 Subjects -> ELECTRONICS (Total: 207 journals)
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- Electronics, Vol. 12, Pages 4888: Characterization Technique for a Doppler
Radar Occupancy Sensor
Authors: Avon Whitworth, Amy Droitcour, Chenyan Song, Olga Boric-Lubecke, Victor Lubecke
First page: 4888
Abstract: Occupancy sensors are electronic devices used to detect the presence of people in monitored areas, and the output of these sensors can be used to optimize lighting control, heating and ventilation control, and real-estate utilization. Testing methods already exist for certain types of occupancy sensors (e.g., passive infrared) to evaluate their relative performance, allowing manufacturers to report coverage patterns for different types of motion. However, the existing published techniques are mostly tailored for passive-infrared sensors and therefore limited to evaluation of large motions, such as walking and hand movement. Here we define a characterization technique for a Doppler radar occupancy sensor based on detecting a small motion representing human breathing, using a well-defined readily reproducible target. The presented technique specifically provides a robust testing method for a single-channel continuous wave Doppler-radar based occupancy sensor, which has variation in sensitivity within each wavelength of range. By comparison with test data taken from a human subject, we demonstrate that the mobile target provides a reproducible alternative for a human target that better accounts for the impact of sensor placement. This characterization technique enables generation of coverage patterns for breathing motion for single-channel continuous wave Doppler radar-based occupancy sensors.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244888
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4889: Architecture for Smart Buildings Based
on Fuzzy Logic and the OpenFog Standard
Authors: Imanol Martín Toral, Isidro Calvo, Jani Xenakis, Eneko Artetxe, Oscar Barambones
First page: 4889
Abstract: The combination of Artificial Intelligence and IoT technologies, the so-called AIoT, is expected to contribute to the sustainability of public and private buildings, particularly in terms of energy management, indoor comfort, as well as in safety and security for the occupants. However, IoT systems deployed on modern buildings may generate big amounts of data that cannot be efficiently analyzed and stored in the Cloud. Fog computing has proven to be a suitable paradigm for distributing computing, storage control, and networking functions closer to the edge of the network along the Cloud-to-Things continuum, improving the efficiency of the IoT applications. Unfortunately, it can be complex to integrate all components to create interoperable AIoT applications. For this reason, it is necessary to introduce interoperable architectures, based on standard and universal frameworks, to distribute consistently the resources and the services of AIoT applications for smart buildings. Thus, the rationale for this study stems from the pressing need to introduce complex computing algorithms aimed at improving indoor comfort, safety, and environmental conditions while optimizing energy consumption in public and private buildings. This article proposes an open multi-layer architecture aimed at smart buildings based on a standard framework, the OpenFog Reference Architecture (IEEE 1934–2018 standard). The proposed architecture was validated experimentally at the Faculty of Engineering of Vitoria-Gasteiz to improve indoor environmental quality using Fuzzy logic. Experimental results proved the viability and scalability of the proposed architecture.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244889
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4890: Harmonic Injection Control of Permanent
Magnet Synchronous Motor Based on Fading Memory Kalman Filtering
Authors: Shenhui Du, Weisong Sun, Yao Wang, Meng Wang, Hongjie Zhang
First page: 4890
Abstract: In order to reduce the harm of harmonic disturbances of the permanent magnet synchronous motor (PMSM) control system to the drive and improve the accuracy of the control system, a harmonic injection control method based on asymptotic fading memory Kalman filtering is proposed. Compared with the traditional harmonic injection method, this method reduces the torque pulsation of the PMSM, converges faster, and realizes fast stabilization of the control system. In order to improve the control accuracy of the system, extended Kalman filtering is used to estimate the mechanical angular velocity and optimize the harmonic extraction process to reduce the interference of noise signals in the extraction process. At the same time, a fading memory factor is introduced to replace the fixed gain in the Kalman filter, which can correct the system error and effectively prevent the filtering dispersion, thus enhancing the system’s stability. Finally, the system is simulated and experimentally analyzed, and the results show that the method can improve the dynamic response speed, stability, and control accuracy of the system compared with the traditional harmonic injection method.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244890
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4891: Design of Orbital Angular Momentum
Antenna Array for Generating High-Order OAM Modes
Authors: Jiaxin Song, Song Gao, Jingtong Lu, Shuai Zhang, Zhiyuan Ren, Jianchun Xu
First page: 4891
Abstract: Orbital angular momentum (OAM) modes can offer high density and high-capacity communication. The traditional phased array antenna can only produce a limited number of OAM beam modes l, usually less than half of the number of array elements (N): −N/2 < lmax < N/2. An OAM antenna array for generating high-order OAM modes is proposed in this letter. The proposed antenna array consists of a ring patch antenna that can generate vortex waves with OAM mode l = 1 or −1 and a phase-shifting feeding network. By adding different feed excitation signals to each element, the generated beam carries a higher-order mode: l = N or −N, breaking the previous limitations. Near-field measurements were conducted on antenna arrays composed of 3, 4, and 5 elements, revealing a high degree of correspondence between their phase distribution and radiation patterns with numerical simulation results. This alignment further substantiates the practical efficacy of this approach in significantly enhancing the generation of high-order OAM modes within antenna arrays. This advancement improves component utilization efficiency, reduces system complexity, and meets the high demands for spectral resources and channel capacity in future communication applications.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244891
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4892: Dual-Stage Attribute Embedding and
Modality Consistency Learning-Based Visible–Infrared Person
Re-Identification
Authors: Zhuxuan Cheng, Huijie Fan, Qiang Wang, Shiben Liu, Yandong Tang
First page: 4892
Abstract: Visible–infrared person re-identification (VI-ReID) is an emerging technology for realizing all-weather smart surveillance systems. To address the problem of pedestrian discriminative information being difficult to obtain and easy to lose, as well as the wide modality difference in the VI-ReID task, in this paper we propose a two-stage attribute embedding and modality consistency learning-based VI-ReID method. First, the attribute information embedding module introduces the fine-grained pedestrian information in the attribute label into the transformer backbone, enabling the backbone to extract identity-discriminative pedestrian features. After obtaining the pedestrian features, the attribute embedding enhancement module is utilized to realize the second-stage attribute information embedding, which reduces the adverse effect of losing the person discriminative information due to the deepening of network. Finally, the modality consistency learning loss is designed for constraining the network to mine the consistency information between two modalities in order to reduce the impact of modality difference on the recognition results. The results show that our method reaches 74.57% mAP on the SYSU-MM01 dataset in All Search mode and 87.02% mAP on the RegDB dataset in IR-to-VIS mode, with a performance improvement of 6.00% and 2.56%, respectively, proving that our proposed method is able to reach optimal performance compared to existing state-of-the-art methods.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244892
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4893: Modular Open-Source Design of Pyrolysis
Reactor Monitoring and Control Electronics
Authors: Finn K. Hafting, Daniel Kulas, Etienne Michels, Sarvada Chipkar, Stefan Wisniewski, David Shonnard, Joshua M. Pearce
First page: 4893
Abstract: Industrial pilot projects often rely on proprietary and expensive electronic hardware to control and monitor experiments. This raises costs and retards innovation. Open-source hardware tools exist for implementing these processes individually; however, they are not easily integrated with other designs. The Broadly Reconfigurable and Expandable Automation Device (BREAD) is a framework that provides many open-source devices which can be connected to create more complex data acquisition and control systems. This article explores the feasibility of using BREAD plug-and-play open hardware to quickly design and test monitoring and control electronics for an industrial materials processing prototype pyrolysis reactor. Generally, pilot-scale pyrolysis plants are expensive custom designed systems. The plug-and-play prototype approach was first tested by connecting it to the pyrolysis reactor and ensuring that it can measure temperature and actuate heaters and a stirring motor. Next, a single circuit board system was created and tested using the designs from the BREAD prototype to reduce the number of microcontrollers required. Both open-source control systems were capable of reliably running the pyrolysis reactor continuously, achieving equivalent performance to a state-of-the-art commercial controller with a ten-fold reduction in the overall cost of control. Open-source, plug-and-play hardware provides a reliable avenue for researchers to quickly develop data acquisition and control electronics for industrial-scale experiments.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244893
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4894: Securing Big Data Exchange: An
Integrated Blockchain Framework for Full-Lifecycle Data Trading with Trust
and Dispute Resolution
Authors: Chuangming Zhou, Zhou Yang, Shaohua Yue, Bona Xuan, Xi Wang
First page: 4894
Abstract: In the era of big data, facilitating efficient data flow is of paramount importance. Governments and enterprises worldwide have been investing in the big data industry, promoting data sharing and trading. However, existing data trading platforms often suffer from issues like privacy breaches, single points of failure, data tampering, and non-transparent transactions due to their reliance on centralized servers. To address these challenges, blockchain-based big data transaction models have been proposed. However, these models often lack system integrity and fail to fully meet user requirements while ensuring adequate security. To overcome these limitations, this paper presents an Ethereum-based big data trading model that establishes a comprehensive and secure trading system. The model aims to provide users with more convenient, secure, and professional services. Through the utilization of smart contracts, users can efficiently match data and negotiate prices online while ensuring secure data delivery through encryption technologies. Additionally, the model introduces a trusted third-party entity that offers professional data evaluation services and actively safeguards user data ownership in the event of disputes. The implementation of the model includes the development of smart contracts and the necessary machine learning code, followed by rigorous testing and validation. The experimental results validate the effectiveness and reliability of our proposed model, demonstrating its potential to ensure effective and secure big data trading.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244894
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4895: A Lightweight Image Encryption Scheme
Using DNA Coding and Chaos
Authors: Marwan A. Fetteha, Wafaa S. Sayed, Lobna A. Said
First page: 4895
Abstract: Protecting transmitted multimedia data such as images is a significant concern. This work proposes an encryption algorithm for greyscale images using a Pseudo-Random Number Generator (PRNG), DNA coding, and pixel sum. The proposed approach is implemented on a Genesys 2 FPGA using minimal hardware resources and can operate at a maximum frequency of 110.8 MHz. In addition, several performance evaluation tests are conducted for multiple images, including statistical analysis of the encrypted image, keyspace analysis, and differential attack analysis. The system is compared to recent works with respect to encryption quality and used hardware resources. The proposed scheme outperformed recent chaos-based image encryption schemes.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244895
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4896: A Knowledge-Enhanced Hierarchical
Reinforcement Learning-Based Dialogue System for Automatic Disease
Diagnosis
Authors: Ying Zhu, Yameng Li, Yuan Cui, Tianbao Zhang, Daling Wang, Yifei Zhang, Shi Feng
First page: 4896
Abstract: Deep Reinforcement Learning is a key technology for the diagnosis-oriented medical dialogue system, determining the type of disease according to the patient’s utterances. The existing dialogue models for disease diagnosis cannot achieve good performance due to the large number of symptoms and diseases. In this paper, we propose a knowledge-enhanced hierarchical reinforcement learning model for strategy learning in the medical dialogue system for disease diagnosis. Our hierarchical strategy alleviates the problem of a large action space in reinforcement learning. In addition, the knowledge enhancement module integrates a learnable disease–symptom relationship matrix and medical knowledge graph into the hierarchical strategy for higher diagnosis success rate. Our proposed model has been proved to be effective on a medical dialogue dataset for automatic disease diagnosis.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244896
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4897: Facial Wrinkle Detection with Multiscale
Spatial Feature Fusion Based on Image Enhancement and ASFF-SEUnet
Authors: Jiang Chen, Mingfang He, Weiwei Cai
First page: 4897
Abstract: Wrinkles, crucial for age estimation and skin quality assessment, present challenges due to their uneven distribution, varying scale, and sensitivity to factors like lighting. To overcome these challenges, this study presents facial wrinkle detection with multiscale spatial feature fusion based on image enhancement and an adaptively spatial feature fusion squeeze-and-excitation Unet network (ASFF-SEUnet) model. Firstly, in order to improve wrinkle features and address the issue of uneven illumination in wrinkle images, an innovative image enhancement algorithm named Coiflet wavelet transform Donoho threshold and improved Retinex (CT-DIR) is proposed. Secondly, the ASFF-SEUnet model is designed to enhance the accuracy of full-face wrinkle detection across all age groups under the influence of lighting factors. It replaces the encoder part of the Unet network with EfficientNet, enabling the simultaneous adjustment of depth, width, and resolution for improved wrinkle feature extraction. The squeeze-and-excitation (SE) attention mechanism is introduced to grasp the correlation and importance among features, thereby enhancing the extraction of local wrinkle details. Finally, the adaptively spatial feature fusion (ASFF) module is incorporated to adaptively fuse multiscale features, capturing facial wrinkle information comprehensively. Experimentally, the method excels in detecting facial wrinkles amid complex backgrounds, robustly supporting facial skin quality diagnosis and age assessment.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244897
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4898: A Novel Contactless Blood Pressure
Measurement System and Algorithm Based on Vision Intelligence
Authors: Mavlonbek Khomidov, Deokwoo Lee, Jong-Ha Lee
First page: 4898
Abstract: The measurement of vital signs such as blood pressure plays a key role in human health. Usually, we encounter some problems when we check them in the traditional way; for example, it is impossible to check continuously, and measuring vital signs requires direct contact with the patient, which can be uncomfortable for certain individuals. In this research, we present a vision-based system for estimating blood pressure using pulse transit time (PTT) and the Eulerian video magnification (EVM) technique to amplify tiny color variations caused by blood flow to calculate arterial pulse waves traveling between two arterial sites. Calculating the PTT by processing the video signal for each subject, an oscillometer BP device was used to evaluate the performance between measurements in different conditions, including rest, exercise, and during recovery. Mean systolic BP was 115 mmHg at rest, 137 mmHg during high-intensity exercise, and 114 mmHg during recovery, respectively. The average value of diastolic blood pressure did not change significantly before, during, and after exercise. When we compared the systolic and diastolic blood pressure with ground-truth results, our system showed an accuracy of 91% for systolic blood pressure and 90% for diastolic blood pressure.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244898
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4899: Implementation of a Prediction Model in
a Smart System for Enhancing Comfort in Dwellings
Authors: Snezhinka Zaharieva, Ivan Georgiev, Slavi Georgiev, Iordan Stoev, Adriana Borodzhieva
First page: 4899
Abstract: This article introduces a novel approach to ensuring optimal comfort in residential environments, using a smart system powered by predictive modeling. At its core lies a complex algorithm, presented alongside a detailed block diagram, guiding the system’s operations, which are tailored for residential comfort. The primary focus is on the time series analysis of forecasting relative humidity—a critical parameter influencing comfort in living spaces. Among the various prediction models analyzed, a model based on the Fourier equation emerged as the most efficient, accounting for approximately 81% of variances in data. Upon validation, the model showcases an impressive relative error of just ±0.1%. The research underscores the potential of leveraging advanced forecasting in optimizing devices like dehumidifiers or air humidifiers, ensuring the desired comfort while minimizing energy consumption. This innovative integration paves the way for a smarter, more sustainable residential living experience.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244899
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4900: Inversion of Target Magnetic Moments
Based on Scalar Magnetic Anomaly Signals
Authors: Ke Zhang, Xiuzhi You, Xiaodong Liu, Jiarui Liu, Wanhua Zhu
First page: 4900
Abstract: As a key physical property of underwater ferromagnetic targets, magnetic moment can reflect important information such as the mass and heading of the target. However, most of the current magnetic moment estimation methods rely on vector magnetic field sensors or sensor arrays to measure the magnetic field, which limits its application in remote target magnetic moment calculation on mobile platforms to some extent. To solve this problem, a real-time magnetic moment inversion method based on the high-precision scalar magnetic measurement data of a high-speed moving platform is proposed in this paper. The method allows the estimation of the magnetic moment of underwater ferromagnetic targets by the scalar magnetic measurement data of an optical pump magnetic field sensor mounted on a high-speed moving platform. The experimental results show that this method has high precision in estimating magnetic moment; the average error of the magnetic moment amplitude was only 5.85%, while the average errors of the magnetic moment inclination and magnetic moment deflection were 1.58° and 2.79°, respectively. These results provide a new and effective way to estimate the magnetic moment of underwater ferromagnetic targets and are expected to have important practical applications.
