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Electronics, Volume 13, Issue 8 (April-2 2024) – 194 articles

Cover Story (view full-size image): This study surveyed IoT platforms for smart city apps, covering commercial and open-source options. We compared them in terms of connectivity, protocols, analytics, security, etc. Aggregated characteristics reveal IoT trends. Discrepancies between city needs and platform capabilities were explored. We included industry, agriculture, and asset tracking platforms as they overlap with smart city needs. Results show a shortage of open source platforms for smart cities, hindering research in developing and testing connected applications. View this paper
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21 pages, 24912 KiB  
Article
Design of Fractional-Order Non-Singular Terminal Sliding Mode Observer Sensorless System for Surface-Mounted Permanent Magnet Synchronous Motor
by Guozhong Yao, Jinlong Gao, Jilin Lei, Shaojun Han and Yuhan Xiao
Electronics 2024, 13(8), 1601; https://doi.org/10.3390/electronics13081601 - 22 Apr 2024
Viewed by 576
Abstract
A new sensorless speed control system for a fractional-order terminal non-singular sliding mode surface-mounted permanent magnet synchronous motor is proposed. The fractional terminal non-singular sliding mode surface, which can converge in finite time, is designed by combining the fractional-order control theory with the [...] Read more.
A new sensorless speed control system for a fractional-order terminal non-singular sliding mode surface-mounted permanent magnet synchronous motor is proposed. The fractional terminal non-singular sliding mode surface, which can converge in finite time, is designed by combining the fractional-order control theory with the terminal attractor concept. Then, a new control rate is proposed to reduce system buffeting. Secondly, an adaptive back electromotive force filter is designed to reduce the harmonics in the sliding mode function estimation and improve the observation accuracy. In addition, the theoretical analysis of the designed system proves that the system can converge in a finite time. Then, a fraction-order phase-locked loop with variable factors is designed to make the system more capable of tracking the rotor. Finally, a simulation and experiment platform is built, and a comparison experiment is carried out, which proves that the designed algorithm has a stronger rotor position tracking ability and a better dynamic performance of the system. Full article
(This article belongs to the Special Issue Advances in Control for Permanent Magnet Synchronous Motor (PMSM))
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17 pages, 3168 KiB  
Article
Advanced Interference Mitigation Method Based on Joint Direction of Arrival Estimation and Adaptive Beamforming for L-Band Digital Aeronautical Communication System
by Lei Wang, Xiaoxiao Hu and Haitao Liu
Electronics 2024, 13(8), 1600; https://doi.org/10.3390/electronics13081600 - 22 Apr 2024
Viewed by 433
Abstract
The L-band digital aeronautical communication system (LDACS) is one of the candidate technologies for future broadband digital aeronautical communications, utilizing the unused L-band spectrum between distance measuring equipment (DME) channels. However, the higher signal power of DME complicates LDACS implementation. This paper proposes [...] Read more.
The L-band digital aeronautical communication system (LDACS) is one of the candidate technologies for future broadband digital aeronautical communications, utilizing the unused L-band spectrum between distance measuring equipment (DME) channels. However, the higher signal power of DME complicates LDACS implementation. This paper proposes an advanced DME mitigation approach for the LDACS, integrating joint direction of arrival (DOA) estimation with adaptive beamforming techniques. The proposed method begins by exploiting the cyclostationary characteristics of signals, accurately obtaining the preliminary direction of the LDACS signal using the Cyclic-MUSIC method. Subsequent precise steering vectors (SVs) are selected through Capon spectrum search, followed by the reconstruction of the interference plus noise covariance matrix (INCM). Using the obtained SV and INCM, the weight vector is calculated and beamforming is performed. Simulation results validate that the proposed method not only accurately estimates the direction of LDACS signal but also efficiently mitigates DME interference, demonstrating a superior performance and reduced algorithmic complexity, even in scenarios with lower signal-to-noise ratios (SNRs) and the presence of DOA estimation errors. Additionally, the proposed method achieves a low bit error rate (BER), further validating its ability to ensure communication quality and enhance the reliability of LDACS. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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16 pages, 2623 KiB  
Article
Using Data Augmentation to Improve the Accuracy of Blood Pressure Measurement Based on Photoplethysmography
by Hanlin Mou, Congjian Li, Haoran Zhou, Daobing Zhang, Wensheng Wang, Junsheng Yu and Jing Tian
Electronics 2024, 13(8), 1599; https://doi.org/10.3390/electronics13081599 - 22 Apr 2024
Viewed by 648
Abstract
Convenient and accurate blood pressure (BP) measurement is of great importance in both clinical and daily life. Although deep learning (DL) can achieve cuff-less BP measurement based on Photoplethysmography (PPG), the performance of DL is affected by few-shot data. Data augmentation becomes an [...] Read more.
