humanoid manufacturing robot with the ability to display its affective states due to changes in task scenarios on its face. The assembly task was divided into several subtasks, and based on an optimization strategy, the subtasks were optimally allocated to the human and the robot. A computational model of the robot’s affective states was derived inspired by that of humans following the biomimetic approach, and an affect-based motion planning strategy for the robot was proposed to enable the robot to adjust its motions and behaviors with task situations and communicate (inform) the situations to the human co-worker through affective expressions. The HRC and the assembly performance for the affect-based motion planning were experimentally evaluated based on a comprehensive evaluation scheme and were compared with two alternative conditions: (i) motion planning that did not display affective states, and (ii) motion planning that displayed text messages instead of displaying affective states to communicate the situations to the human co-worker. The results clearly showed that the dynamic affect-based motion planning produced significantly better HRC and assembly performance than that produced by motion planning associated with the display of no affective states or text messages. The results encouraged employing manufacturing robots with dynamic affective expressions to collaborate with humans in flexible assembly in manufacturing to improve HRC and assembly performance.
]]>Electronics doi: 10.3390/electronics13061043
Authors: Ting Yuan Chi Zhang Feng Yi Pingping Lv Meitong Zhang Shupei Li
In this paper, an adaptive trajectory tracking control method combining proportional–integral–derivative (PID) control, Radial Basis Function neural network (RBFNN)-based integral sliding mode control (ISMC), and feedforward control, i.e., the PIDFF-ISMC method, is proposed. The PIDFF-ISMC method aims to deal with the dynamic uncertainties, disturbances, and slow response in lower limb exoskeleton robot systems. Firstly, the Lagrange function is utilized to establish dynamic models that include frictional force and unmodeled dynamics. Secondly, the feedback controller is composed of PID and RBFNN-based ISMC to improve tracking performance and decrease the chattering phenomenon. The feedforward controller is adopted to reduce the response time by employing inverse dynamic models. Finally, the Lyapunov function proves the stability of the proposed control method. The experimental results show that the proposed control method can effectively reduce the trajectory tracking error and response time at two different speeds while alleviating control input chattering.
]]>Electronics doi: 10.3390/electronics13061042
Authors: Jingjing Huang Hanbin Wang Guokun Ma Houzhao Wan Yiheng Rao Liangping Shen Hao Wang
Binary metal oxide materials, such as nickel oxide, are widely used in flexible resistive variable memory devices due to advantages such as their easily controllable material composition, simple structural composition, and good compatibility between manufacturing processes and complementary metal oxide processes. In this work, a solution combustion method was employed to prepare NiOx thin films for use as a resistive switching layer of resistance random-access memory. The formation temperature of the NiOx layer in the RRAM device was kept as low as 175 °C, making the device compatible with flexible substrates. With polyethylene naphthalenediate as the substrate, the NiOx-based RRAM exhibited obvious bipolar resistance-switching properties, superb bending resistance, and good stability. Through theoretical fitting and structural characterization, the conduction mechanisms were attributed to the combination of the space-charge-limited current and Ohmic mechanisms, while the valence change mechanism was determined to be the resistive switching mechanism. This study demonstrates a low-temperature and scalable approach to constructing NiOx-based RRAM devices on flexible substrates.
]]>Electronics doi: 10.3390/electronics13061041
Authors: Li Sun Xin Wang Chenglian Ma
To solve the problems of large switching losses and the need for large-capacity electrolytic capacitances in three-phase DC/AC on-board chargers for vehicle-to-grid (V2G) applications, this paper proposes a single-stage bidirectional high-frequency isolated converter that eliminates the need for large-capacity capacitances. Combined with the proposed modulation scheme, it can theoretically reduce the switching loss by about two-thirds with the three-phase converter compared with the conventional modulation scheme, improving the converter’s operating efficiency and power density. Firstly, based on the characteristics of the proposed topology, a hybrid modulation scheme is proposed, which combines a phase-shift modulation scheme based on double modulation waves and a sawtooth carrier with a 1/3 modulation scheme, and the theoretical feasibility of the hybrid modulation scheme is verified using a mathematical modeling equation. Secondly, this paper provides a detailed analysis of the four operating modes of the two full-bridge circuits and the commutation process of the three-phase converter within 1/6 of the fundamental frequency cycle (P1 modulation interval). Then, the control strategy is given for the constant-current and constant-voltage charging and constant-current discharging for electric vehicle batteries. Finally, simulation results verify the correctness of the proposed topology and modulation scheme in vehicle–grid interaction.