Citation: Electronics
PubDate: 2023-12-05
DOI: 10.3390/electronics12244900
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4901: On-Cloud Linking Approach Using a
Linkable Glue Layer for Metamorphic Edge Devices
Authors: Dongkyu Lee, Daejin Park
First page: 4901
Abstract: As sensors operating at the edge continue to evolve, the amount of data that edge devices need to process is increasing. Cloud computing methods have been proposed to process complex data on edge devices that are powered by limited resources. However, the existing cloud computing approach, which provides services from servers determined at the compile stage on the edge, is not suitable for the metamorphic edge device proposed in this paper. Therefore, we have realized the operation of metamorphic edge devices by changing the service that accelerates the application in real time according to the surrounding environmental conditions on the edge device. The on-cloud linking approach separates the code for communication from the edge and server into a linkable glue layer. The separated communication code in the linkable glue layer is reconfigured in real time according to the environment of the edge device. To verify the computational acceleration of cloud computing and the real-time service change of the metamorphic edge device, we operated services that perform matrix multiplication operations with one process, two processes, and four processes in parallel on the edge–cloud system based on the on-cloud linking approach. Through the experiments, it was confirmed that the on-cloud linking approach changes the service provided in real time according to changes in external environmental data without changing the code built into the edge. When a square matrix operation with 1000 rows was loaded onto the proposed platform, the size of the code embedded into the edge device decreased by 8.88% and the operation time decreased by 96.7%.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244901
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4902: A Review: Remote Sensing Image Object
Detection Algorithm Based on Deep Learning
Authors: Chenshuai Bai, Xiaofeng Bai, Kaijun Wu
First page: 4902
Abstract: Target detection in optical remote sensing images using deep-learning technologies has a wide range of applications in urban building detection, road extraction, crop monitoring, and forest fire monitoring, which provides strong support for environmental monitoring, urban planning, and agricultural management. This paper reviews the research progress of the YOLO series, SSD series, candidate region series, and Transformer algorithm. It summarizes the object detection algorithms based on standard improvement methods such as supervision, attention mechanism, and multi-scale. The performance of different algorithms is also compared and analyzed with the common remote sensing image data sets. Finally, future research challenges, improvement directions, and issues of concern are prospected, which provides valuable ideas for subsequent related research.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244902
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4903: Design and Assurance of Safety-Critical
Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method
and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control
System
Authors: Antonio V. Silva Neto, Henrique L. Silva, João B. Camargo, Jorge R. Almeida, Paulo S. Cugnasca
First page: 4903
Abstract: With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems meet their safety requirements is of paramount importance for their revenue service. Based on this context, this paper has two main objectives. The first of them is to present the Safety ArtISt method, developed by the authors to guide the lifecycle of AI-based safety-critical systems, and emphasize its FPGA-oriented tasks and recommended practice towards safety assurance. The second one is to illustrate the application of Safety ArtISt with an FPGA-based braking control system for autonomous vehicles relying on explainable AI generated with High-Level Synthesis. The results indicate that Safety ArtISt played four main roles in the safety lifecycle of AI-based systems for FPGAs. Firstly, it provided guidance in identifying the safety-critical role of activities such as sensitivity analyses for numeric representation and FPGA dimensioning to achieve safety. Furthermore, it allowed building qualitative and quantitative safety arguments from analyses and physical experimentation with actual FPGAs. It also allowed the early detection of safety issues—thus reducing project costs—and, ultimately, it uncovered relevant challenges not discussed in detail when designing safety-critical, explainable AI for FPGAs.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244903
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4904: The Design and Development of a
UAV’s Micro-Turbogenerator System and the Associated Control Testing
Bench
Authors: Tiberius-Florian Frigioescu, Gabriel Petre Badea, Mădălin Dombrovschi, Mihaela Raluca Condruz, Daniel-Eugeniu Crunțeanu, Grigore Cican
First page: 4904
Abstract: A study on the possibility of integrating a micro-turbogenerator system into a multi-rotor UAV platform was performed along with a performance evaluation of the result. This paper presents the design and development of a micro-turbogenerator system constructed from commercially available components and the associated test bench that was needed to validate the system. The goal of the micro-turbogenerator system was to replace the electrical power source (the batteries) of an experimental UAV. Substituting the electrical power source with a hybrid propulsion system has the potential to enhance the UAV’s endurance and functionality, rendering it more versatile and efficient. The hybrid propulsion system involves the use of a commercially available micro-gas turbine that propels an electric generator, supplying the required electrical power for the UAV’s electric propulsion system. Integrating this micro-turbogenerator system ensures a substantial increase in UAV endurance. The test bench was used to assess the performance of the micro-turbogenerator system and formulate a control law necessary for maintaining a balance between the power generated by the system and the power consumed by the UAV. The developed test bench yielded crucial data, including electric power, generated voltage, generator speed, and power consumption (simulating the UAV in this case). During the testing campaign, the variation in the main physical quantities involved in the command and control of the hybrid propulsion system was registered and analyzed. A total power of 700 W was obtained during the tests, which is the maximum that can be registered for maintaining a power of 25 V.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244904
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4905: Learning Adaptive Quantization Parameter
for Consistent Quality Oriented Video Coding
Authors: Tien Huu Vu, Minh Ngoc Do, Sang Quang Nguyen, Huy PhiCong, Thipphaphone Sisouvong, Xiem HoangVan
First page: 4905
Abstract: In the industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named video multimethod assessment fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain consistent quality while still achieving high compression performance. First of all, in order to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of rate-distortion optimization (RDO) mechanism is adaptively computed according to rate-QP and quality-QP models. The experimental results show that the proposed PVC solution has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and the relevant benchmark, notably with around 10% bitrate saving on average.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244905
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4906: A Study of Direction-of-Arrival
Estimation with an Improved Monopulse Ratio Curve Using Beamforming for an
Active Phased Array Antenna System
Authors: Jinwoo Jung, Bagas Satriyotomo, Seongmin Pyo
First page: 4906
Abstract: When constructing a wireless communication network, the line of sight of radio waves is limited by the terrain features in a ground communication network. Also, satellite communication networks face capacity limitations and are vulnerable to jamming. Aviation communication networks can solve the above-mentioned problems. To construct seamless aviation communication networks, fast counterpart location estimation and efficient beam steering performance are essential. Among various techniques used for searching the counterpart’s location, the monopulse technique has the advantage of quickly estimating the location through a simplified procedure. However, the nonlinear characteristics of the monopulse ratio curve, which are inevitably caused by the general antenna beam shape, both limit the location estimation range and reduce the estimated location accuracy. To overcome these limitations, a method that improves the estimation accuracy and extends the range by correcting the sum and difference patterns using the beamforming technique of active phased array antennas was proposed. An antenna system model suitable for aviation communication networks was presented, and the proposed model was experimentally proven to be effective. An average angle error of 0.021° was observed in the estimation of the accuracy of the antenna location.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244906
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4907: Model Reference Adaptive Observer for
Permanent Magnet Synchronous Motors Based on Improved Linear Dead-Time
Compensation
Authors: Huipeng Chen, Renjie Zhang, Shaopeng Zhu, Jian Gao, Rougang Zhou
First page: 4907
Abstract: Aiming at the problem that the model reference adaptive observer (MRAS) is sensitive to the parameters of the motor model, this paper designs a model reference adaptive observer with resistance adaption and considers the influence of the inverter dead zone. The method introduces the online identification of resistance on the traditional reference adaptive observer model, corrects the resistance parameters of the model’s reference adaptive observer in real time, selects PI as the adaptive law, and proves the stability of the adaptive law using Popov’s stability theorem. To study the influence of the inverter dead zone on the voltage parameters in the estimation model at low speeds, an improved linear dead zone compensation method is proposed to improve the model reference adaptive observer, which eliminates the voltage error between the estimated motor model and the actual motor model. For a given rotational speed of 300 rpm, the observed errors of sliding mode observer (SMO) and traditional MRAS in a steady state were 8.3% and 6%, respectively, and the rotational speed error was controlled to 1.6% using the compensation scheme proposed in this paper. After compensation, the resistance identification error was reduced from 17% to 1.6%. The simulation and experimental results showed that the accuracy of position estimation can be significantly improved using dead zone compensation under low-speed conditions, and the dead zone compensation can improve the accuracy of the online recognition of the resistance.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244907
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4908: Lightweight and Secure Multi-Message
Multi-Receiver Certificateless Signcryption Scheme for the Internet of
Vehicles
Authors: Guishuang Xu, Xinchun Yin, Xincheng Li
First page: 4908
Abstract: The Internet of Vehicles (IoV) improves traffic efficiency and enhances driving safety through the real-time collection and analysis of traffic-related data. Numerous secure and privacy-preserving communication protocols have been proposed for the IoV. However, various security threats, privacy leakage, and inefficient communications remain unaddressed. Therefore, a lightweight and secure multi-message multi-receiver certificateless signcryption (LS-MRCLSC) scheme based on elliptic curve cryptography (ECC) is proposed. The proposed scheme guarantees secure communication and promotes messaging efficiency with multi-cast mode. Multiple key generation centers (KGCs) collaborate to generate and update the system master key (SMK) using Feldman’s verifiable secret-sharing (FVSS) algorithm, avoiding the single point of failure (SPoF) problem. Formal security proofs under the random oracle model (ROM) demonstrate that the proposed scheme meets requirements such as data confidentiality, message unforgeability, anonymity, and unlinkability. Performance evaluations confirm that the LS-MRCLSC scheme is better than similar schemes in terms of efficiency, feasibility, and scalability.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244908
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4909: Advancements in Household Load
Forecasting: Deep Learning Model with Hyperparameter Optimization
Authors: Hamdi A. Al-Jamimi, Galal M. BinMakhashen, Muhammed Y. Worku, Mohamed A. Hassan
First page: 4909
Abstract: Accurate load forecasting is of utmost importance for modern power generation facilities to effectively meet the ever-changing electricity demand. Predicting electricity consumption is a complex task due to the numerous factors that influence energy usage. Consequently, electricity utilities and government agencies are constantly in search of advanced machine learning solutions to improve load forecasting. Recently, deep learning (DL) has gained prominence as a significant area of interest in prediction efforts. This paper introduces an innovative approach to electric load forecasting, leveraging advanced DL techniques and making significant contributions to the field of energy management. The hybrid predictive model has been specifically designed to enhance the accuracy of multivariate time series forecasting for electricity consumption within the energy sector. In our comparative analysis, we evaluated the performance of our proposed model against ML-based and state-of-the-art DL models, using a dataset obtained from the Distribution Network Station located in Tetouan City, Morocco. Notably, the proposed model surpassed its counterparts, demonstrating the lowest error in terms of the Root-Mean-Square Error (RMSE). This outcome underscores its superior predictive capability and underscores its potential to advance the accuracy of electricity consumption forecasting.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244909
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4910: Penetration State Identification of
Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using
Multi-Spectrogram Fusion Inception Convolutional Neural Network
Authors: Guang Yang, Kainan Guan, Jiarun Yang, Li Zou, Xinhua Yang
First page: 4910
Abstract: The CMT welding process has been widely used for aluminum alloy welding. The weld’s penetration state is essential for evaluating the welding quality. Arc sound signals contain a wealth of information related to the penetration state of the weld. This paper studies the correlation between the frequency domain features of arc sound signals and the weld penetration state, as well as the correlation between Mel spectrograms, Gammatone spectrograms and Bark spectrograms and the weld penetration state. Arc sound features fused with multilingual spectrograms are constructed as inputs to a custom Inception CNN model that is optimized based on GoogleNet for CMT weld penetration state recognition. The experimental results show that the accuracy of the method proposed in this paper for identifying the fusion state of CMT welds in aluminum alloy plates is 97.7%, which is higher than the identification accuracy of a single spectrogram as the input. The recognition accuracy of the customized Inception CNN is improved by 0.93% over the recognition accuracy of GoogleNet. The customized Inception CNN also has high recognition results compared to AlexNet and ResNet.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244910
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4911: Lane Detection Based on Adaptive
Cross-Scale Region of Interest Fusion
Authors: Lujuan Deng, Xinglong Liu, Min Jiang, Zuhe Li, Jiangtao Ma, Hanbing Li
First page: 4911
Abstract: Lane detection, a crucial component of autonomous driving systems, is in charge of precise lane location to ensure that cars navigate lanes appropriately. However, in challenging conditions like shadows and extreme lighting, lanes may become obstructed or blurred, posing a significant challenge to the lane-detection task as the model struggles to extract sufficient visual information from the image. The current anchor-based lane-detection network detects lanes in complex scenes by mapping anchors to images to extract features and calculating the relationship between each anchor and other anchors for feature fusion. However, it is insufficient for anchors to extract subtle features from images, and there is no guarantee that the information carried by each anchor is valid. Therefore, this study proposes the adaptive cross-scale ROI fusion network (ACSNet) to fully extract the features in the image so that the anchor carries more useful information. ACSNet selects important anchors in an adaptive manner and fuses these important anchors with the original anchors across scales. Through this feature extraction method, the features of different field-of-view ranges under complex road surfaces can be learned, and diversified features can be integrated to ensure that lanes can be well detected under complex road surfaces such as shadows and extreme lighting. Furthermore, due to the slender structure of lane lines, there are relatively few useful features in the images. Therefore, this study also proposes a Three-dimensional Coordinate Attention Mechanism (TDCA) to enhance image features. The Three-dimensional Coordinate Attention Mechanism extensively explores relationships among features in the row, column, and spatial dimensions. It calculates feature weights for each of these dimensions and ultimately performs element-wise multiplication with the entire feature map. Experimental results demonstrate that our network achieves excellent performance on the existing public datasets, CULane and Tusimple.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244911
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4912: Analysis of Indirect Lightning Effects
on Low-Noise Amplifier and Protection Design
Authors: Zhenyang Ma, Jiahao Liu, Zhaobin Duan, Chunlei Shi, Shaonan He
First page: 4912
Abstract: In order to analyze the interference mechanisms of indirect lightning effects on a low-noise amplifier (LNA), a circuit model of the LNA was constructed based on the advanced design system 2020 (ADS 2020) software. Lightning pulse injection simulations were conducted to explore the influence of lightning pulses on the performance of the LNA. A pin injection test was performed to investigate the interference and damage threshold of the LNA. A protective circuit incorporating the transient voltage suppressor (TVS) and Darlington structure was designed through simulation, employing the ADS 2020 for the LNA. The research findings reveal that the interference threshold for the LNA is 60 V, while the damage threshold is determined to be 100 V. The protective circuit demonstrates a measured insertion loss of 0.1 dB, a response time of 1.5 ns, and a peak output voltage of 20 V. The research results indicate that the protective circuit can effectively reduce the impact of lightning’s indirect effects on the LNA. In the future, we will continue the design work of the protective circuit and proceed with physical fabrication and experimental validation.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244912