Convenient and accurate blood pressure (BP) measurement is of great importance in both clinical and daily life. Although deep learning (DL) can achieve cuff-less BP measurement based on Photoplethysmography (PPG), the performance of DL is affected by few-shot data. Data augmentation becomes an effective way to enhance the size of the training data. In this paper, we use cropping, flipping, DTW barycentric averaging (DBA), generative adversarial network (GAN) and variational auto-encoder (VAE) for the data augmentation of PPG. Furthermore, a PE–CNN–GRU model is designed for cuff-less BP measurement applying position encoding (PE), convolutional neural networks (CNNs) and gated recurrent unit (GRU) networks. Experiment results based on real-life datasets show that VAE is the most suitable method for PPG data augmentation, which can reduce the mean absolute error (MAE) of PE–CNN–GRU when measuring systolic blood pressure (SBP) and diastolic blood pressure (DBP) by 18.80% and 19.84%. After the data augmentation of PPG, PE–CNN–GRU achieves accurate and cuff-less BP measurement, thus providing convenient support for preventing cardiovascular diseases. Full article
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20 pages, 1557 KiB  
Article
A Hardware Implementation of the PID Algorithm Using Floating-Point Arithmetic
by Józef Kulisz and Filip Jokiel
Electronics 2024, 13(8), 1598; https://doi.org/10.3390/electronics13081598 - 22 Apr 2024
Viewed by 737
Abstract
The purpose of the paper is to propose a new implementation of the PID (proportional–integral–derivative) algorithm in digital hardware. The proposed structure is optimized for cost. It follows a serialized, rather than parallel, scheme. It uses only one arithmetic block, performing the multiply-and-add [...] Read more.
The purpose of the paper is to propose a new implementation of the PID (proportional–integral–derivative) algorithm in digital hardware. The proposed structure is optimized for cost. It follows a serialized, rather than parallel, scheme. It uses only one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequentially cyclic manner. The proposed circuit operates on standard single-precision (32-bit) floating-point numbers. It implements an extended PID formula, containing a non-ideal derivative component, and weighting coefficients, which enable reducing the influence of setpoint changes in the proportional and derivative components. The circuit was implemented in a Cyclone V FPGA (Field-Programmable Gate Array) device from Intel, Santa Clara, CA, USA. The proper operation of the circuit was verified in a simulation. For the specific implementation, which is reported in the paper, the sampling period of 516 ns was obtained, which means that the proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on single-precision floating-point numbers. However, the presented solution is much more efficient in terms of cost. It uses 1173 LUT (Look-up Table) blocks, 1026 registers, and 1 DSP (Digital Signal Processing) block, i.e., about 30% of logic resources required by comparable solutions. Full article
(This article belongs to the Special Issue Energy Technologies in Electronics and Electrical Engineering)
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17 pages, 642 KiB  
Article
Differentiated Security Requirements: An Exploration of Microservice Placement Algorithms in Internet of Vehicles
by Xing Zhang, Jun Liang, Yuxi Lu, Peiying Zhang and Yanxian Bi
Electronics 2024, 13(8), 1597; https://doi.org/10.3390/electronics13081597 - 22 Apr 2024
Viewed by 512
Abstract
In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in [...] Read more.
In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider’s operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate. Full article
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68 pages, 1508 KiB  
Article
Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence
by Dovilė Kuizinienė and Tomas Krilavičius
Electronics 2024, 13(8), 1596; https://doi.org/10.3390/electronics13081596 - 22 Apr 2024
Viewed by 779
Abstract
Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of [...] Read more.
Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises. Full article
(This article belongs to the Special Issue New Trends in Artificial Neural Networks and Its Applications)
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13 pages, 1004 KiB  
Article
Shared Knowledge Distillation Network for Object Detection
by Zhen Guo, Pengzhou Zhang and Peng Liang
Electronics 2024, 13(8), 1595; https://doi.org/10.3390/electronics13081595 - 22 Apr 2024
Viewed by 437
Abstract
Object detection based on Knowledge Distillation can enhance the capabilities and performance of 5G and 6G networks in various domains, such as autonomous vehicles, smart surveillance, and augmented reality. The integration of object detection with Knowledge Distillation techniques is expected to play a [...] Read more.