]]>Electronics doi: 10.3390/electronics13061040
Authors: Samaneh Bidabadi Messaoud Ahmed Ouameur Miloud Bagaa Daniel Massicotte
The pursuit of energy-efficient solutions in the context of reconfigurable intelligent surface (RIS)-assisted wireless networks has become imperative and transformative. This paper investigates the integration of RIS into an orthogonal frequency-division multiple access (OFDMA) framework for multi-user downlink communication systems. We address the challenge of jointly optimizing RIS reflection coefficients alongside OFDMA frequency and power allocations, with the aim of maximizing energy efficiency. This optimization is subject to specific quality-of-service (QoS) requirements for each user equipment (UE) and a constraint on transmission power and the RIS phase shift matrix. To address this complex optimization problem, we propose a novel practical and low-complexity approach that is based on optimizing a computationally efficient and numerically tractable lower bound on energy efficiency. The numerical results highlight the effectiveness of our approach, demonstrating a substantial increase in energy efficiency compared to scenarios without RIS, with random RIS integration, and with the scheme using the Genetic Algorithm (GA).
]]>Electronics doi: 10.3390/electronics13061039
Authors: Quang-Huy Do Rémi Antony Bernard Ratier Johann Bouclé
Layered halide perovskites have emerged as a promising contender in solid-state lighting; however, the fabrication of perovskite light-emitting devices in laboratories usually experiences low device-to-device reproducibility since perovskite crystallization is highly sensitive to ambient conditions. Although device processing inside gloveboxes is primarily used to reduce the influence of oxygen and moisture, several extraneous variables, including thermal fluctuations in the inert atmosphere or contaminations from residual solvents, can destabilize the crystallization process and alter the properties of the emissive layers. Here, we examine typical experimental configurations used in research laboratories to deposit layered perovskite films in inert atmospheres and discuss their crucial influences on the formation of polycrystalline thin films. Our results demonstrate that fluctuations in the glovebox properties (concentrations of residual O2 and H2O or solvent traces), even in very short timescales, can negatively impact the consistency of the perovskite film formation, while thermal variation plays a relatively minor role in this phenomenon. Furthermore, the careful storage of chemical species inside the workstation is critical for reproducing high-quality perovskite layers. Consequently, when applying our most controlled environment for perovskite deposition, the photoluminescence lifetime of perovskite thin films shows a standard deviation of only 3%, whereas the reference set-up yields a 15% standard deviation. Regarding complete perovskite light-emitting diodes, the uncertainties in statistical luminance and EQE data are significantly reduced from 230% and 140% to 38% and 42%, respectively.
]]>Electronics doi: 10.3390/electronics13061038
Authors: Jiajun Li Huabo Shi Baohong Li Qin Jiang Yue Yin Yingmin Zhang Tianqi Liu Chang Nie
The interline power flow controller (IPFC) based on a modular multilevel converter with a half-bridge configuration can control the active and reactive power flows of multiple alternating current (AC) lines. However, it forms a multiterminal system on the direct current (DC) side, which leads to DC faults. To reduce the protection and clearance requirements on the DC side of IPFCs, this paper proposes a hybrid current limiter topology suitable for generating a DC-side fault ride-through scheme. The current limiter employs a low-loss branch in steady-state conditions; when the fault occurs, a commutation capacitor and controllable power electronic devices are used to transfer the fault current to the current-limiting branch. To clarify the operating principles of the current limiter, the working states of each stage and electrical stress of each device are analyzed. Different components with varying limiter parameters are also discussed, and optimal parameters to achieve the best limitation effect are discussed. PSCAD simulations show that the proposed limiter can limit the overcurrent effectively, and DC-side fault clearance can be achieved easily with this fault ride-through strategy.