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4913: Adaptive Truck Platooning with Drones: A
Decentralized Approach for Highway Monitoring
Authors: J. de Curtò, I. de Zarzà, Juan Carlos Cano, Pietro Manzoni, Carlos T. Calafate
First page: 4913
Abstract: The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244913
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4914: Deployment and Implementation Aspects of
Radio Frequency Fingerprinting in Cybersecurity of Smart Grids
Authors: Maaz Ali Awan, Yaser Dalveren, Ferhat Ozgur Catak, Ali Kara
First page: 4914
Abstract: Smart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart grids, cybersecurity of IoT is paramount. To this end, use of cryptographic security methods is prevalent in existing IoT. Non-cryptographic methods such as radio frequency fingerprinting (RFF) have been on the horizon for a few decades but are limited to academic research or military interest. RFF is a physical layer security feature that leverages hardware impairments in radios of IoT devices for classification and rogue device detection. The article discusses the potential of RFF in wireless communication of IoT devices to augment the cybersecurity of smart grids. The characteristics of a deep learning (DL)-aided RFF system are presented. Subsequently, a deployment framework of RFF for smart grids is presented with implementation and regulatory aspects. The article culminates with a discussion of existing challenges and potential research directions for maturation of RFF.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244914
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4915: Throughput Optimization for Blockchain
System with Dynamic Sharding
Authors: Chuyi Liu, Jianxiong Wan, Leixiao Li, Bingbing Yao
First page: 4915
Abstract: Sharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL)-based methods, where the action space must be discretized to increase processability. However, the discretization degrades the quality of the solution since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and provide dynamic sharding strategies based on them. The Branching Dueling Q-Network Blockchain Sharding (BDQBS) algorithm is designed for discrete action spaces. Compared with traditional DRL algorithms, the BDQBS overcomes the drawbacks of high action space dimensions and difficulty in training neural networks. And it improves the performance of the blockchain system by 1.25 times. We also propose a sharding control algorithm based on the Parameterized Deep Q-Networks (P-DQN) algorithm, i.e., the Parameterized Deep Q-Networks Blockchain Sharding (P-DQNBS) algorithm, to efficiently handle the discrete–continuous hybrid action space without the scalability issues. Also, the method can effectively improve the TPS by up to 28%.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244915
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4916: A 12~14-Bit SAR-SS Hybrid ADC with SS
Bit Shifting Resolution Reconfigurable Method for Bio-Signal Processing
Authors: Cheol Woo Moon, Kwang Sub Yoon, Jonghwan Lee
First page: 4916
Abstract: This paper presents a low-power, high-resolution reconfigurable hybrid ADC for bio-electrical signal processing. The proposed ADC contains a SAR ADC for the most significant bit (MSB) and a single-slope ADC for the least significant bit (LSB). To solve the issue of exponentially increasing sampling speed based on the resolution of the single-slope ADC, the SAR ADC is designed to be reconfigurable with a resolution of 8–10-bit, while the single-slope ADC is configured with a resolution of 4-bit. To achieve this resolution reconfiguration, the bit shifting method is proposed and implemented with reconfigurable SAR logic circuit and 4-bit single-slope digital ramp generator. Measurement results demonstrate the power consumption of 34.0 uW, which includes analog power of 23.8 uW and digital power of 10.2 uW, INL/DNL of ±3.5 LSB and −1.0/+2.5 LSB. The ENOB and FoM are measured to be 10.8 bits and 53 fJ/step, respectively.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244916
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4917: Communication Time Optimization of
Register-Based Data Transfer
Authors: Andrzej Bożek, Dariusz Rzonca
First page: 4917
Abstract: The data exchange according to communication protocols used in automation is often based on registers (e.g., Modbus). Values of many variables can be sent in a single frame, provided that they are placed in adjacent registers. If the required registers are not adjacent, it may sometimes be advantageous to transmit more registers than required, along with redundant ones, to minimize the number of frames and the total transmission time. The article analyzes the possibilities of improving time parameters and determining the optimal grouping based on the arrangement of registers. Various existing optimization approaches such as mixed integer linear programming, constraint programming, and a tabu search are analyzed, and several new simple deterministic algorithms (greedy or heuristic rule-based) are proposed. The results obtained were confirmed experimentally.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244917
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4918: A Study on the High Reliability Audio
Target Frequency Generator for Electronics Industry
Authors: Changsik Park, Euntack Han, Ikjae Kim, Dongkyoo Shin
First page: 4918
Abstract: The frequency synthesizer performs a simple function of generating a desired frequency by manipulating a reference frequency signal, but stable and precise frequency generation is essential for reliable operation in mechanical equipment such as communication, control, surveillance, medical, and commercial fields. Frequency synthesis, which is commonly used in various contexts, has been used in analog and digital methods or hybrid methods. Especially in the field of communication, a precise frequency synthesizer is required for each frequency band, from very low-frequency AF (audio frequency) to high-frequency microwaves. The purpose of this paper is to design and implement a highly reliable frequency synthesizer for application to a railway track circuit systems using AF frequency only with the logic circuit of an FPGA (field programmable gate array) without using a microprocessor. Therefore, the development trend of analog, digital, and hybrid frequency synthesizers is examined, and a method for precise frequency synthesizer generation on the basis of the digital method is suggested. In this paper, the generated frequency generated by applying the digital frequency synthesizer using the ultra-precision algorithm completed by many trials and errors shows the performance of generating the target frequency with an accuracy of more than 99.999% and a resolution of mHz, which is much higher than the resolution of 5 Hz in the previous study. This highly precise AF-class frequency synthesizer contributes greatly to the safe operation and operation of braking and signaling systems when used in transportation equipment such as railways and subways.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244918
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4919: Testing the Verification and Validation
Capability of a DCP-Based Interface for Distributed Real-Time Applications
Authors: Mikel Segura, Alejandro J. Calderón, Tomaso Poggi, Rafael Barcena
First page: 4919
Abstract: Cyber–physical systems (CPS) integrate diverse elements developed by various vendors, often dispersed geographically, posing significant development challenges. This paper presents an improved version of our previously developed co-simulation interface based on the non-proprietary Distributed Co-Simulation Protocol (DCP) standard, now optimized for broader hardware platform compatibility. The core contributions include a demonstration of the interface’s hardware-agnostic capabilities and its straightforward adaptability across different platforms. Furthermore, we provide a comparative analysis of our interface against the original DCP. It is validated via various X-in-the-Loop simulations, reinforcing the interface’s versatility and applicability in diverse scenarios, such as distributed real-time executions, verification and validation processes, or Intellectual Property protection.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244919
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4920: Reliably Controlling Massive Traffic
between a Sensor Network End Internet of Things Device Environment and a
Hub Using Transmission Control Protocol Mechanisms
Authors: Viacheslav Kovtun, Krzysztof Grochla, Wojciech Kempa, Konrad Połys
First page: 4920
Abstract: The constant growth of Internet of Things traffic is ensured by the ongoing evolution of the hierarchy of all hardware links of sensor networks. At the same time, the implementation of the Edge computing ideology regulates the complexity of the “first-mile” section (from the sensors array to the peripheral server). Here, the authors suggest paying attention to the growing share of massive traffic from target sensors in the total traffic of the sensors array. This circumstance makes it expedient to introduce an additional link to the peripheral server for summarizing massive traffic from target sensors. The authors present a sensor network end IoT device (SNEIoTD), implemented grounded on a reliable and cheap Raspberry Pi computing platform, as such a link. The introduction of this SNEIoTD makes it possible to reduce the probability of information loss from the critical infrastructure of a smart city and increase the flexibility of controlling the massive traffic of the first mile. In this context, the urgent task is the reliable control of information transfer from the SNEIoTD environment to a hub, which the authors formalize based on Transmission Control Protocol (TCP). This article proposes a mathematical model of the interaction of the main mechanisms of the TCP in the form of a queuing system. As part of this model, a semi-Markov process of an information transfer with a unified speed is selected and its stationary distribution is analytically formalized. A computationally efficient information technology for determining the TCP Window Size is formulated, taking into account the interaction of TCP mechanisms in the process of massive traffic control. Using the example of TCP Westwood+ protocol modification, it is shown that the results of the application of information technology permit increases in the stability of data transfer under the circumstances of increasing Round-Trip Times.
Citation: Electronics
PubDate: 2023-12-06
DOI: 10.3390/electronics12244920
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4921: Analysis and Design of a Pulsed Power
Generator for a Low-Energy Magnetic Pulse Welding System
Authors: Young-Min Kwon, Min-Wook Hwang, Kwang-Cheol Ko
First page: 4921
Abstract: Magnetic pulse welding (MPW) is a joining method that uses Lorentz force generated from an electromagnetic field. This method not only has the advantage of not causing thermal deformation of the material and no by-products compared to the method of joining by melting by heat but also enables the joining of dissimilar metals rather than the joining of the same metal. Joining dissimilar metals can reduce the weight of mechanical devices and apply them to various fields. Recent research on MPW has focused on the characteristics of bonding according to the material or structure of metal rather than on pulse power research that generates the main factor of operation. However, in the operation of MPW, a Lorentz force is generated by the induced current caused by the electromotive force created in the flyer tube and the external magnetic field in the actuator. Therefore, it is necessary to analyze and optimize the pulse power to improve reliability and to miniaturize the system to expand the MPW utilization range. In this paper, we analyzed MPW operation according to a section of the pulse power output waveform. A condition for obtaining the maximum current in the flyer tube was proposed, and a plateau-shaped waveform was derived as an ideal output waveform capable of maintaining the Lorentz force. Through analysis, the proposed pulse power device is designed as a pulse-forming network (PFN) that generates a plateau output waveform. The design specification is that the circuit of PFN (type E) is designed so that the output waveform is pulse width 10 (μs) and the maximum output current is 100 (kA), and it is verified by simulation.
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244921
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4922: Improvements in the Electronic
Performance of ZnO-Based Varistors by Modifying the Manufacturing Process
Parameters
Authors: Attila Simo, Flaviu Mihai Frigura-Iliasa, Mihaela Frigura-Iliasa, Petru Andea
First page: 4922
Abstract: Varistors processed from mixtures of certain metal oxides (as additives to the main component, zinc oxide, ZnO), called MOVs, represent the devices most used for overvoltage protection and are integrated into the construction of high-performance surge arresters. The manufacturing process of these powerful electronic devices is crucial for their electronic performance. For manufacturing temperature-related studies, we used two seven-varistor experimental series: one based on two added oxides and the other on five ones. The main goal of these series was to identify the suitable sintering temperature in the case of each chemical composition from the point of view of assessing the most important electric/electronic behavioral parameters. A simple study considering mass losses after the sintering process was carried out in order to provide a brief reference for the manufacturing engineers. Before performing these studies, each varistor was sintered at a different temperature. In order to draw a general set of conclusions about the impact of the sintering pressure on the main electrical and electronic performances, a second activity involved producing two additional smaller varistors series with similar chemical compositions (two main oxides and five main oxides as additives) all processed at two different sintering pressures 4900 N/cm2 and 9800 N/cm2. The electrical/electronic parameters considered for the assessment are the main current–voltage characteristics, the non-linearity logarithmic coefficient, and the normal operational leakage current. All electrical/electronic behavioral tests were performed according to the IEC standards and regulations for both types of varistor devices (seven different temperatures and two pressure values). We concluded that a sintering temperature of 1300 °C and a pressure of 4900 N/cm2 are optimal for both types of varistors (with two and five additives).
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244922
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4923: PLA—A Privacy-Embedded Lightweight
and Efficient Automated Breast Cancer Accurate Diagnosis Framework for the
Internet of Medical Things
Authors: Chengxiao Yan, Xiaoyang Zeng, Rui Xi, Awais Ahmed, Mengshu Hou, Muhammad Hanif Tunio
First page: 4923
Abstract: The Internet of Medical Things (IoMT) can automate breast tumor detection and classification with the potential of artificial intelligence. However, the leakage of sensitive data can cause harm to patients. To address this issue, this study proposed an intrauterine breast cancer diagnosis method, namely “Privacy-Embedded Lightweight and Efficient Automated (PLA)”, for IoMT, which represents an approach that combines privacy-preserving techniques, efficiency, and automation to achieve our goals. Firstly, our model is designed to achieve lightweight classification prediction and global information processing of breast cancer by utilizing an advanced IoMT-friendly ViT backbone. Secondly, PLA protects patients’ privacy by federated learning, taking the classification task of breast cancer as the main task and introducing the texture analysis task of breast cancer images as the auxiliary task to train the model. For our PLA framework, the classification accuracy is 0.953, the recall rate is 0.998 for the best, the F1 value is 0.969, the precision value is 0.988, and the classification time is 61.9 ms. The experimental results show that the PLA model performs better than all of the comparison methods in terms of accuracy, with an improvement of more than 0.5%. Furthermore, our proposed model demonstrates significant advantages over the comparison methods regarding time and memory.
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244923
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4924: Rulers2023: An Annotated Dataset of
Synthetic and Real Images for Ruler Detection Using Deep Learning
Authors: Dalius Matuzevičius
First page: 4924
Abstract: This research investigates the usefulness and efficacy of synthetic ruler images for the development of a deep learning-based ruler detection algorithm. Synthetic images offer a compelling alternative to real-world images as data sources in the development and advancement of computer vision systems. This research aims to answer whether using a synthetic dataset of ruler images is sufficient for training an effective ruler detector and to what extent such a detector could benefit from including synthetic images as a data source. The article presents the procedural method for generating synthetic ruler images, describes the methodology for evaluating the synthetic dataset using trained convolutional neural network (CNN)-based ruler detectors, and shares the compiled synthetic and real ruler image datasets. It was found that the synthetic dataset yielded superior results in training the ruler detectors compared with the real image dataset. The results support the utility of synthetic datasets as a viable and advantageous approach to training deep learning models, especially when real-world data collection presents significant logistical challenges. The evidence presented here strongly supports the idea that when carefully generated and used, synthetic data can effectively replace real images in the development of CNN-based detection systems.
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244924
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4925: Integration of Deep Learning into the
IoT: A Survey of Techniques and Challenges for Real-World Applications
Authors: Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara, Qinghe Zheng
First page: 4925
Abstract: The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks.
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244925
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4926: LiDAR Point Clouds Semantic Segmentation
in Autonomous Driving Based on Asymmetrical Convolution
Authors: Xiang Sun, Shaojing Song, Zhiqing Miao, Pan Tang, Luxia Ai
First page: 4926
Abstract: LiDAR has become a vital sensor for autonomous driving scene understanding. To meet the accuracy and speed of LiDAR point clouds semantic segmentation, an efficient model ACPNet is proposed in this paper. In the feature extraction stage, the backbone is constructed with asymmetric convolutions, so the skeleton of the square convolution kernel is enhanced, which leads to greater robustness to target rotation. Moreover, a contextual feature enhancement module is designed to extract richer contextual features. During training, global scaling and global translation are performed to enrich the diversity of datasets. Compared with the baseline network PolarNet, the mIoU of ACPNet on the SemanticKITTI, SemanticPOSS and nuScenes datasets are improved by 5.1%, 1.6% and 2.9%, respectively. Meanwhile, the speed of ACPNet is 14 FPS, which basically meets the real-time requirements in autonomous driving scenarios. The experimental results show that ACPNet significantly improves the performance of LiDAR point cloud semantic segmentation.