Object detection based on Knowledge Distillation can enhance the capabilities and performance of 5G and 6G networks in various domains, such as autonomous vehicles, smart surveillance, and augmented reality. The integration of object detection with Knowledge Distillation techniques is expected to play a pivotal role in realizing the full potential of these networks. This study presents Shared Knowledge Distillation (Shared-KD) as a solution to overcome optimization challenges caused by disparities in cross-layer features between teacher–student networks. The significant gaps in intermediate-level features between teachers and students present a considerable obstacle to the efficacy of distillation. To tackle this issue, we draw inspiration from collaborative learning in real-world education, where teachers work together to prepare lessons and students engage in peer learning. Building upon this concept, our innovative contributions in model construction are highlighted as follows: (1) A teacher knowledge augmentation module: this module is proposed to combine lower-level teacher features, facilitating the knowledge transfer from the teacher to the student. (2) A student mutual learning module is introduced to enable students to learn from each other, mimicking the peer learning concept in collaborative learning. (3) The Teacher Share Module combines lower-level teacher features: the specific functionality of the teacher knowledge augmentation module is described, which involves combining lower-level teacher features. (4) The multi-step transfer process can be easily optimized due to the minimal gap between the features: the proposed approach breaks down the knowledge transfer process into multiple steps, which can be easily optimized due to the minimal gap between the features involved in each step. Shared-KD uses simple feature losses without additional weights in transformation, resulting in an efficient distillation process that can be easily combined with other methods for further improvement. The effectiveness of our approach is validated through experiments on popular tasks such as object detection and instance segmentation. Full article
(This article belongs to the Special Issue Mobile Networking: Latest Advances and Prospects)
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21 pages, 3913 KiB  
Article
Spatial–Temporal Fusion Gated Transformer Network (STFGTN) for Traffic Flow Prediction
by Haonan Xie, Xuanxuan Fan, Kaiyuan Qi, Dong Wu and Chongguang Ren
Electronics 2024, 13(8), 1594; https://doi.org/10.3390/electronics13081594 - 22 Apr 2024
Viewed by 454
Abstract
Traffic flow prediction is essential for smart city management and planning, aiding in optimizing traffic scheduling and improving overall traffic conditions. However, due to the correlation and heterogeneity of traffic data, effectively integrating the captured temporal and spatial features remains a significant challenge. [...] Read more.
Traffic flow prediction is essential for smart city management and planning, aiding in optimizing traffic scheduling and improving overall traffic conditions. However, due to the correlation and heterogeneity of traffic data, effectively integrating the captured temporal and spatial features remains a significant challenge. This paper proposes a model spatial–temporal fusion gated transformer network (STFGTN), which is based on an attention mechanism that integrates temporal and spatial features. This paper proposes an attention mechanism-based model to address these issues and model complex spatial–temporal dependencies in road networks. The self-attention mechanism enables the model to achieve long-term dependency modeling and global representation of time series data. Regarding temporal features, we incorporate a time embedding layer and a time transformer to learn temporal dependencies. This capability contributes to a more comprehensive and accurate understanding of spatial–temporal dynamic patterns throughout the entire time series. As for spatial features, we utilize DGCN and spatial transformers to capture both global and local spatial dependencies, respectively. Additionally, we propose two fusion gate mechanisms to effectively accommodate to the complex correlation and heterogeneity of spatial–temporal information, resulting in a more accurate reflection of the actual traffic flow. Our experiments on three real-world datasets illustrate the superior performance of our approach. Full article
(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)
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17 pages, 2521 KiB  
Article
Modeling and Control of an Inductive Power Transmitter Based on Buck–Half Bridge–Resonant Tank
by Domingo Cortes, Leobardo Hernandez-Gonzalez, Jazmin Ramirez-Hernandez and Maria Vargas
Electronics 2024, 13(8), 1593; https://doi.org/10.3390/electronics13081593 - 22 Apr 2024
Viewed by 378
Abstract
In this paper, a previously proposed converter to be used for inductive wireless power transmission is modeled and a control strategy is proposed. The converter topology combines into a single stage, two buck converters and a half-bridge converter to feed a resonant stage. [...] Read more.