]]>Electronics doi: 10.3390/electronics13061037
Authors: Xin Ning Yuhang Li Ziwei Feng Jinhua Liu Youdong Ding
Video frame interpolation aims to generate intermediate frames in a video to showcase finer details. However, most methods are only trained and tested on low-resolution datasets, lacking research on 4K video frame interpolation problems. This limitation makes it challenging to handle high-frame-rate video processing in real-world scenarios. In this paper, we propose a 4K video dataset at 120 fps, named UHD4K120FPS, which contains large motion. We also propose a novel framework for solving the 4K video frame interpolation task, based on a multi-scale pyramid network structure. We introduce self-attention to capture long-range dependencies and self-similarities in pixel space, which overcomes the limitations of convolutional operations. To reduce computational cost, we use a simple mapping-based approach to lighten self-attention, while still allowing for content-aware aggregation weights. Through extensive quantitative and qualitative experiments, we demonstrate the excellent performance achieved by our proposed model on the UHD4K120FPS dataset, as well as illustrate the effectiveness of our method for 4K video frame interpolation. In addition, we evaluate the robustness of the model on low-resolution benchmark datasets.
]]>Electronics doi: 10.3390/electronics13061036
Authors: Tahsin Koroglu Elanur Ekici M. Mustafa Savrun
This paper introduces a novel five-port, three-input, dual-output isolated bidirectional dc-dc converter (FPIBC) topology with an effective controller for power-sharing and voltage-balancing in bipolar dc microgrids (BPDCMGs). The proposed converter acts as the interface for the integration of a hybrid generation system comprising a solid oxide fuel cell (SOFC), a photovoltaic (PV) system, and a battery into BPDCMGs. It employs a reduced number of circuit elements compared with similar multiport converter topologies suggested for BPDCMG applications. Symmetrical bipolar output voltages are ensured by a voltage-balancing circuit composed of a fully controlled switch and four diodes. The FPIBC is equipped with different controllers for output voltage regulation and balancing, power sharing, maximum power point tracking of the PV, the optimum operating region of the SOFC, and constant-current, constant-voltage charging of the battery. To verify the viability and effectiveness of the proposed system, a simulation model was developed with a 4.2 kW SOFC, a 3.7 kW PV, and a 140 V 10.8 Ah battery in MATLAB/Simulink. The performance of the FPIBC was evaluated through extensive case studies with different operational modes, including battery charge/discharge states and SOFC and PV parameter changes under varying load conditions. In addition, the proposed system was examined using a daily dynamic load profile. According to the simulation results, a peak efficiency of 97.28% is achieved and the voltage imbalance between the output ports is maintained below 0.5%. It is shown that the FPIBC has advantages over previous converters in terms of the number of ports, number of circuit elements, bipolar output voltage, bidirectional power flow, and efficiency.
]]>Electronics doi: 10.3390/electronics13061035
Authors: Mário Marques da Silva Ali Gashtasbi Rui Dinis Gelson Pembele Américo Correia João Guerreiro
One of the key technologies of 6G communications relies on large intelligent surfaces (LIS), which can be viewed as a near-field beamformer that is supportive of extremely high symbol rates and enables a high level of interference avoidance. This article focuses on LIS systems, analysing the impact of the use of a whole LIS system or a subset of an antenna array. We analyse an LIS system associated with a single carrier with frequency domain equalization (SC-FDE), and with different receiver types of varying complexities. Because it is a function of the number of antennas, the computational complexity decreases when antenna elements that are closer to the user equipment are used instead of the whole LIS. Moreover, with a partial LIS, a reduction of energy consumption is achieved, and mitigation of the interference levels is obtained, allowing a performance very close to that obtained with the whole LIS system.
]]>Electronics doi: 10.3390/electronics13061034
Authors: Oskar Zetterstrom Nelson J. G. Fonseca Oscar Quevedo-Teruel
Beam-switching antennas based on quasi-optical beamformers can provide cost-effective solutions for high-frequency communication applications. Here, we propose a wide-angle beam-switching planar lens antenna based on the recently presented virtual image lens. The antenna operates from 24 to 28 GHz and produces a beam that can be steered in a 100-degrees range in one plane with less than 2 dB simulated gain variation over the angular range and operational band. The performance of the presented antenna is similar to reported lens antennas with stable gain, but the proposed lens requires a smaller refractive index range to be realized, which alleviates the manufacturing.