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244926
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4927: An Efficient Point Cloud Semantic
Segmentation Method Based on Bilateral Enhancement and Random Sampling
Authors: Dan Shan, Yingxuan Zhang, Xiaofeng Wang, Wenrui Luo, Xiangdong Meng, Yuhan Liu, Xiang Gao
First page: 4927
Abstract: Point cloud semantic segmentation is of utmost importance in practical applications. However, most existing methods have evolved to be incredibly intricate, leading to a rise in complexity that has made them increasingly impractical for real-world utilization. The escalating complexity of these methods has resulted in a deterioration in their efficiency and ease of implementation, making them less suitable for use in time-sensitive and resource-constrained environments. Towards this issue, we propose an efficient and lightweight segmentation method, able to achieve a remarkable performance in terms of both segmentation accuracy, training speed, and space consumption. Specifically, we first propose to adopt random sampling to replace the original one to obtain more efficiency. Moreover, a lightweight decoding module and an improved bilateral enhancement (BAE) module are developed to further improve the performance. The proposed method achieved a 73.6% and 60.7% mIoU on the S3DIS and Semantickitti datasets, respectively. In the future, the random sampling and the proposed BAE module can be adopted in a more concise and lightweight network to achieve faster and more-accurate point cloud segmentation.
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244927
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4928: Real-Time Object Detection and Tracking
for Unmanned Aerial Vehicles Based on Convolutional Neural Networks
Authors: Shao-Yu Yang, Hsu-Yung Cheng, Chih-Chang Yu
First page: 4928
Abstract: This paper presents a system applied to unmanned aerial vehicles based on Robot Operating Systems (ROSs). The study addresses the challenges of efficient object detection and real-time target tracking for unmanned aerial vehicles. The system utilizes a pruned YOLOv4 architecture for fast object detection and the SiamMask model for continuous target tracking. A Proportional Integral Derivative (PID) module adjusts the flight attitude, enabling stable target tracking automatically in indoor and outdoor environments. The contributions of this work include exploring the feasibility of pruning existing models systematically to construct a real-time detection and tracking system for drone control with very limited computational resources. Experiments validate the system’s feasibility, demonstrating efficient object detection, accurate target tracking, and effective attitude control. This ROS-based system contributes to advancing UAV technology in real-world environments.
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244928
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4929: Integrating Lorenz Hyperchaotic
Encryption with Ring Oscillator Physically Unclonable Functions (RO-PUFs)
for High-Throughput Internet of Things (IoT) Applications
Authors: Alexander Magyari, Yuhua Chen
First page: 4929
Abstract: With the combined call for increased network throughput and security comes the need for high-bandwidth, unconditionally secure systems. Through the combination of true random number generators (TRNGs) for unique seed values, and four-dimensional Lorenz hyperchaotic systems implemented on a Stratix 10 Intel FPGA, we are able to implement 60 MB/s encryption/decryption schemes with 0% data loss on an unconditionally secure system with the NIST standard using less than 400 mW. Further, the TRNG implementation allows for unique encryption outputs for similar images while still enabling proper decryption. Histogram and adjacent pixel analysis on sample images demonstrate that without the key, it is not possible to extract the plain text from the encrypted image. This encryption scheme was implemented via PCIe for testing and analysis.
Citation: Electronics
PubDate: 2023-12-07
DOI: 10.3390/electronics12244929
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4930: A Reconfigurable Three-Dimensional
Electromagnetically Induced Transparency Metamaterial with Low Loss and
Large Group Delay
Authors: Pei Cheng, Zhongyin Xiao, Xuxian Jiang, Yulong Liu, Xianshun Cai
First page: 4930
Abstract: In this paper, a solid-state plasma (SSP) metamaterial for an analog of the electromagnetically induced transparency phenomenon is designed and investigated. This electromagnetically induced transparency metamaterial has the ability to interact with both incident electric and magnetic fields, and its low-loss characteristics, slow-wave effect, band reconfigurability, and polarization-insensitive characteristics are researched and explored. According to the tunable SSP, we have successfully implemented two modes of operation (mode 1 and mode 2) by whether the SSP resonance unit is excited or not. Low-loss characteristics and polarization-insensitive properties are achieved by rotating the split-ring resonator (SRR) by 180° in the plane and rotating the overall plane framework 90° to form a three-dimensional structure. After that, the maximum group delay of 261.51 ps and 785.09 ps as well as the delay bandwidth product of 17.51 and 62.96 at mode 1 and mode 2, respectively, are discussed respectively. This indicates a good slow-wave effect as well as a high efficiency of communication devices. After all, in mode 1, a transmission peak at 0.541 THz is observed for a transmission ratio of 92.05%; and in mode 2, a transmission peak at 0.741 THz is observed for a transmission ratio of 93.01%, resulting in a bandwidth shift of 0.2 THz. Due to the uniqueness of the developed metamaterial, it holds potential for a wide range of applications in slow-wave devices, modulators, sensors, and communications equipment.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244930
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4931: Li-Ion Battery Immersed Heat Pipe
Cooling Technology for Electric Vehicles
Authors: In-Taek Oh, Ji-Su Lee, Jin-Se Han, Seong-Woo Lee, Su-Jong Kim, Seok-Ho Rhi
First page: 4931
Abstract: Lithium-ion batteries, crucial in powering Battery Electric Vehicles (BEVs), face critical challenges in maintaining safety and efficiency. The quest for an effective Battery Thermal Management System (BTMS) arises from critical concerns over the safety and efficiency of lithium-ion batteries, particularly in Battery Electric Vehicles (BEVs). This study introduces a pioneering BTMS solution merging a two-phase immersion cooling system with heat pipes. Notably, the integration of NovecTM 649 as the dielectric fluid substantially mitigates thermal runaway-induced fire risks without requiring an additional power source. Comprehensive 1-D modeling, validated against AMESim (Advanced Modeling Environment for Simulation of Engineering Systems) simulations and experiments, investigates diverse design variable impacts on thermal resistance and evaporator temperature. At 10 W, 15 W, and 20 W heat inputs, the BTMS consistently maintained lithium-ion battery temperatures within the optimal range (approximately 27–34 °C). Optimized porosity (60%) and filling ratios (30–40%) minimized thermal resistance to 0.3848–0.4549 °C/W. This innovative system not only enhances safety but also improves energy efficiency by reducing weight, affirming its potential to revolutionize lithium-ion battery performance and address critical challenges in the field.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244931
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4932: Demonstration of a Frequency Doubler
Using a Tunnel Field-Effect Transistor with Dual Pocket Doping
Authors: Jang Hyun Kim, Hyunwoo Kim
First page: 4932
Abstract: In this study, a frequency doubler that consists of a tunnel field-effect transistor (TFET) with dual pocket doping is proposed, and its operation is verified using technology computer-aided design (TCAD) simulations. The frequency-doubling operation is important to having symmetrical current characteristics, which eliminate odd harmonics and the need for extra filter circuitry. The proposed TFET has intrinsically bidirectional and controllable currents that can be implemented by pocket doping, which is located at the junction between the source/drain (S/D) and the channel region, to modify tunneling probabilities. The source-to-channel (ISC) and channel-to-drain currents (ICD) can be independently changed by managing each pocket doping concentration on the source and drain sides (NS,POC and ND,POC). After that, the current matching process was investigated through NS,POC and ND,POC splits, respectively. However, it was found that the optimized doping condition achieved at the device level (namely, a transistor evaluation) is not suitable for a frequency doubler operation because the voltage drop generated by a load resistor in the frequency doubler circuit configuration causes the currents to be unbalanced between ISC and ICD. Therefore, after symmetrical current matching was performed by optimizing NS,POC and ND,POC at the circuit level, it was clearly seen that the output frequency was doubled in comparison to the input sinusoidal signal. In addition, the effects of the S/D and pocket doping variations that can occur during process integration were investigated to determine how much frequency multiplications are affected, and these variations have the immunity of S/D doping and pocket doping length changes. Furthermore, the impact of device scaling with gate length (LG) variations was evaluated. Based on these findings, the proposed frequency doubler is anticipated to offer benefits for circuit design and low-power applications compared to the conventional one.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244932
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4933: Applying the Lombard Effect to
Speech-in-Noise Communication
Authors: Gražina Korvel, Krzysztof Kąkol, Povilas Treigys, Bożena Kostek
First page: 4933
Abstract: This study explored how the Lombard effect, a natural or artificial increase in speech loudness in noisy environments, can improve speech-in-noise communication. This study consisted of several experiments that measured the impact of different types of noise on synthesizing the Lombard effect. The main steps were as follows: first, a dataset of speech samples with and without the Lombard effect was collected in a controlled setting; then, the frequency changes in the speech signals were detected using the McAulay and Quartieri algorithm based on a 2D speech representation; next, an average formant track error was computed as a metric to evaluate the quality of the speech signals in noise. Three image assessment methods, namely the SSIM (Structural SIMilarity) index, RMSE (Root Mean Square Error), and dHash (Difference Hash) were used for this purpose. Furthermore, this study analyzed various spectral features of the speech signals in relation to the Lombard effect and the noise types. Finally, this study proposed a method for automatic noise profiling and applied pitch modifications to neutral speech signals according to the profile and the frequency change patterns. This study used an overlap-add synthesis in the STRAIGHT vocoder to generate the synthesized speech.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244933
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4934: LPI Radar Signal Recognition Based on
Feature Enhancement with Deep Metric Learning
Authors: Feitao Ren, Daying Quan, Lai Shen, Xiaofeng Wang, Dongping Zhang, Hengliang Liu
First page: 4934
Abstract: Low probability of intercept (LPI) radar signals are widely used in electronic countermeasures due to their low power and large bandwidth. However, they are susceptible to interference from noise, posing challenges for accurate identification. To address this issue, we propose an LPI radar signal recognition method based on feature enhancement with deep metric learning. Specifically, time-domain LPI signals are first transformed into time–frequency images via the Choi–Williams distribution. Then, we propose a feature enhancement network with attention-based dynamic feature extraction blocks to fully extract the fine-grained features in time–frequency images. Meanwhile, we introduce deep metric learning to reduce noise interference and enhance the time–frequency features. Finally, we construct an end-to-end classification network to achieve the signal recognition task. Experimental results demonstrate that our method obtains significantly higher recognition accuracy under a low signal-to-noise ratio compared with other baseline methods. When the signal-to-noise ratio is −10 dB, the successful recognition rate for twelve typical LPI signals reaches 94.38%.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244934
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4935: System Architecture for Diagnostics and
Supervision of Industrial Equipment and Processes in an IoE Device
Environment
Authors: Marek Bolanowski, Andrzej Paszkiewicz, Tomasz Żabiński, Grzegorz Piecuch, Mateusz Salach, Krzysztof Tomecki
First page: 4935
Abstract: IoE components are becoming an integral part of our lives and support the operation of systems such as smart homes, smart cities, or Industry 4.0. The large number and variety of IoE components force the creation of flexible systems for data acquisition, processing, and analysis. The work presents a proposal for a new flexible architecture model and technology stack designed for the diagnostics and monitoring of industrial components and processes in an IoE device environment. The proposed solutions allow creating custom flexible systems for managing a distributed IoT environment, including the implementation of innovative mechanisms like, for example: predictive maintenance, anomaly detection, business intelligence, optimization of energy consumption, or supervision of the manufacturing process. In the present study, two detailed system architectures are proposed, and one of them was implemented. The developed system was tested in near-production conditions using a real IoT device infrastructure including industrial systems, drones, and sensor networks. The results showed that the proposed model of a central data-acquisition and -processing system allows the flexible integration of various IoE solutions and has a very high implementation potential wherever there is a need to integrate data from different sources and systems.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244935
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4936: Lightweight DB-YOLO Facemask Intelligent
Detection and Android Application Based on Bidirectional Weighted Feature
Fusion
Authors: Bin Qin, Ying Zeng, Xin Wang, Junmin Peng, Tao Li, Teng Wang, Yuxin Qin
First page: 4936
Abstract: Conventional facemask detection algorithms face challenges of insufficient accuracy, large model size, and slow computation speed, limiting their deployment in real-world scenarios, especially on edge devices. Aiming at addressing these issues, we proposed a DB-YOLO facemask intelligent detection algorithm, which is a lightweight solution that leverages bidirectional weighted feature fusion. Our method is built on the YOLOv5 algorithm model, replacing the original YOLOv5 backbone network with the lightweight ShuffleNetv2 to reduce parameters and computational requirements. Additionally, we integrated BiFPN as the feature fusion layer, enhancing the model’s detection capability for objects of various scales. Furthermore, we employed a CARAFE lightweight upsampling factor to improve the model’s perception of details and small-sized objects and the EIOU loss function to expedite model convergence. We validated the effectiveness of our proposed method through experiments conducted on the Pascal VOC2007+2012 and Face_Mask datasets. Our experimental results demonstrate that the DB-YOLO model boasts a compact size of approximately 1.92 M. It achieves average precision values of 70.1% and 93.5% on the Pascal VOC2007+2012 and Face_Mask datasets, respectively, showcasing a 2.3% improvement in average precision compared to the original YOLOv5s. Furthermore, the model’s size is reduced by 85.8%. We also successfully deployed the model on Android devices using the NCNN framework, achieving a detection speed of up to 33 frames per second. Compared to lightweight algorithm models like YOLOv5n, YOLOv4-Tiny, and YOLOv3-Tiny, DB-YOLO not only reduces the model’s size but also effectively improves detection accuracy, exhibiting excellent practicality and promotional value on edge devices.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244936
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4937: A Dynamic Emotional Propagation Model
over Time for Competitive Environments
Authors: Zhihao Chen, Bingbing Xu, Tiecheng Cai, Zhou Yang, Xiangwen Liao
First page: 4937
Abstract: Emotional propagation research aims to discover and show the laws of opinion evolution in social networks. The short-term observation of the emotional propagation process for a predetermined time window ignores situations in which users with different emotions compete over a long diffusion time. To that end, we propose a dynamic emotional propagation model based on an independent cascade. The proposed model is inspired by the interpretable factors of the reinforced Poisson process, portraying the “rich-get-richer” phenomenon within a social network. Specifically, we introduce a time-decay mechanism to illustrate the change in influence over time. Meanwhile, we propose an emotion-exciting mechanism allowing prior users to affect the emotions of subsequent users. Finally, we conduct experiments on an artificial network and two real-world datasets—Wiki, with 7194 nodes, and Bitcoin-OTC, with 5881 nodes—to verify the effectiveness of our proposed model. The proposed method improved the F1-score by 3.5% and decreased the MAPE by 0.059 on the Wiki dataset. And the F1-score improved by 0.4% and the MAPE decreased by 0.013 on the Bitcoin-OTC dataset. In addition, the experimental results indicate a phenomenon of emotions in social networks tending to converge under the influence of opinion leaders after a long enough time.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244937
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4938: Chirp Rate Estimation of LFM Signals
Based on Second-Order Synchrosqueezing Transform
Authors: Gangyi Zhai, Jianjiang Zhou, Kanglin Yu, Jiangtao Li
First page: 4938
Abstract: For the problem of low time-frequency aggregation of the short-time Fourier transform (STFT), which causes the parameter estimation performance degradation of linear frequency modulation (LFM) signals at low signal-to-noise ratio (SNR), second-order synchrosqueezing transform (SSST) is proposed based on the square of STFT amplitude. The time-frequency resolution and energy aggregation are improved by means of squeezing and reassigning the time-frequency spectrum. Meanwhile, in order to decrease the calculation of classical parameter estimation methods, the Hough transform is used for rough estimation, and then the fractional Fourier transform (FRFT) is used for accuracy estimation based on the Renyi entropy. The simulation result shows that higher estimation accuracy is obtained at low SNR, and it has better robustness.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244938