In this paper, a previously proposed converter to be used for inductive wireless power transmission is modeled and a control strategy is proposed. The converter topology combines into a single stage, two buck converters and a half-bridge converter to feed a resonant stage. This simple and symmetrical topology is straightforward to design; only a buck converter and a parallel resonant tank must be specified. It would be desirable for the converter to feed a wide range of loads and be robust under input voltage variations. These objectives can not be attained with a linear model and control. For this reason, in this paper, a nonlinear converter model is derived step by step and controller strategy is developed without relying on system linearization. The proposed controller does not measure the output but only its peak value. This can be conducted because it takes advantage of the square current pulse fed into a resonant tank; it outputs an approximately sinusoidal signal. The control strategy is completed with a scheme to build the required pulse at the input of the resonant tank. The resulting nonlinear controller has a fast closed-loop performance; furthermore, it is robust under parameter uncertainty, and load and input voltage variations. Despite its features, the controller is fairly simple to implement. Full article
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16 pages, 6318 KiB  
Article
Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach
by Shuqi Wang, Nan Qi, Hua Jiang, Ming Xiao, Haoxuan Liu, Luliang Jia and Dan Zhao
Electronics 2024, 13(8), 1592; https://doi.org/10.3390/electronics13081592 - 22 Apr 2024
Cited by 1 | Viewed by 481
Abstract
Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, [...] Read more.
Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm. Full article
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28 pages, 5142 KiB  
Article
Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data
by Ali Akdag and Omer Kaan Baykan
Electronics 2024, 13(8), 1591; https://doi.org/10.3390/electronics13081591 - 22 Apr 2024
Viewed by 441
Abstract
This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and [...] Read more.
This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and processed in separate channels. Using these multichannel data, we trained the proposed MultiChannel-MobileNetV2 model to provide a detailed analysis of finger movements. In our study, we first subject the features extracted from all trained models to dimensionality reduction using Principal Component Analysis. Subsequently, we combine these processed features for classification using a Support Vector Machine. Furthermore, our proposed method includes processing body and facial information using MobileNetV2. Our final proposed sign language recognition method has achieved remarkable accuracy rates of 97.15%, 95.13%, 99.78%, and 95.37% on the BosphorusSign22k-general, BosphorusSign22k, LSA64, and GSL datasets, respectively. These results underscore the generalizability and adaptability of the proposed method, proving its competitive edge over existing studies in the literature. Full article
(This article belongs to the Special Issue Artificial Intelligence in Vision Modelling)
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20 pages, 1314 KiB  
Article
WSN-Driven Advances in Soil Moisture Estimation: A Machine Learning Approach
by Tinku Singh, Majid Kundroo and Taehong Kim
Electronics 2024, 13(8), 1590; https://doi.org/10.3390/electronics13081590 - 22 Apr 2024
Viewed by 528
Abstract
Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite [...] Read more.
Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite and climate data, this research aims to enhance the accuracy and resolution of soil moisture estimation, enabling more effective agricultural planning, irrigation management, and environmental monitoring. Five ML models, including linear regression, support vector machines, decision trees, random forests, and long short-term memory networks (LSTM), are evaluated and compared using real-world data from multiple geographical regions, which includes a dataset from NASA’s SMAP project, supplemented by climate data, which employs both active and passive sensors for data collection. The outcomes demonstrate that the LSTM model consistently outperforms other ML algorithms across various evaluation metrics, highlighting the effectiveness of WSN-driven approaches to soil moisture estimation. The study contributes to the advancement of soil moisture monitoring technologies, offering insights into the potential of WSNs combined with ML and DL for sustainable agriculture and environmental management practices. Full article
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17 pages, 1090 KiB  
Article
An Effective Federated Recommendation Framework with Differential Privacy
by Zihang Xu, Chiawei Chu and Shiyang Song
Electronics 2024, 13(8), 1589; https://doi.org/10.3390/electronics13081589 - 22 Apr 2024
Viewed by 462
Abstract
This paper proposes a novel federated recommendation framework that incorporates differential privacy to safeguard user privacy without compromising on the accuracy of recommendations. Unlike conventional recommendation systems that centralize user data, leading to potential privacy breaches, our framework ensures that user data remain [...] Read more.