]]>Electronics doi: 10.3390/electronics13061033
Authors: Hossein Ahmadi Luca Mesin
In the evolving field of Brain–Computer Interfaces (BCIs), accurately classifying Electroencephalography (EEG) signals for Motor Imagery (MI) tasks is challenging. We introduce the Correlation-Optimized Weighted Stacking Ensemble (COWSE) model, an innovative ensemble learning framework designed to improve MI EEG signal classification. The COWSE model integrates sixteen machine learning classifiers through a weighted stacking approach, optimizing performance by balancing the strengths and weaknesses of each classifier based on error correlation analysis and performance metrics evaluation across benchmark datasets. The COWSE model’s development involves selecting base classifiers, dynamically assigning weights according to performance, and employing a meta-classifier trained on these weighted predictions. Testing on the BNCI2014-002 dataset, the COWSE model achieved classification accuracy exceeding 98.16%, marking a significant advancement in MI EEG classification. This study highlights the potential of integrating multiple machine learning classifiers to address the complex challenges of EEG signal classification. By achieving new benchmarks and showcasing enhanced classification capabilities, the COWSE model contributes significantly to BCI research, encouraging further exploration into advanced ensemble learning strategies.
]]>Electronics doi: 10.3390/electronics13061032
Authors: Ki-Seung Lee
Variation in lighting conditions is a major cause of performance degradation in pattern recognition when using optical imaging. In this study, infrared (IR) and depth images were considered as possible robust alternatives against variations in illumination, particularly for improving the performance of automatic lip-reading. The variations due to lighting conditions were quantitatively analyzed for optical, IR, and depth images. Then, deep neural network (DNN)-based lip-reading rules were built for each image modality. Speech recognition techniques based on IR or depth imaging required an additional light source that emitted light in the IR range, along with a special camera. To mitigate this problem, we propose a method that does not use an IR/depth image directly, but instead estimates images based on the optical RGB image. To this end, a modified U-net was adopted to estimate the IR/depth image from an optical RGB image. The results show that the IR and depth images were rarely affected by the lighting conditions. The recognition rates for the optical, IR, and depth images were 48.29%, 95.76%, and 92.34%, respectively, under various lighting conditions. Using the estimated IR and depth images, the recognition rates were 89.35% and 80.42%, respectively. This was significantly higher than for the optical RGB images.
]]>Electronics doi: 10.3390/electronics13061031
Authors: Rawan Bukhowah Ahmed Aljughaiman M. M. Hafizur Rahman
The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms.
]]>Electronics doi: 10.3390/electronics13061030
Authors: Jaehyuk Lee Jinseo Yun Kyungroul Lee
Ransomware, which emerged in 1989, has evolved to the present in numerous variants and new forms. For this reason, serious damage caused by ransomware has occurred not only within our country but around the world, and, according to the analysis of ransomware trends, ransomware poses an ongoing and significant threat, with major damage expected to continue to occur in the future. To address this problem, various approaches to detect ransomware have been explored, with a recent focus on file entropy estimation methods. These methods exploit the characteristic increase in file entropy that is caused by ransomware encryption. In response, a method was developed to neutralize entropy-based ransomware detection technology by manipulating entropy using encoding methods from the attacker’s perspective. Consequently, from the defender’s standpoint, countermeasures are essential to minimize the damage caused by ransomware. Therefore, this article proposes a methodology that utilizes diverse machine learning models such as K-Nearest Neighbors (KNN), logistic regression, decision tree, random forest, gradient boosting, support vector machine (SVM), and multi-layer perception (MLP) to detect files infected with ransomware. The experimental results demonstrate empirically that files infected with ransomware can be detected with approximately 98% accuracy, and the results of this research are expected to provide valuable information for developing countermeasures against various ransomware detection technologies.