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4939: An Actor-Critic Hierarchical
Reinforcement Learning Model for Course Recommendation
Authors: Kun Liang, Guoqiang Zhang, Jinhui Guo, Wentao Li
First page: 4939
Abstract: Online learning platforms provide diverse course resources, but this often results in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, existing course recommendation methods face the challenge of accurately recommending the target course that is most relevant to the user because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the actor-critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendations effectively. Furthermore, we propose a policy gradient based on the temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and to improve the accuracy of the recommendation. We evaluate the proposed model using two real datasets, and the experimental results show that the proposed model significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244939
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4940: LezioSeg: Multi-Scale Attention
Affine-Based CNN for Segmenting Diabetic Retinopathy Lesions in Images
Authors: Mohammed Yousef Salem Ali, Mohammed Jabreel, Aida Valls, Marc Baget, Mohamed Abdel-Nasser
First page: 4940
Abstract: Diagnosing some eye pathologies, such as diabetic retinopathy (DR), depends on accurately detecting retinal eye lesions. Automatic lesion-segmentation methods based on deep learning involve heavy-weight models and have yet to produce the desired quality of results. This paper presents a new deep learning method for segmenting the four types of DR lesions found in eye fundus images. The method, called LezioSeg, is based on multi-scale modules and gated skip connections. It has three components: (1) Two multi-scale modules, the first is atrous spatial pyramid pooling (ASPP), which is inserted at the neck of the network, while the second is added at the end of the decoder to improve the fundus image feature extraction; (2) ImageNet MobileNet encoder; and (3) gated skip connection (GSC) mechanism for improving the ability to obtain information about retinal eye lesions. Experiments using affine-based transformation techniques showed that this architecture improved the performance in lesion segmentation on the well-known IDRiD and E-ophtha datasets. Considering the AUPR standard metric, for the IDRiD dataset, we obtained 81% for soft exudates, 86% for hard exudates, 69% for hemorrhages, and 40% for microaneurysms. For the E-ophtha dataset, we achieved an AUPR of 63% for hard exudates and 37.5% for microaneurysms. These results show that our model with affine-based augmentation achieved competitive results compared to several cutting-edge techniques, but with a model with much fewer parameters.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244940
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4941: Self-Supervised Health Index Curve
Generation for Condition-Based Predictive Maintenance
Authors: Steffen Seitz, Marvin Arnold, Ronald Tetzlaff, Peter Holstein
First page: 4941
Abstract: Modern machine degradation trend evaluation relies on the unsupervised model-based estimation of a health index (HI) from asset measurement data. This minimizes the need for timely human evaluation and avoids assumptions on the degradation shape. However, the comparability of multiple HI curves over time generated by unsupervised methods suffers from a scaling mismatch (non-coherent HIs) caused by the slightly different asset initial conditions and distinct HI model training. In this paper, we propose a novel self-supervised approach to obtain HI curves without suffering from the scale mismatch. Our approach uses an unsupervised autoencoder based on a convolutional neural network (CNN) to detect initial faults and autonomously label measurement samples. The resulting self-labeled data is used to train a 1D-CNN health predictor, effectively eliminating the scaling mismatch problem. On the basis of a bearing test-to-failure experiment, we show that our self-supervised scheme offers a promising solution for the non-coherent HI problem. In addition, we observed that our method indicates the gradual wear affecting the bearing prior to the independent analysis of a human expert.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244941
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4942: A Novel Global Routing Algorithm for
Printed Circuit Boards Based on Triangular Grid
Authors: Jiarui Chen, Yujing Zhou, Qinghai Liu, Xinhong Zhang
First page: 4942
Abstract: Global routing plays a crucial role in printed circuit board (PCB) design and affects the cost of the design significantly. Conventional methods based on rectangular grids have some limitations, whereas this paper introduces a new algorithm that employs a triangular grid model, which offers a more efficient solution to the problem. Firstly, we present a technique to sort all unconnected two-pin nets. Next, a triangular grid graph is constructed to represent the routing resources on the printed circuit board. Finally, we use the concept of maximum flow to identify the paths for global routing and apply detailed routing for the completion of wires. Results from experiments demonstrate that our algorithm is faster than two state-of-the-art routers and does not have any design rule violations for all industrial PCB instances.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244942
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4943: WRA-MF: A Bit-Level
Convolutional-Weight-Decomposition Approach to Improve Parallel Computing
Efficiency for Winograd-Based CNN Acceleration
Authors: Siwei Xiang, Xianxian Lv, Yishuo Meng, Jianfei Wang, Cimang Lu, Chen Yang
First page: 4943
Abstract: FPGA-based convolutional neural network (CNN) accelerators have been extensively studied recently. To exploit the parallelism of multiplier–accumulator computation in convolution, most FPGA-based CNN accelerators heavily depend on the number of on-chip DSP blocks in the FPGA. Consequently, the performance of the accelerators is restricted by the limitation of the DSPs, leading to an imbalance in the utilization of other FPGA resources. This work proposes a multiplication-free convolutional acceleration scheme (named WRA-MF) to relax the pressure on the required DSP resources. Firstly, the proposed WRA-MF employs the Winograd algorithm to reduce the computational density, and it then performs bit-level convolutional weight decomposition to eliminate the multiplication operations. Furthermore, by extracting common factors, the complexity of the addition operations is reduced. Experimental results on the Xilinx XCVU9P platform show that the WRA-MF can achieve 7559 GOP/s throughput at a 509 MHz clock frequency for VGG16. Compared with state-of-the-art works, the WRA-MF achieves up to a 3.47×–27.55× area efficiency improvement. The results indicate that the proposed architecture achieves a high area efficiency while ameliorating the imbalance in the resource utilization.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244943
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4944: EEG Topography Amplification Using
FastGAN-ASP Method
Authors: Min Zhao, Shuai Zhang, Xiuqing Mao, Lei Sun
First page: 4944
Abstract: Electroencephalogram (EEG) signals are bioelectrical activities generated by the central nervous system. As a unique information factor, they are correlated with the genetic information of the subjects, exhibiting robustness against forgery. The development of biometric identity recognition based on EEG signals has significantly improved the security and accuracy of biometric recognition. However, EEG signals obtained from incompatible acquisition devices have low universality and are prone to noise, making them challenging for direct use in practical identity recognition scenarios. Employing deep learning network models for data augmentation can address the issue of data scarcity. Yet, the time–frequency–space characteristics of EEG signals pose challenges for extracting features and efficiently generating data with deep learning models. To tackle these challenges, this paper proposes a data generation method based on channel attention normalization and spatial pyramid in a generative adversative network (FastGAN-ASP). The method introduces attention mechanisms in both the generator and discriminator to locate crucial feature information, enhancing the training performance of the generative model for EEG data augmentation. The EEG data used here are preprocessed EEG topographic maps, effectively representing the spatial characteristics of EEG data. Experiments were conducted using the BCI Competition IV-Ⅰ and BCI Competition IV-2b standard datasets. Quantitative and usability evaluations were performed using the Fréchet inception distance (FID) metric and ResNet-18 classification network, validating the quality and usability of the generated data from both theoretical and applied perspectives. The FID metric confirmed that FastGAN-ASP outperforms FastGAN, WGAN-GP, and WGAN-GP-ASP in terms of performance. Moreover, utilizing the dataset augmented with this method for classification recognition achieved an accuracy of 95.47% and 92.43%.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244944
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4945: Multi-Vehicle Navigation Using
Cooperative Localization
Authors: Juan Carlos Oliveros, Hashem Ashrafiuon
First page: 4945
Abstract: This paper assesses the effectiveness of cooperative localization for improving the performance of closed-loop control systems for networks for autonomous multi-vehicle navigation. Nonlinear dynamic models of two- and three-dimensional vehicles are presented along with their linearized forms. A nonlinear control algorithm is then presented based on the dynamic model. Relative position measurement equations and their linearized forms are introduced. The state and measurement equations are then employed for the propagation and update steps of an EKF-based cooperative localization algorithm. Initially, a series of experiments with networks of quadcopters and mobile robots are presented to validate the performance of cooperative localization for state estimation with the continuous or intermittent presence of absolute measurements or their complete absence. Finally, the performance of the control algorithm is evaluated with and without cooperative localization to demonstrate its effectiveness for improving performance.
Citation: Electronics
PubDate: 2023-12-08
DOI: 10.3390/electronics12244945
Issue No: Vol. 12, No. 24 (2023)
- Electronics, Vol. 12, Pages 4846: Augmented Grad-CAM++: Super-Resolution
Saliency Maps for Visual Interpretation of Deep Neural Network
Authors: Yongshun Gao, Jie Liu, Weihan Li, Ming Hou, Yang Li, Huimin Zhao
First page: 4846
Abstract: In recent years, deep neural networks have shown superior performance in various fields, but interpretability has always been the Achilles’ heel of deep neural networks. The existing visual interpretation methods for deep neural networks still suffer from inaccurate and insufficient target localization and low-resolution saliency maps. To address the above issues, this paper presents a saliency map generation method based on image geometry augmentation and super-resolution called augmented high-order gradient weighting class activation mapping (augmented grad-CAM++). Unlike previous approaches that rely on a single input image to generate saliency maps, this method first introduces the image geometry augmentation technique to create a set of augmented images for the input image and generate activation mappings separately. Secondly, the augmented activation mappings are combined to form the final saliency map. Finally, a super-resolution technique is introduced to add pixel points to reconstruct the saliency map pixels to improve the resolution of the saliency map. The proposed method is applied to analyze standard image data and industrial surface defect images. The results indicate that, in experiments conducted on standard image data, the proposed method achieved a 3.1% improvement in the accuracy of capturing target objects compared to traditional methods. Furthermore, the resolution of saliency maps was three times higher than that of traditional methods. In the application of industrial surface defect detection, the proposed method demonstrated an 11.6% enhancement in the accuracy of capturing target objects, concurrently reducing the false positive rate. The presented approach enables more accurate and comprehensive capture of target objects with higher resolution, thereby enhancing the visual interpretability of deep neural networks. This improvement contributes to the greater interpretability of deep learning models in industrial applications, offering substantial performance gains for the practical deployment of deep learning networks in the industrial domain.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234846
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4847: A Clone Selection Algorithm Optimized
Support Vector Machine for AETA Geoacoustic Anomaly Detection
Authors: Qiyi He, Han Wang, Changyi Li, Wen Zhou, Zhiwei Ye, Liang Hong, Xinguo Yu, Shengjie Yu, Lu Peng
First page: 4847
Abstract: Anomaly in geoacoustic emission is an important earthquake precursor. Current geoacoustic anomaly detection methods are limited by their low signal-to-noise ratio, low intensity, sample imbalance, and low accuracy. Therefore, this paper proposes a clone selection algorithm optimized one-class support vector machine method (CSA-OCSVM) for geoacoustic anomaly detection. First, the interquartile range (IQR), cubic spline interpolation, and time window are designed to amplify the geoacoustic signal intensity and energy change rules to reduce the interference of geoacoustic signal noise and intensity. Secondly, to address the imbalance of positive and negative samples in geoacoustic anomaly detection, a one-class support vector machine is introduced for anomaly detection. Meanwhile, in view of the optimization capabilities of the clone selection algorithm, it is adopted to optimize the hyperparameters of OCSVM to improve its detection accuracy. Finally, the proposed model is applied to geoacoustic data anomaly detection in nine different datasets, which are derived from our self-developed acoustic electromagnetic to AI (AETA) system, to verify its effectiveness. By designing comparative experiments with IQR, genetic algorithm OCSVM (GA-OCSVM), particle swarm optimization OCSVM (PSO-OCSVM), and evaluating the performance of the true positive rate (TPR) and false positive rate (FPR), the experimental results depict that the proposed model is superior to the existing state-of-the-art geoacoustic anomaly detection approaches.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234847
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4848: Model-Free Predictive Current Control of
Five-Phase PMSM Drives
Authors: Wentao Huang, Yijia Huang, Dezhi Xu
First page: 4848
Abstract: Model predictive control is highly dependent on accurate models and the parameters of electric motor drives. Multiphase permanent magnet synchronous motors (PMSMs) contain nonlinear parameters and mutual cross-coupling dynamics, resulting in challenges in modeling and parameter acquisition. To lessen the parameter dependence of current predictions, a model-free predictive current control (MFPCC) strategy based on an ultra-local model and motor outputs is proposed for five-phase PMSM drives. The ultra-local model is constructed according to the differential equation of current. The inherent relation between the parameters in the predictive current model and the ultra-local model is analyzed in detail. The unknowns of the ultra-local model are estimated using the motor current and voltage at different time instants without requiring motor parameters or observers. Moreover, space vector modulation technology is employed to minimize the voltage tracking error. Finally, simulations and experiments are conducted to verify the effectiveness of the MFPCC with space vector modulation. The results confirm that the proposed method can effectively eliminate the impact of motor parameters and improve steady-state performance. Moreover, this control strategy demonstrates good robustness against load variations.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234848
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4849: Influence of JFET Width on Short-Circuit
Robustness of 1200 V SiC Power MOSFETs
Authors: Hongyi Xu, Baozhu Wang, Na Ren, Hu Long, Kai Huang, Kuang Sheng
First page: 4849
Abstract: This paper investigates and compares the static performance and short-circuit (SC) robustness of 1200 V SiC MOSFETs with varying JFET widths (WJFET = 2.0–5.0 μm). Short-circuit measurements as well as electrical-thermal simulations are used to identify thermal distribution and maximum electrical field, providing valuable insights into the design limits. The devices under test (DUTs) with narrow and wide WJFET exhibit different failure mechanisms under SC stress. After the short-circuit failure, interlayer dielectric (ILD) cracks are observed in DUTs with narrow JFET width (WJFET < 3 μm). In contrast, it is discovered that the burn mark is located in the channel region of the device with a wide JFET width. Moreover, the short-circuit withstand time (SCWT) of DUTs with narrow and wide WJFET exhibits varying trends under high temperature conditions (100 °C). These results can help verify the different failure mechanisms and determine an optimal JFET design to improve the trade-off between the static performance and SC ruggedness of the SiC MOSFETs.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234849
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4850: Voice-Controlled Intelligent Personal
Assistant for Call-Center Automation in the Uzbek Language
Authors: Abdinabi Mukhamadiyev, Ilyos Khujayarov, Jinsoo Cho
First page: 4850
Abstract: The demand for customer support call centers has surged across various sectors due to the pandemic. Yet, the constraints of round-the-clock human services and fluctuating wait times pose challenges in fully meeting customer needs. In response, there’s a growing need for automated customer service systems that can provide responses tailored to specific domains and in the native languages of customers, particularly in developing nations like Uzbekistan where call center usage is on the rise. Our system, “UzAssistant,” is designed to recognize user voices and accurately present customer issues in standardized Uzbek, as well as vocalize the responses to voice queries. It employs feature extraction and recurrent neural network (RNN)-based models for effective automatic speech recognition, achieving an impressive 96.4% accuracy in real-time tests with 56 participants. Additionally, the system incorporates a sentence similarity assessment method and a text-to-speech (TTS) synthesis feature specifically for the Uzbek language. The TTS component utilizes the WaveNet architecture to convert text into speech in Uzbek.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234850
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4851: Practical and Malicious Multiparty
Private Set Intersection for Small Sets
Authors: Ji Zhou, Zhusen Liu, Luyao Wang, Chuan Zhao, Zhe Liu, Lu Zhou
First page: 4851
Abstract: Private set intersection (PSI) is a pivotal subject in the realm of privacy computation. Numerous research endeavors have concentrated on situations involving vast and imbalanced sets. Nevertheless, there is a scarcity of existing PSI protocols tailored for small sets. Those that exist are either restricted to interactions between two parties or necessitate resource-intensive homomorphic operations. To bring forth practical multiparty private set intersection solutions for small sets, we present two multiparty PSI protocols founded on the principles of Oblivious Key–Value Stores (OKVSs), polynomials, and gabled cuckoo tables. Our security analysis underscores the resilience of these protocols against malicious models and collision attacks. Through experimental evaluations, we establish that, in comparison to related endeavors, our protocols excel in small-set contexts, particularly in low-bandwidth wide area network (WAN) settings.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234851
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4852: Low-Profile UWB-MIMO Antenna System with
Enhanced Isolation Using Parasitic Elements and Metamaterial Integration
Authors: Yamina Tighilt, Chahrazed Bensid, Djamel Sayad, Samira Mekki, Rami Zegadi, Mohamed Lamine Bouknia, Issa Elfergani, Pankaj Singh, Jonathan Rodriguez, Chemseddine Zebiri
First page: 4852
Abstract: A new compact UWB multiple-input–multiple-output (MIMO) antenna is presented in this paper. The proposed antenna, with a compact size of 30 × 20 × 1.6 mm3, consists of a two-element microstrip line-fed pentagonal-shaped patch associated with a parasitic element and a partial ground plane. Three complementary split-ring resonator (CSRR) structures are integrated into the defected ground with the aims of reducing the mutual coupling and enhancing the bandwidth. A UWB impedance bandwidth is achieved covering the FCC band (3.1–10.6 GHz), corresponding to a reflection coefficient below −10 dB and a reduced mutual coupling below −22 dB. Additionally, acceptable limits of the diversity performance parameters are obtained. Furthermore, all the simulated outcomes of the suggested antenna are convenient for UWB MIMO wireless applications. Measures carried out on the fabricated prototype of the antenna demonstrate good agreement between both the simulation and measurement results of the optimized two-port MIMO antenna.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234852
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4853: BPath-RO: A Performance- and
Area-Efficient In Situ Delay Measurement Scheme for Digital IC
Authors: Danqing Li, Huaguo Liang, Hong Zhang, Yue Wang, Maoxiang Yi, Yingchun Lu, Zhengfeng Huang
First page: 4853
Abstract: Circuit delays are increasingly sensitive to process, voltage, temperature, and aging (PVTA) variations, severely impacting circuit performance. Accurate measurement of circuit delay is essential. However, the additional hardware structures for measuring circuit delay add to the critical path delay. To address this issue, this paper proposes a bypass-based ring oscillator (BPath-RO) that reduces the impact on the critical path delay by moving the added measurement control structures to the bypass. The proposed measurement scheme requires only two transistors inserted into the critical path, which is more conducive to engineering change order (ECO). Measurement simulation experiments were performed on representative critical paths of the ISCAS’89 s298 and ITC’99 b15 benchmark circuits. The experimental results show that, in comparison with the existing architectures, the Bpath-RO delay measurement scheme improves the circuit performance by an average of 13.81% (s298) and 3.47% (b15) and reduces the hardware overhead by up to 70% for each path.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234853
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4854: Attribute-Based Proxy Signature Scheme
Supporting Flexible Threshold Predicate for UAV Networks
Authors: Lei He, Yong Gan, Yanhua Zhang
First page: 4854
Abstract: Unmanned aerial vehicle (UAV) is an attractive application because of its flexibility and economy. It may use a digital signature scheme to protect commands sent to UAVs. Moreover, the digital signature scheme should guarantee the real-time performance of UAVs executing commands and protect the signer’s privacy. Therefore, we proposed an attribute-based proxy signature (ABPS) scheme supporting flexible threshold predicate for UAV networks and proved its security. It has existential unforgeability under selective-predicate and chosen message attacks (EUF-sP-CMA) and can protect the signer’s privacy. We analyzed its computation costs based on experimental data and communication costs. The analysis results indicate that our ABPS scheme has less computation costs than other ABPS schemes and is at the same level as other ABPS schemes on communication costs.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234854
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4855: Dynamic Dead-Time Compensation Method
Based on Switching Characteristics of the MOSFET for PMSM Drive System
Authors: Xi Liu, Hui Li, Yingzhe Wu, Lisheng Wang, Shan Yin
First page: 4855
Abstract: In order to effectively avoid the shoot-through issue of the semiconductor device (such as the power metal-oxide-semiconductor field-effect transistor (MOSFET)) adopted in the phase leg of the motor drives, a dead-time zone should be inserted. However, the nonlinearity caused by the dead-time effect will bring about voltage/current distortion, as well as high-order harmonics, which largely degrades the performance of motor drives, especially in low-speed operations with slight loads. In this paper, a dead-time compensation method is proposed to suppress such side effects caused by dead-time zones and improve the performance of motor drives. Compared with other existing methods, the proposed method is mainly focused on the switching characteristics of the power MOSFET, which is directly relative to the compensation time in each pulse-width modulation (PWM) period. Firstly, a detailed derivation process is elaborated to reveal the relationship between compensation time and the switching performance of the MOSFET. Meanwhile, the switching process of the MOSFET is also well analyzed, which summarizes the variations in the switching time of the MOSFET with a varied load current. Then, the multipulse test (MPT) is carried out to obtain accurate values of the switching time with the varied load current in a wide range (0–80 A) and form a 2D lookup table. As a result, the compensation method can easily be realized by combining the lookup table and linear interpolation based on the phase current of the motor dynamically. Finally, the effectiveness of the proposed method is verified based on a 12 V permanent magnet synchronous machine (PMSM) drive system. According to the relative experiment results, it can be clearly observed that the time-domain waveform distortion, high-order harmonics, and total harmonic distortion (THD) value are reduced significantly with the proposed dynamic compensation method.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234855
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4856: A Study on Enhancing the Information
Security of Urban Traffic Control Systems Using Evolutionary Game Theory
Authors: Ke Pan, Li Wang, Lingyu Zhang
First page: 4856
Abstract: In recent years, there has been significant development in intelligent technologies for urban traffic control, such as smart city and vehicle-to-everything (V2X) communication. These advancements aim to provide more efficient and convenient services to participants in urban transportation. As the urban traffic control (UTC) system integrates with various networks and physical infrastructure, the potential threats of malicious attacks and breaches pose significant risks to the safety of individuals and their properties. To address this issue, this academic paper focuses on studying the network structure of the UTC system. A signal security game model is constructed based on the concepts of evolutionary game theory (EGT), involving three parties: attackers, upper computers (UC), and traffic signal machines (TSM). The model aims to analyze the evolutionary stability of the strategies chosen by each party, and to explore the relationships between various factors and the strategy choices of the three parties. Furthermore, the stability of equilibrium points in the three-party game system is analyzed using the Liapunov method. The conditions in which UC and TSM, dependent on detection rates and defense costs, choose to abandon defense at pure-strategy equilibrium points were obtained. Finally, MATLAB is utilized for simulation analysis to validate the impact of attack costs, defense costs, and detection rates on the information security of UTC systems.
Citation: Electronics
PubDate: 2023-11-30
DOI: 10.3390/electronics12234856
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4857: A Precise Calibration Method for the
Robot-Assisted Percutaneous Puncture System
Authors: Jinbiao Li, Minghui Li, Quan Zeng, Cheng Qian, Tao Li, Shoujun Zhou
First page: 4857
Abstract: The precision and stability of the Robot-Assisted Percutaneous Puncture (RAPP) system have become increasingly crucial with the widespread integration of robotic technology in the field of medicine. The accurate calibration of the RAPP system prior to surgery significantly influences target positioning performance. This study proposes a novel system calibration method that simultaneously addresses system hand–eye calibration and robot kinematic parameters calibration, thereby enhancing the surgery success rate and ensuring patient safety. Initially, a Closed-loop Hand–eye Calibration (CHC) method is employed to rapidly establish transformation relationships among system components. These CHC results are then integrated with nominal robot kinematic parameters to preliminarily determine the system calibration parameters. Subsequently, a hybrid algorithm, combining the regularized Levenberg–Marquardt (LM) algorithm and a particle filtering algorithm, is utilized to accurately estimate the system calibration parameters in stages. Numerical simulations and puncture experiments were conducted using the proposed system calibration method and other comparative methods. The experimental results revealed that, among several comparative methods, the approach presented in this paper yields the greatest improvement in the puncture accuracy of the RAPP system, demonstrating the accuracy and effectiveness of this method. In conclusion, this calibration method significantly contributes to enhancing the precision, operational capability, and safety of the RAPP system in practical applications.
Citation: Electronics
PubDate: 2023-12-01
DOI: 10.3390/electronics12234857
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4858: A Comprehensive Survey on Wi-Fi Sensing
for Human Identity Recognition
Authors: Pengsong Duan, Xianguang Diao, Yangjie Cao, Dalong Zhang, Bo Zhang, Jinsheng Kong
First page: 4858
Abstract: In recent years, Wi-Fi sensing technology has become an emerging research direction of human–computer interaction due to its advantages of low cost, contactless, illumination insensitivity, and privacy preservation. At present, Wi-Fi sensing research has been expanded from target location to action recognition and identity recognition, among others. This paper summarizes and analyzes the research of Wi-Fi sensing technology in human identity recognition. Firstly, we overview the history of Wi-Fi sensing technology, compare it with traditional identity-recognition technologies and other wireless sensing technologies, and highlight its advantages for identity recognition. Secondly, we introduce the steps of the Wi-Fi sensing process in detail, including data acquisition, data pre-processing, feature extraction, and identity classification. After that, we review state-of-the-art approaches using Wi-Fi sensing for single- and multi-target identity recognition. In particular, three kinds of approaches (pattern-based, model-based, and deep learning-based) for single-target identity recognition and two kinds of approaches (direct recognition and separated recognition) for multi-target identity recognition are introduced and analyzed. Finally, future research directions are discussed, which include transfer learning, improved multi-target recognition, and unified dataset construction.
Citation: Electronics
PubDate: 2023-12-01
DOI: 10.3390/electronics12234858
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4859: A Deep Learning Approach for Speech
Emotion Recognition Optimization Using Meta-Learning
Authors: Lara Toledo Cordeiro Ottoni, André Luiz Carvalho Ottoni, Jés de Jesus Fiais Cerqueira
First page: 4859
Abstract: Speech emotion recognition (SER) is widely applicable today, benefiting areas such as entertainment, robotics, and healthcare. This emotional understanding enhances user-machine interaction, making systems more responsive and providing more natural experiences. In robotics, SER is useful in home assistance devices, eldercare, and special education, facilitating effective communication. Additionally, in healthcare settings, it can monitor patients’ emotional well-being. However, achieving high levels of accuracy is challenging and complicated by the need to select the best combination of machine learning algorithms, hyperparameters, datasets, data augmentation, and feature extraction methods. Therefore, this study aims to develop a deep learning approach for optimal SER configurations. It delves into the domains of optimizer settings, learning rates, data augmentation techniques, feature extraction methods, and neural architectures for the RAVDESS, TESS, SAVEE, and R+T+S (RAVDESS+TESS+SAVEE) datasets. After finding the best SER configurations, meta-learning is carried out, transferring the best configurations to two additional datasets, CREMA-D and R+T+S+C (RAVDESS+TESS+SAVEE+CREMA-D). The developed approach proved effective in finding the best configurations, achieving an accuracy of 97.01% for RAVDESS, 100% for TESS, 90.62% for SAVEE, and 97.37% for R+T+S. Furthermore, using meta-learning, the CREMA-D and R+T+S+C datasets achieved accuracies of 83.28% and 90.94%, respectively.
Citation: Electronics
PubDate: 2023-12-01
DOI: 10.3390/electronics12234859
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4860: Specific Point in Time Excitation
Control Method for Spatial Multi-Degree-of-Freedom Systems under
Continuous Operation
Authors: Shengtao Zhang, Yixiao Qin
First page: 4860
Abstract: The port container gantry crane studied in this paper is a four-degree-of-freedom spatial continuous system. In actual work, in order to make the container transfer smoothly, the response of the whole system needs to be accurately predicted and timely adjusted. The whole system is divided into rotary mechanism, lifting mechanism, lifting trolley mechanism, and big cart mechanism for detailed analysis. By constructing the field transfer matrix, a one-dimensional wave equation of continuous system and the Lagrange equation with redundant parameters, the response of each subsystem is solved precisely. The results of the study found that in some periods, the swing of the container was too large. In order to improve the safety and stability of transmission, an active control method of specific point in time excitation (SPE) is proposed for the first time. This method predicts the swing amplitude of the container in advance using the response results of the numerical model. When the set response interval is exceeded, the external excitation intervention can effectively inhibit the moving range of the container in the transit process. Finally, the results are compared with the simulation model to achieve the experimental purpose. It is in line with the expected experimental effect.
Citation: Electronics
PubDate: 2023-12-01
DOI: 10.3390/electronics12234860
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4861: Research on the Signal Noise Reduction
Method of Fish Electrophysiological Behavior Based on CEEMDAN with
Improved Wavelet Thresholding
Authors: Jingfei Meng, Weiming Cai, Siyi Ou, Jian Zhao, Shengli Fan, Bicong Zheng
First page: 4861
Abstract: Electrophysiological signals are one of the key ways that fish convey information and govern movement. Changes in physiological electrical signals may indirectly reflect changes in fish sensory thresholds and locomotor behavior. The acquisition of physiological electrical signals in fish is more susceptible than in mammals to the effects of surface mucus and water noise, thereby reducing signal quality. In this study, a noise reduction method for electrophysiological behavioral signals in fish was proposed, namely the decomposition of the original EMG signal into multiple intrinsic mode components using CEEMDAN. To choose the signal-dominated IMF, noise-dominated IMF, and pure IMF, mutual correlation function characteristic analysis is done on each IMF and the original signal. The signal-dominated IMF is then filtered using the improved wavelet thresholding approach. Finally, the wavelet threshold filtered signal-dominated IMF with pure IMF was reconstructed into the processed fish EMG signal. It is demonstrated that the algorithm proposed in this paper improves the SNR by 3.1977 dB and reduces the RMSE by 0.0235 when compared to the traditional wavelet threshold denoising. The denoising method proposed in this paper can effectively improve the signal quality and provides an effective tool for the in-depth analysis of fish behavior from the perspective of physiological electrical signals.
Citation: Electronics
PubDate: 2023-12-01
DOI: 10.3390/electronics12234861
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4862: Switching Capacitor Filter with Multiple
Functions, Adjustable Bandwidth in the Range of 5 Hz–10 kHz
Authors: Fan He, Yubo Yuan, Jinjin Xiao, Zehao Han, Yingchun Fu, Shuilong Huang
First page: 4862
Abstract: This article proposes a second-order switch-capacitor filter that integrates low-pass, high-pass, band-pass, band-stop, and all-pass, and achieves flexible bandwidth adjustment of the filter through clock rate and capacitance ratio. The final filter design consists of two completely independent second-order switch-capacitor filter channels, and a 4-order Butterworth low-pass filter is designed through two-stage cascades. The two completely independent second-order switch-capacitor filters are integrated on a single chip and manufactured using the Huahong BCD350GE high-voltage 24 V process. The measurement results indicate that the proposed switch-capacitor filter achieves various functional filtering characteristics and achieves a bandwidth of 5 Hz to 10 kHz. The chip area is 5.1 × 3.1 mm2, powered by a dual power supply of ± 5 V, and the power consumption is 80 mW.