]]>Electronics doi: 10.3390/electronics13061029
Authors: Xusheng Tang Mingfeng Wei
Radiation source signal sorting in complex environments is currently a hot issue in the field of electronic countermeasures. The pulse repetition interval (PRI) can provide stable and obvious parametric features in radiation source identification, which is an important parameter relying on the signal sorting problem. To solve the problem linked to the difficulties in sorting the PRI in complex environments using the traditional method, a signal sorting method based on a parallel denoising autoencoder is proposed. This method implements the binarized preprocessing of known time-of-arrival (TOA) sequences and then constructs multiple parallel denoising autoencoder models using fully connected layers to achieve the simultaneous sorting of multiple signal types in the overlapping signals. The experimental results show that this method maintains high precision in scenarios prone to large error and can efficiently filter out noise and highlight the original features of the signal. In addition, the present model maintains its performance and some robustness in the sorting of different signal types. Compared with the traditional algorithm, this method improves the precision of sorting. The algorithm presented in this study still maintains above 90% precision when the pulse loss rate reaches 50%.
]]>Electronics doi: 10.3390/electronics13061028
Authors: Dong Mao Qiongqian Yang Hongkai Wang Zuge Chen Chen Li Yubo Song Zhongyuan Qin
Federated learning (FL) is increasingly challenged by security and privacy concerns, particularly vulnerabilities exposed by malicious participants. There remains a gap in effectively countering threats such as model inversion and poisoning attacks in existing research. To address these challenges, this paper proposes the Effective Private-Protected Federated Learning Aggregation Algorithm (EPFed), a framework that utilizes a blockchain platform, homomorphic encryption, and secret sharing to fortify the data privacy and computational efficiency in a federated learning environment. EPFed works by establishing “trust groups” through the unique integration of a Chinese Remainder Theorem-based secret sharing scheme with Paillier homomorphic encryption, streamlining secure model parameter exchange and aggregation while minimizing the computational load. Our performance-driven aggregation strategy leverages local performance metrics to safeguard against malicious contributions, ensuring both the integrity and efficiency of the learning process. The evaluations demonstrate that EPFed achieves a remarkable accuracy rate of 92.5%, thereby confirming the advanced nature of the proposed solution in addressing the pressing challenges of FL.
]]>Electronics doi: 10.3390/electronics13061027
Authors: Yuxiao Deng Jingyu Liu Yang Zhou
With the development of intelligent vehicles, the vehicle image processing system has put forward increasing demand for computing resource utilization efficiency and real-time processing. However, the traditional information processing method that binds software and hardware severely restricts the efficient use of image processing resources. In order to solve this problem, this paper proposes a resource reconstruction scheme based on a load balancing strategy, which can realize unified management and dynamic allocation of image processing resources by establishing a system resource view. Then, this paper builds a physical verification platform and constructs a comparative verification experiment to verify the effectiveness of the resource reconstruction scheme. Experimental results prove that this resource reconstruction scheme can effectively improve the resource utilization efficiency of multi-core digital signal processing (DSP), realize software-defined hardware functions, and optimize the real-time performance of parallel processing.
]]>Electronics doi: 10.3390/electronics13061026
Authors: Wenlong Zhu Changli Zhou Linmei Jiang
With the rapid popularization of current Internet of Things (IoT) technology and 5G networks, as well as the continuous updating of new service lifestyles and businesses, the era of big data processing for the IoT has arrived. However, centralizing all data for processing in the cloud can lead to issues such as communication latency and privacy breaches. To solve these problems, edge computing, as a new network architecture close to terminal data sources and supporting low latency services, has gradually emerged. In this context, cloud edge collaborative computing has become an important network architecture. With the changing security requirements and communication methods of cloud edge collaborative network architecture, traditional authentication key agreement protocols are no longer applicable. Therefore, a new IoT authentication and key agreement protocol needs to be designed to solve this problem. This study proposes an IoT accessible solution for cloud edge collaboration. This scheme adopts a chaotic mapping algorithm to achieve efficient authentication. It ensures the anonymity and untraceability of users. Following this, we conducted strict security verification using BAN logic and Scyther tools. Through experimental comparative analysis, the research results show that the protocol performs better than other schemes while ensuring security. This indicates that the protocol can achieve efficient authentication and key negotiation in cloud edge collaborative network architecture, providing a secure and reliable solution for the accessibility of the IoT.