Citation: Electronics
PubDate: 2023-12-01
DOI: 10.3390/electronics12234862
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4863: A Novel Relocalization Method-Based
Dynamic Steel Billet Flaw Detection and Marking System
Authors: Hongxing Zhou, Juan Chen, Qinghan Hu, Xue Zhao, Zhiqing Li
First page: 4863
Abstract: In the current steel production process, occasional flaws within the billet are somewhat inevitable. Overlooking these flaws can compromise the quality of the resulting steel products. To address and mark these flaws for further handling, Magnetic Particle Testing (MT) in conjunction with machine vision is commonly utilized. This method identifies flaws on the billet’s surface and subsequently marks them via a device, eliminating the need for manual intervention. However, certain processes, such as magnetic particle cleaning, require substantial spacing between the vision system and the marking device. This extended distance can lead to shifts in the billet position, thereby potentially affecting the precision of flaw marking. In response to this challenge, we developed a detection-marking system consisting of 2D cameras, a manipulator, and an integrated 3D camera to accurately pinpoint the flaw’s location. Importantly, this system can be integrated into active production lines without causing disruptions. Experimental assessments on dynamic billets substantiated the system’s efficacy and feasibility.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234863
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4864: A Novel Unsupervised Outlier Detection
Algorithm Based on Mutual Information and Reduced Spectral Clustering
Authors: Yuehua Huang, Wenfen Liu, Song Li, Ying Guo, Wen Chen
First page: 4864
Abstract: Outlier detection is an essential research field in data mining, especially in the areas of network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier detection algorithms, which can be divided into supervised methods and unsupervised methods, suffer from the following problems: curse of dimensionality, lack of labeled data, and hyperparameter tuning. To address these issues, we present a novel unsupervised outlier detection algorithm based on mutual information and reduced spectral clustering, called MISC-OD (Mutual Information and reduced Spectral Clustering—Outlier Detection). MISC-OD first constructs a mutual information matrix between features, then, by applying reduced spectral clustering, divides the feature set into subsets, utilizing the LOF (Local Outlier Factor) for outlier detection within each subset and combining the outlier scores found within each subset. Finally, it outputs the outlier score. Our contributions are as follows: (1) we propose a novel outlier detection method called MISC-OD with high interpretability and scalability; (2) numerous experiments on 18 benchmark datasets demonstrate the superior performance of the MISC-OD algorithm compared with eight state-of-the-art baselines in terms of ROC (receiver operating characteristic) and AP (average precision).
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234864
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4865: Mobile Sensoring Data Verification via a
Pairing-Free Certificateless Signature Secure Approach against Novel
Public Key Replacement Attacks
Authors: Guilin Wang, Hua Shen, Liquan Chen, Jinguang Han, Ge Wu
First page: 4865
Abstract: To achieve flexible sensing coverage with low deployment costs, mobile users need to contribute their equipment as sensors. Data integrity is one of the most fundamental security requirements and can be verified by digital signature techniques. In the mobile crowdsensing (MCS) environment, most sensors, such as smartphones, are resource-limited. Therefore, many traditional cryptographic algorithms that require complex computations cannot be efficiently implemented on these sensors. In this paper, we study the security of certificateless signatures, in particular, some constructions without pairing. We notice that there is no secure pairing-free certificateless signature scheme against the super adversary. We also find a potential attack that has not been fully addressed in previous studies. To handle these two issues, we propose a concrete secure construction that can withstand this attack. Our scheme does not rely on pairing operations and can be applied in scenarios where the devices’ resources are limited.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234865
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4866: A 22.3-Bit Third-Order Delta-Sigma
Modulator for EEG Signal Acquisition Systems
Authors: Qianqian Wang, Fei Liu, Liyin Fu, Qianhui Li, Jing Kang, Ke Chen, Zongliang Huo
First page: 4866
Abstract: This paper presents a high resolution delta-sigma modulator for continuous acquisition of electroencephalography (EEG) signals. The third-order single-loop architecture with a 1-bit quantizer is adopted to achieve 22.3-bit resolution. The effects of thermal noise on the performance of the delta-sigma modulator are analyzed to reasonably allocate the switched-capacitor sizes for optimal signal to noise ratio (SNR) and minimum chip area. The coefficients in feedback path and input path are optimized to avoid the signal distortion under the full-scale input voltage range with almost no increase in total capacitance sizes. Fabricated in 0.5 µm CMOS technology and powered by a 5 V voltage supply, the proposed delta-sigma modulator can achieve 136 dB peak SNR with 16 Hz input and 137 dB dynamic range in 100 Hz signal bandwidth with an oversampling ratio of 512. The modulator dissipates 700 µA. The core chip area is 1.96 mm2. The modulator occupies 1.41 mm2 and the decimator occupies 0.55 mm2.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234866
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4867: RLARA: A TSV-Aware Reinforcement
Authors: Jiajia Jiao, Ruirui Shen, Lujian Chen, Jin Liu, Dezhi Han
First page: 4867
Abstract: A three-dimensional Network-on-Chip (3D NoC) equips modern multicore processors with good scalability, a small area, and high performance using vertical through-silicon vias (TSV). However, the failure rate of TSV, which is higher than that of horizontal links, causes unpredictable topology variations and requires adaptive routing algorithms to select the available paths dynamically. Most works have aimed at the congestion control for TSV partially 3D NoCs to bypass the TSV reliability issue, while others have focused on the fault tolerance in TSV fully connected 3D NoCs and ignored the performance degradation. In order to adequately improve reliability and performance in TSV fully connected 3D NoC architectures, we propose a TSV-aware Reinforcement Learning Assisted Routing Algorithm (RLARA) for fault-tolerant 3D NoCs. The proposed method can take advantage of both the high throughput of fully connected TSVs and the cost-effective fault tolerance of partially connected TSVs using periodically updated TSV-aware Q table of reinforcement learning. RLARA makes the distributed routing decision with the lowest TSV utilization to avoid the overheating of the TSVs and mitigate the reliability problem. Furthermore, the K-means clustering algorithm is further adopted to compress the routing table of RLARA by exploiting the routing information similarity. To alleviate the inherent deadlock issue of adaptive routing algorithms, the link Q-value from reinforcement learning is combined with the router status based in buffer utilization to predict the congestion and enable RLARA to perform best even under a high traffic load. The experimental results of the ablation study on simulator Garnet 2.0 verify the effectiveness of our proposed RLARA under different fault models, which can perform better than the latest 3D NoC routing algorithms, with up to a 9.04% lower average delay and 8.58% higher successful delivered rate.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234867
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4868: KULF-TT53: A Display-Specific
Turntable-Based Light Field Dataset for Subjective Quality Assessment
Authors: Kamran Javidi, Maria G. Martini, Peter A. Kara
First page: 4868
Abstract: Light field datasets enable researchers to conduct both objective and subjective quality assessments, which are particularly useful when acquisition equipment or resources are not available. Such datasets may vary in terms of capture technology and methodology, content, quality characteristics (e.g., resolution), and the availability of subjective ratings. When contents of a light field dataset are visualized on a light field display, the display system matches the received input to its output capabilities through various processes, such as interpolation. Therefore, one of the most straightforward methods to create light field contents for a specific display is to consider its visualization parameters during acquisition. In this paper, we introduce a novel display-specific light field dataset, captured using a DSLR camera and a turntable rig. The visual data of the seven static scenes were recorded twice by using two settings of angular resolution. While both were acquired uniformly within a 53-degree angle, which matches the viewing cone of the display they were captured for, one dataset consists of 70 views per content, while the other of 140. Capturing the contents twice was a more straightforward solution than downsampling, as the latter approach could either degrade the quality or make the FOV size inaccurate. The paper provides a detailed characterization of the captured contents, as well as compressed variations of the contents with various codecs, together with the calculated values of commonly-used quality metrics for the compressed light field contents. We expect that this dataset will be useful for the research community working on light field compression, processing, and quality assessment, for instance to perform subjective quality assessment tests on a display with a 53-degree display cone and to test new interpolation methods and objective quality metrics. In future work, we will also focus on subjective tests and provide relevant results. This dataset is made free to access for the research community.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234868
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4869: Knowledge-Aware Graph Self-Supervised
Learning for Recommendation
Authors: Shanshan Li, Yutong Jia, You Wu, Ning Wei, Liyan Zhang, Jingfeng Guo
First page: 4869
Abstract: Collaborative filtering (CF) based on graph neural networks (GNN) can capture higher-order relationships between nodes, which in turn improves recommendation performance. Although effective, GNN-based methods still face the challenges of sparsity and noise in real scenarios. In recent years, researchers have introduced graph self-supervised learning (SSL) techniques into CF to alleviate the sparse supervision problem. The technique first augments the data to obtain contrastive views and then utilizes the mutual information maximization to provide self-supervised signals for the contrastive views. However, the existing approaches based on graph self-supervised signals still face the following challenges: (i) Most of the works fail to effectively mine and exploit the supervised information from the item knowledge graph, resulting in suboptimal performance. (ii) Existing data augmentation methods are unable to fully exploit the potential of contrastive learning, because they primarily focus on the contrastive view of data structure changes and neglect the adjacent relationship among users and items. To address these issues, we propose a novel self-supervised learning approach, namely Knowledge-aware Graph Self-supervised Learning (KGSL). Specifically, we calculate node similarity based on semantic relations between items in the knowledge graph to generate a semantic-based item similarity graph. Then, the self-supervised learning contrast views are generated from both the user–item interaction graph and the item similarity graph, respectively. Maximization of the information from these contrastive views provides additional self-supervised signals to enhance the node representation capacity. Finally, we establish a joint training strategy for the self-supervised learning task and the recommendation task to further optimize the learning process of KGSL. Extensive comparative experiments as well as ablation experiments are conducted on three real-world datasets to verify the effectiveness of KGSL.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234869
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4870: Phase-Noise Characterization in Stable
Optical Frequency Transfer over Free Space and Fiber Link Testbeds
Authors: Peter Barcik, Jan Hrabina, Martin Cizek, Zdenek Kolka, Petr Skryja, Lenka Pravdova, Ondrej Cip, Lucie Hudcova, Ondrej Havlis, Josef Vojtech
First page: 4870
Abstract: Time and frequency metrology depends on stable oscillators in both radio-frequency and optical domains. With the increased complexity of the highly precise oscillators also came the demand for delivering the oscillators’ harmonic signals between delocalized sites for comparison, aggregation, or other purposes. Besides the traditional optical fiber networks, free-space optical links present an alternative tool for disseminating stable sources’ output. We present a pilot experiment of phase-coherent optical frequency transfer using a free-space optical link testbed. The experiment performed on a 30 m long link demonstrates the phase-noise parameters in a free-space optical channel under atmospheric turbulence conditions, and it studies the impact of active MEMS mirror stabilization of the received optical wave positioning on the resulting transfer’s performance. Our results indicate that a well-configured MEMS mirror beam stabilization significantly enhances fractional frequency stability, achieving the−14th-order level for integration times over 30 s.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234870
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4871: A Multimodal Late Fusion Framework for
Physiological Sensor and Audio-Signal-Based Stress Detection: An
Experimental Study and Public Dataset
Authors: Vasileios-Rafail Xefteris, Monica Dominguez, Jens Grivolla, Athina Tsanousa, Francesco Zaffanela, Martina Monego, Spyridon Symeonidis, Sotiris Diplaris, Leo Wanner, Stefanos Vrochidis, Ioannis Kompatsiaris
First page: 4871
Abstract: Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234871
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4872: Improving Medical Entity Recognition in
Spanish by Means of Biomedical Language Models
Authors: Aitana Villaplana, Raquel Martínez, Soto Montalvo
First page: 4872
Abstract: Named Entity Recognition (NER) is an important task used to extract relevant information from biomedical texts. Recently, pre-trained language models have made great progress in this task, particularly in English language. However, the performance of pre-trained models in the Spanish biomedical domain has not been evaluated in an experimentation framework designed specifically for the task. We present an approach for named entity recognition in Spanish medical texts that makes use of pre-trained models from the Spanish biomedical domain. We also use data augmentation techniques to improve the identification of less frequent entities in the dataset. The domain-specific models have improved the recognition of name entities in the domain, beating all the systems that were evaluated in the eHealth-KD challenge 2021. Language models from the biomedical domain seem to be more effective in characterizing the specific terminology involved in this task of named entity recognition, where most entities correspond to the "concept" type involving a great number of medical concepts. Regarding data augmentation, only back translation has slightly improved the results. Clearly, the most frequent types of entities in the dataset are better identified. Although the domain-specific language models have outperformed most of the other models, the multilingual generalist model mBERT obtained competitive results.
Citation: Electronics
PubDate: 2023-12-02
DOI: 10.3390/electronics12234872
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4873: UHF Textronic RFID Transponder with
Bead-Shaped Microelectronic Module
Authors: Piotr Jankowski-Mihułowicz, Mariusz Węglarski, Patryk Pyt, Kacper Skrobacz, Karol Karpiński
First page: 4873
Abstract: The idea of novel antennas and matching circuits, developed for radio frequency identification (RFID) passive transponders, and made on textile substrates, is presented in this paper. By manufacturing an RFID transponder by the means used in every clothing factory, we developed the concept of RFIDtex tags, which, as textronic devices, make a new significant contribution to the Internet of Textile Things (IoTT). The main feature of the device consists of the use of an uncommon inductively coupled system as the antenna feed element. The antenna is sewn/embroidered with a conductive thread, and the microelectronic module with an RFID chip is made in the form of a bead, using standard electronic technology. Finally, the construction of the RFIDtex tag is developed for easy implementation in production lines in the garment industry. The proposed inductive coupling scheme has not been considered anywhere, so far. The developed transponder is dedicated to operating in RFID systems of the ultra-high frequency band (UHF). The numerical calculations confirmed by the experimental results clearly indicate that the proposed coupling system between the antenna and the microelectronic module works properly and the RFIDtex device can operate correctly within a distance of several meters. The proposed design is based on the authors’ patent on the textronic RFID transponder (patent no PL 231291 B1).
Citation: Electronics
PubDate: 2023-12-03
DOI: 10.3390/electronics12234873
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4874: An Efficient Bit-Based Approach for
Mining Skyline Periodic Itemset Patterns
Authors: Yanzhi Li, Zhanshan Li
First page: 4874
Abstract: Periodic itemset patterns (PIPs) are widely used in predicting the occurrence of periodic events. However, extensive redundancy arises due to a large number of patterns. Mining skyline periodic itemset patterns (SPIPs) can reduce the number of PIPs and guarantee the accuracy of prediction. The existing SPIP mining algorithm uses FP-Growth to generate frequent patterns (FPs), and then identify SPIPs from FPs. Such separate steps lead to a massive time consumption, so we propose an efficient bit-based approach named BitSPIM to mine SPIPs. The proposed method introduces efficient bitwise representations and makes full use of the data obtained in the previous steps to accelerate the identification of SPIPs. A novel cutting mechanism is applied to eliminate unnecessary steps. A series of comparative experiments were conducted on various datasets with different attributes to verify the efficiency of BitSPIM. The experiment results demonstrate that our algorithm significantly outperforms the latest SPIP mining approach.
Citation: Electronics
PubDate: 2023-12-03
DOI: 10.3390/electronics12234874
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4875: Evaluation of Contactless Identification
Card Immunity against a Current Pulse in an Adjacent Conductor
Authors: Peter Vestenický, Marián Hruboš, Eduard Kolla
First page: 4875
Abstract: This paper analyses the possibility of damaging and destroying an identification chip of the Mifare type in a frequently used contactless identification card of size ID-1, following the standard ISO/IEC 7810 (i.e., with dimensions 85.60 × 53.98 × 0.76 mm), using the magnetic field of an adjacent conductor in which a current pulse of a defined shape and amplitude is flowing. For analysis purposes, the nonlinear current–voltage characteristic of the Mifare chip voltage limiter was measured and approximated, and the mutual inductance of the straight conductor and the rectangle coil antenna in the card was calculated. Next, a mathematical analysis was conducted based on the description of the equivalent electrical circuit by the differential equations. The results of the mathematical analysis were verified by a simulation in the free simulation software Micro-Cap 12. The peak value of the current pulse that can damage the Mifare chip was measured by a combination wave generator. Based on these measurements and the chip characteristics, the energy capable of destroying the chip was calculated. The characteristics of chip damage were determined using a comparison of the resonant characteristics of undamaged and damaged RFID cards with Mifare chips.