]]>Electronics doi: 10.3390/electronics13061024
Authors: Jinyang Ren Peihan Qi Chenxi Li Panpan Zhu Zan Li
To address the threat posed by unknown signal sources within Mobile Crowd Sensing (MCS) systems to system stability and to realize effective localization of unknown sources in long-distance scenarios, this paper proposes a unilateral branch ratio decision algorithm (UBRD). This approach addresses the inadequacies of traditional sparse localization algorithms in long-distance positioning by introducing a time–frequency domain composite block sparse localization model. Given the complexity of localizing unknown sources, a unilateral branch ratio decision scheme is devised. This scheme derives decision thresholds through the statistical characteristics of branch residual ratios, enabling adaptive control over iterative processes and facilitating multisource localization under conditions of remote blind sparsity. Simulation results indicate that the proposed model and algorithm, compared to classic sparse localization schemes, are more suitable for long-distance localization scenarios, demonstrating robust performance in complex situations like blind sparsity, thereby offering broader scenario adaptability.
]]>Electronics doi: 10.3390/electronics13061025
Authors: Vishnu Pendyala Hyungkyun Kim
Machine learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, this work provides insights into the models’ workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and, at times, unsound ranking of the features to make the predictions. This paper therefore argues that it is important for research in applied machine learning to provide insights into the explainability of models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare.
]]>Electronics doi: 10.3390/electronics13061023
Authors: Junping Xu Sixuan Liu Wei Yang Meichen Fang Younghwan Pan
With the rise of the metaverse, digital transformation is profoundly affecting the field of art exhibitions. Museums and galleries are actively adopting metaverse technologies to present artworks through virtual platforms, providing audiences with novel opportunities for immersive engagement and art experiences and shaping high-quality user experiences. However, the factors influencing user engagement in the metaverse art exhibition platform (MeAEP) remain unclear in the current research. This research combines the information systems success model (ISSM) and the hedonic motivation system adoption model (HMSAM) to construct a theoretical model that provides insights into the factors influencing MeAEP users’ intention to engage and their immersion behavior, with a focus on the sustainability of the art exhibition. We quantitatively analyzed 370 users that experienced MeAEP and analyzed the data and measurement model using SPSS 27 and partial least squares structural equation modeling (PLS-SEM). The results showed that information quality (IQ), system quality (SQ), and perceived ease of use (PEOU) significantly and positively influenced perceived usefulness (PU), curiosity (CUR), joy (JOY), and control (CON). PU, JOY, and CON have a positive and significant effect on Immersion (IM). Finally, PU, CUR, JOY, and CON had a positive effect on behavioral intention (BI). In conclusion, only one of the twenty hypotheses was not supported. The research findings not only enrich the academic and managerial theories related to the metaverse and art exhibition platforms, but also provide practical insights for administrators, developers, and MeAEP designers to create higher-quality and more immersive art content, as well as provide constructive ideas for the sustainability of art exhibitions to further enhance user experience.
]]>Electronics doi: 10.3390/electronics13061022
Authors: Dengyu Ran Xiao Chen Lei Song
Programmable networks comprise heterogeneous network devices based on both hardware and software. Hardware devices provide superior bandwidth and low latency but encounter challenges in managing large table entries. Conversely, software devices offer abundant flow tables but have a limited forwarding capacity. To overcome this limitation, some commercial switches offer implementations that combine both hardware and software devices. In this context, this paper presents the Composite Pipeline (ComPipe), an algorithm for high-performance and high-precision flow placement and measurement. ComPipe utilizes a multi-level hashing algorithm for the real-time identification of heavy hitters, incorporates a unique flow eviction strategy, and is implemented on commercial programmable hardware. For non-heavy flows, ComPipe employs sketch structures to accomplish a high-performance flow synopsis within limited memory constraints. This design allows to replace flow rules entirely in the data plane, ensuring the timely detection and offloading of heavy-hitter flows, and offering a unified interface to the controller. The ComPipe prototype has been implemented in both testbed and simulation environments. The results indicate that ComPipe is an effective solution for dynamic flow placement in programmable networks, distinguished by its low cost, high performance, and high accuracy.