Citation: Electronics
PubDate: 2023-12-03
DOI: 10.3390/electronics12234875
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4876: Research on Orderly Charging Strategy
for Electric Vehicles Based on Electricity Price Guidance and Reliability
Evaluation of Microgrid
Authors: Zhipeng Weng, Jinghua Zhou, Xiaotong Song, Liuming Jing
First page: 4876
Abstract: With the increasing use of electric vehicles (EVs), EVs will be widely connected to the microgrid in the future. However, the influence of the disorderly charging behavior of EVs on the stable and reliable operation of the power grid cannot be ignored. To address these challenges, the charging load characteristic model is established to describe the charging behavior of EVs. Then, an EVs orderly charging strategy based on electricity price guidance is proposed, and the goal is to minimize the peak–valley difference ratio and the total cost of EV charging. The result shows that, compared with disorderly charging, the EV orderly charging strategy based on electricity price guidance proposed in this paper can effectively reduce the peaking and valley difference ratio of load, reduce user’s charging costs, and optimize the reliability level of the microgrid.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234876
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4877: A Systematic Evaluation: Fine-Grained
CNN vs. Traditional CNN Classifiers
Authors: Saeed Anwar, Nick Barnes, Lars Petersson
First page: 4877
Abstract: Fine-grained classifiers collect information about inter-class variations to best use the underlying minute and subtle differences. The task is challenging due to the minor differences between the colors, viewpoints, and structure in the same class entities. The classification becomes difficult and challenging due to the similarities between the differences in viewpoint with other classes and its own. This work investigates the performance of landmark traditional CNN classifiers, presenting top-notch results on large-scale classification datasets and comparing them against state-of-the-art fine-grained classifiers. This paper poses three specific questions. (i) Do the traditional CNN classifiers achieve comparable results to fine-grained classifiers' (ii) Do traditional CNN classifiers require any specific information to improve fine-grained ones' (iii) Do current traditional state-of-the-art CNN classifiers improve the fine-grained classification while utilized as a backbone' Therefore, we train the general CNN classifiers throughout this work without introducing any aspect specific to fine-grained datasets. We show an extensive evaluation on six datasets to determine whether the fine-grained classifier can elevate the baseline in their experiments. We provide ablation studies regarding efficiency, number of parameters, flops, and performance.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234877
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4878: Accumulation and Elimination: A Hard
Decision-Based Multi-User Interference Cancellation Method in Satellite
Communication System
Authors: Haohan Li, Yongjia Jin, Deguang Zhao, Xuhui Ding, Kai Yang
First page: 4878
Abstract: With the increasing number of users in the Medium-Orbit (MEO) satellite communication system, multi-access interference (MAI) has become an important factor that restricts the reliability and capacity of the system. Additionally, the low carrier-power-to-noise-density ratio (C/N0) resulting from long-distance transmission poses a significant concern. The parallel interference cancellation (PIC) algorithm, utilized within the paradigm of multi-user detection (MUD), exhibits the capability to effectively mitigate the impact of MAI within the same system. Simultaneously, coherent accumulation serves as a means to substantially enhance the correct detection probability (Pcd) at low C/N0. In this study, a signal acquisition method for multi-user spread spectrum satellite receivers is proposed, which employs interference cancellation and coherent accumulation as its core mechanisms. Furthermore, we introduce a power estimation method based on the outcomes of signal acquisition, which can be integrated into the signal reconstruction module of PIC. Finally, we implement the aforementioned algorithms in both simulation and hardware platforms. Remarkably, we observe that when the interference-to-signal ratio (ISR) caused by MAI equals 20 dB, the improved algorithm attains a maximum Pcd of 0.95 within the high signal-to-noise ratio (SNR) region, closely approaching the theoretical limit for the bit error rate (BER). The experimental results prove the effectiveness and feasibility of the acquisition algorithm. In summary, the enhanced algorithm holds vast potential for widespread implementation in multi-user spread spectrum communication systems.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234878
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4879: “Canalvoltaico” in
Emilia-Romagna, Italy: Assessing Technical, Economic, and Environmental
Feasibility of Suspended Photovoltaic Panels over Water Canals
Authors: Valentino Solfrini, Riccardo Farneti, Jessica Rossi, Augusto Bianchini, Matteo Morolli, Ivan Savini
First page: 4879
Abstract: Solar energy has become an increasingly important part of the global energy mix. In Italy, the photovoltaic power installed has grown by 40% since 2015, which raises the issue of land use and occupation. A viable alternative, already experienced in India, is placing solar panels on the top of water canals (Canal-Top—in Italian, “Canalvoltaico”). It is a relatively new and innovative approach to solar energy installation that offers several advantages including the potential to generate renewable energy without occupying additional land, reduce water evaporation from canals, and improve water quality by reducing algae growth. The article explores various Canal-Top solar projects over the world; then, a feasible application in the Italian region “Emilia-Romagna” is discussed, evaluating two potential construction designs. The primary aim is to establish a capital expenditure cost framework, offering reference values currently lacking in the extant literature and industry studies pertaining to Italy. Moreover, the study addresses additional key factors, including water savings, maintenance considerations, and safety implications.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234879
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4880: Triboelectric Nanogenerator-Based
Electronic Sensor System for Food Applications
Authors: Yutong Wang, Weifeng Jin, Langhong Wang, Zhiyuan Zhu
First page: 4880
Abstract: Triboelectric nanogenerators (TENGs) have garnered significant attention due to their ability to efficiently harvest energy from the surrounding environment and from living organisms, as well as to enable the efficient utilization of various materials, such as organic polymers, metals, and inorganic compounds. As a result, TENGs represent an emerging class of self-powered devices that can power small sensors or serve as multifunctional sensors themselves to detect a variety of physical and chemical stimuli. In this context, TENGs are expected to play a pivotal role in the entire process of food manufacturing. The rapid development of the Internet of Things and sensor technology has built a huge platform for sensor systems for food testing. TENG-based sensor data provide novel judgment and classification features, offering a fast and convenient means of food safety detection. This review comprehensively summarizes the latest progress in the application of TENGs in the food field, mainly involving food quality testing, food monitoring, food safety, and agricultural production. We also introduce different TENG-based, self-powered devices for food detection and improvement from the perspective of material strategies and manufacturing solutions. Finally, we discuss the current challenges and potential opportunities for future development of TENGs in the food field. We hope that this work can provide new insights into the structural and electronic design of TENGs, thereby benefiting environmental protection and food health.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234880
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4881: Recent Advances in Motion Planning and
Control of Autonomous Vehicles
Authors: Bai Li, Xiaoming Chen, Tankut Acarman, Xiaohui Li, Youmin Zhang
First page: 4881
Abstract: An autonomous vehicle operates without human intervention, marking advancements in navigating structured urban roads and unstructured environments [...]
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234881
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4882: Localization Method for Underwater Robot
Swarms Based on Enhanced Visual Markers
Authors: Qingbo Wei, Yi Yang, Xingqun Zhou, Chuanzhi Fan, Quan Zheng, Zhiqiang Hu
First page: 4882
Abstract: In challenging tasks such as large-scale resource detection, deep-sea exploration, prolonged cruising, extensive topographical mapping, and operations within intricate current regions, AUV swarm technologies play a pivotal role. A core technical challenge within this realm is the precise determination of relative positions among AUVs within the cluster. Given the complexity of underwater environments, this study introduces an integrated and high-precision underwater cluster positioning method, incorporating advanced image restoration algorithms and enhanced underwater visual markers. Utilizing the Hydro-Optical Image Restoration Model (HOIRM) developed in this research, image clarity in underwater settings is significantly improved, thereby expanding the attenuation coefficient range for marker identification and enhancing it by at least 20%. Compared to other markers, the novel underwater visual marker designed in this research elevates positioning accuracy by 1.5 times under optimal water conditions and twice as much under adverse conditions. By synthesizing the aforementioned techniques, this study has successfully developed a comprehensive underwater visual positioning algorithm, amalgamating image restoration, feature detection, geometric code value analysis, and pose resolution. The efficacy of the method has been validated through real-world underwater swarm experiments, providing crucial navigational and operational assurance for AUV clusters.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234882
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4883: Convolution Power Ratio Based on
Single-Ended Protection Scheme for HVDC Transmission Lines
Authors: Guangqiang Peng, Lixin Chen, Jiyang Wu, Huimin Jiang, Zhijie Wang, Haifeng Li
First page: 4883
Abstract: In order to solve the problems of insufficient abilities to withstand transition resistance under remote faults and difficulties in identifying internal and external faults for HVDC transmission line protection, a new single-ended protection scheme based on time-domain convolutional power was proposed. In this scheme, the ratio of time-domain convolution power at different frequencies is used to detect internal and external faults, and the long window convolution power is used to form the pole selection criteria. Due to the integration of transient power fault characteristics at high and low frequencies, this scheme amplifies the characteristic differences between internal and external faults caused by DC line boundaries and has a strong ability to withstand transition resistance. Based on PSCAD/EMTDC, simulation verification was conducted on the Yunnan–Guangzhou ±800 kV HVDC project. The results show that the proposed single-ended protection scheme can effectively identify fault poles, as well as internal and external faults. It has strong resistance to transition resistance and certain anti-interference ability and has strong adaptability to DC line boundaries, which meets the protection requirements of HVDC transmission systems for high speed, selectivity and reliability.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234883
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4884: Revisiting Hard Negative Mining in
Contrastive Learning for Visual Understanding
Authors: Hao Zhang, Zheng Li, Jiahui Yang, Xin Wang, Caili Guo, Chunyan Feng
First page: 4884
Abstract: Efficiently mining and distinguishing hard negatives is the key to Contrastive Learning (CL) in various visual understanding tasks. By properly emphasizing the penalty of hard negatives, Hard Negative Mining (HNM) can improve the CL performance. However, there is no method to quantitatively analyze the penalty strength of hard negatives, which makes training difficult to converge. In this paper, we propose a method for measuring and controlling the penalty strength. We first define a penalty strength metric to provides a quantitative analysis tool for HNM. Then, we propose a Triplet loss with Penalty Strength Control (T-PSC), which can balance the penalty strength of hard negatives and the difficulty of model optimization. In order to verify the effectiveness of the proposed T-PSC method in different modalities, we applied it to two visual understanding tasks: Image–Text Retrieval (ITR) for multi-model processing, and Temporal Action Localization (TAL) for video processing. T-PSC can be applied to existing ITR and TAL models in a plug-and-play manner without any changes. Experiments combined with existing models show that a reasonable control of the penalty strength can speed up training and improve the performance on higher-level tasks.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234884
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4885: Circuit Techniques for Immunity to
Process, Voltage, and Temperature Variations in the Attachable Fractional
Divider
Authors: Motozawa, Hiraku, Hirai, Hiyama, Imanaka, Morishita
First page: 4885
Abstract: In the automotive industry, system-on-chips are crucial for managing weak radio waves from space, known as satellite signals. Integer-N phase-locked loops have played a vital role in the operation of system-on-chips in recent history. Their clock frequencies are carefully designed to prevent electromagnetic interference. However, as global navigation satellite system becomes more prevalent, integer-N phase-locked loops face new challenges in generating clocks within the shrinking frequency bands due to large frequency steps determined using a reference clock. To address it, replacing integer-N phase-locked loops with fractional-N phase-locked loops is required. This topic has not been discussed extensively, but it is a practical issue that requires consideration due to its potential impact on development costs. This is why we developed an attachable fractional divider. Our developed divider can efficiently transform integer-N phase-locked loops into fractional-N phase-locked loops, achieving low jitter degradation of 0.35 psrms and a low fractional spur of −69.3 dBc. Thanks to its attachable design, it expedites time-to-market. Regarding mass production, ensuring immunity to process, voltage, and temperature variations is a significant concern. We introduce the circuit techniques employed in the developed fractional divider for immunity to process, voltage, and temperature variations. Subsequently, we provide a comprehensive set of measurement results. The frequency differences over process variations in fractional-N mode is 6.14 ppm. Power supply and temperature dependances are extremely small in spread-spectrum clocking mode. This article illustrates that the developed fractional divider enhances both time-to-market and product reliance.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234885
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4886: ATS-YOLOv7: A Real-Time Multi-Scale
Object Detection Method for UAV Aerial Images Based on Improved YOLOv7
Authors: Heng Zhang, Faming Shao, Xiaohui He, Weijun Chu, Dewei Zhao, Zihan Zhang, Shaohua Bi
First page: 4886
Abstract: The objects in UAV aerial images have multiple scales, dense distribution, and occlusion, posing considerable challenges for object detection. In order to address this problem, this paper proposes a real-time multi-scale object detection method based on an improved YOLOv7 model (ATS-YOLOv7) for UAV aerial images. First, this paper introduces a feature pyramid network, AF-FPN, which is composed of an adaptive attention module (AAM) and a feature enhancement module (FEM). AF-FPN reduces the loss of deep feature information due to the reduction of feature channels in the convolution process through the AAM and FEM, strengthens the feature perception ability, and improves the detection speed and accuracy for multi-scale objects. Second, we add a prediction head based on a transformer encoder block on the basis of the three-head structure of YOLOv7, improving the ability of the model to capture global information and feature expression, thus achieving efficient detection of objects with tiny scales and dense occlusion. Moreover, as the location loss function of YOLOv7, CIoU (complete intersection over union), cannot facilitate the regression of the prediction box angle to the ground truth box—resulting in a slow convergence rate during model training—this paper proposes a loss function with angle regression, SIoU (soft intersection over union), in order to accelerate the convergence rate during model training. Finally, a series of comparative experiments are carried out on the DIOR dataset. The results indicate that ATS-YOLOv7 has the best detection accuracy (mAP of 87%) and meets the real-time requirements of image processing (detection speed of 94.2 FPS).
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234886
Issue No: Vol. 12, No. 23 (2023)
- Electronics, Vol. 12, Pages 4887: Object Detection Based on an Improved
Authors: Dewei Zhao, Faming Shao, Li Yang, Xiannan Luo, Qiang Liu, Heng Zhang, Zihan Zhang
First page: 4887
Abstract: When working with objects on a smaller scale, higher detection accuracy and faster detection speed are desirable features. Researchers aim to endow drones with these attributes in order to improve performance when patrolling in controlled areas for object detection. In this paper, we propose an improved YOLOv7 model. By incorporating the variability attention module into the backbone network of the original model, the association between distant pixels is increased, resulting in more effective feature extraction and, thus, improved model detection accuracy. By improving the original network model with deformable convolution modules and depthwise separable convolution modules, the model enhances the semantic information extraction of small objects and reduces the number of model parameters to a certain extent. Pretraining and fine-tuning techniques are used for training, and the model is retrained on the VisDrone2019 dataset. Using the VisDrone2019 dataset, the improved model achieves an mAP50 of 52.3% on the validation set. Through the visual comparative analysis of the detection results in our validation set, we find that the model shows a significant improvement in detecting small objects compared with previous iterations.
Citation: Electronics
PubDate: 2023-12-04
DOI: 10.3390/electronics12234887
Issue No: Vol. 12, No. 23 (2023)
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