]]>Electronics doi: 10.3390/electronics13061021
Authors: Yaru Ding Zeping Weng Zhangsheng Lan Chu Yan Daolin Cai Yiming Qu Yi Zhao
This work experimentally investigated the wake-up behaviors of hafnium oxide-based ferroelectric capacitors by manipulating the interval time between each characterization cycle. Both Positive-Up–Negative-Down (PUND) and Negative-Down–Positive-Up (NDPU) waveforms were used as the stress and measurement waveforms in the experiments. It was found that the imprint occurs as the total interval time increases to a several-seconds level. However, this only affects the remnant polarization (PR) of ferroelectric capacitors when stressed by NDPU waveforms, since the voltage amplitude saturates under the PUND stress conditions and does not influence the PR. The wake-up behavior has been proved to be caused by the defects redistribution during electrical cycling. Notably, when using PUND waveforms, the change in the interval time can result in different increase rates of PR, indicating the possibility of recovery during the intervals. This recovery leads to a slower wake-up when cycling with a longer interval time. Moreover, it is observed that this PR recovery could reach saturation after several seconds of the interval time. This comprehensive investigation of wake-up and imprint behaviors can provide new insights to evaluate and enhance the reliability of ferroelectric memories.
]]>Electronics doi: 10.3390/electronics13061020
Authors: Yeeun Kim Seul Ki Hong Jong Kyung Park
This paper presents an innovative approach to alleviate Z-interference in 3D NAND flash memory by proposing an optimized confined nitride trap layer structure. Z-interference poses a significant challenge in 3D NAND flash memory, especially with the reduction in cell spacing to accommodate an increased number of vertically stacked 3D NAND flash memories. While the confined nitride trap layer device designed for complete isolation of the trapping layer in three dimensions effectively reduces Z-interference, the results showed substantial variations based on the confined structure. To clarify this issue, we compared three distinct confined nitride trap layer structures and investigated their impact on Z-interference. Our findings indicate that the rectangle structure exhibited the most significant mitigation, implying that differences in the electric field applied to the poly silicon channel, which is influenced by the structure, and the increase in effective channel length are effective strategies for alleviating Z-interference. The proposed structure undergoes a comprehensive examination through technology computer-aided design (TCAD) simulations. Additionally, we introduce a practical process flow designed to minimize Z-interference.
]]>Electronics doi: 10.3390/electronics13061019
Authors: Zhe Qu Lizhen Cui Xiaohui Yang
Ensuring safety while driving relies heavily on normal driving behavior, making the timely detection of dangerous driving patterns crucial. In this paper, an Hourglass Attention ResNet Network (HAR-Net) is proposed to detect dangerous driving behavior. Uniquely, we separately input optical flow data, RGB data, and RGBD data into the network for spatial–temporal fusion. In the spatial fusion part, we combine ResNet-50 and the hourglass network as the backbone of CenterNet. To improve the accuracy, we add the attention mechanism to the network and integrate center loss into the original Softmax loss. Additionally, a dangerous driving behavior dataset is constructed to evaluate the proposed model. Through ablation and comparative studies, we demonstrate the efficacy of each HAR-Net component. Notably, HAR-Net achieves a mean average precision of 98.84% on our dataset, surpassing other state-of-the-art networks for detecting distracted driving behaviors.
]]>Electronics doi: 10.3390/electronics13061018
Authors: Lianhai Wang Chenxi Guan
In the original publication [...]
]]>Electronics doi: 10.3390/electronics13061017
Authors: Lingling Zi Xin Cong
This study aims to give a comprehensive overview of the application of the metaverse in educational evaluation. First, we characterize the metaverse and illustrate how it can support educational evaluation from the perspectives of virtual reality, augmented reality, and blockchain. Then, we outline the metaverse exploration framework and summarize its technical advantages. Based on this, we propose a metaverse-based implementation scheme to address the issues of reliability, accuracy, and credibility in educational evaluation. Finally, we show its implementation difficulties, performance evaluation, and future work. This proposed scheme opens up new research directions for the reform of educational evaluation while expanding the potential and reach of metaverse applications in education. We think that this study can help researchers in building an ecosystem for educational evaluation that is trustworthy, equitable, and legitimate.
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