This paper proposes a novel federated recommendation framework that incorporates differential privacy to safeguard user privacy without compromising on the accuracy of recommendations. Unlike conventional recommendation systems that centralize user data, leading to potential privacy breaches, our framework ensures that user data remain on local devices. It leverages a federated learning approach, where a global model is trained across multiple devices without exchanging raw data. To enhance privacy protection, we integrate a specially designed differential privacy algorithm that adds carefully calibrated noise to the aggregated data updates. This mechanism ensures that the global model cannot be exploited to infer individual user information. We evaluate our framework on two real-world datasets, one from the e-commerce sector and another from the multimedia content recommendation domain. The results exhibit that our framework achieves competitive recommendation accuracy compared to traditional centralized approaches, with minimal loss in precision and recall metrics, while significantly enhancing user privacy. Our work stands as a testament to the feasibility of creating recommendation systems that do not have to choose between privacy and performance, paving the way for more ethical AI applications in sensitive domains. Full article
(This article belongs to the Special Issue Data Privacy and Cybersecurity in Mobile Crowdsensing)
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16 pages, 643 KiB  
Article
An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
by Honghao Shi, Zhiyong Luo and Congduan Li
Electronics 2024, 13(8), 1588; https://doi.org/10.3390/electronics13081588 - 22 Apr 2024
Viewed by 418
Abstract
Faster-than-Nyquist (FTN) signaling has attracted increasing interest in the past two decades. However, when the fifth-generation (5G) communication low-density parity check (LDPC) code is applied to FTN signaling with low Bahl–Cock–Jelinek–Raviv (BCJR) states of detection and few turbo equalization iterations, an error floor [...] Read more.
Faster-than-Nyquist (FTN) signaling has attracted increasing interest in the past two decades. However, when the fifth-generation (5G) communication low-density parity check (LDPC) code is applied to FTN signaling with low Bahl–Cock–Jelinek–Raviv (BCJR) states of detection and few turbo equalization iterations, an error floor near 105 is found, which does not exist in the original LDPC used for orthogonal signaling. This can be eliminated through many detection and decoding iterations, but this is unacceptable considering the increase in latency and storage. To solve this problem, we propose an LDPC and Reed–Solomon (RS) concatenation code, shortening, and perturbation scheme to lower the error floor. We propose a parallel encoder architecture for RS component code and a concise algorithm to calculate its constant multiplier coefficients, leveraging a traditional serial encoder, which can also be used for other parallelisms, rates, and lengths. The simulation results show that the proposed concatenation and shortening scheme can lower the error floor to about 107. The proposed scheme has an error correction capability for coded FTN signaling and successfully lowers the error floor with the limitation of few turbo iterations and few BCJR states. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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15 pages, 4840 KiB  
Article
Light-Emitting Diodes for Energy Harvesting
by Lorenzo Colace, Gaetano Assanto and Andrea De Iacovo
Electronics 2024, 13(8), 1587; https://doi.org/10.3390/electronics13081587 - 22 Apr 2024
Viewed by 786
Abstract
Energy harvesting is gaining substantial relevance in the realm of ultra-low-power electronics and Internet-of-Things devices with limited access to classic power sources. Several harvesting approaches are available, depending on the energy source; among them, photovoltaic devices benefit from the highest energy density. However, [...] Read more.
Energy harvesting is gaining substantial relevance in the realm of ultra-low-power electronics and Internet-of-Things devices with limited access to classic power sources. Several harvesting approaches are available, depending on the energy source; among them, photovoltaic devices benefit from the highest energy density. However, the inclusion of a dedicated photovoltaic cell in a low-power system may result in increased costs and complexity, thus hampering economic sustainability. Conversely, electronic apparatuses often make use of light-emitting-diodes (LEDs), which could be effectively employed as photovoltaic energy harvesters whenever not actively generating photons. Here, we explore the potentials of commercially available LEDs for energy harvesting and determine their quantum efficiency. We examine the correlation of the latter with the spectral response and the available light, demonstrating that visible-wavelength diode emitters can yield very high conversions in the photovoltaic mode. We report measured quantum efficiencies as high as 39% under low-intensity (100 µW/cm2) fluorescent illumination. Full article
(This article belongs to the Special Issue Energy Harvesting and Energy Storage Systems, 3rd Edition)
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4 pages, 161 KiB  
Editorial
Guest Editorial: Foreword of the Special Issue on Real-World Applications of Machine Learning
by Syed Tahir Hussain Rizvi and Arslan Arif
Electronics 2024, 13(8), 1586; https://doi.org/10.3390/electronics13081586 - 22 Apr 2024
Viewed by 479
Abstract
Machine learning is an ever-growing field, and many real-time applications are utilized in daily life [...] Full article
(This article belongs to the Special Issue Applications of Machine Learning in Real World)
20 pages, 1477 KiB  
Article
LaANIL: ANIL with Look-Ahead Meta-Optimization and Data Parallelism
by Vasu Tammisetti, Kay Bierzynski, Georg Stettinger, Diego P. Morales-Santos, Manuel Pegalajar Cuellar and Miguel Molina-Solana
Electronics 2024, 13(8), 1585; https://doi.org/10.3390/electronics13081585 - 22 Apr 2024
Viewed by 640
Abstract
Meta-few-shot learning algorithms, such as Model-Agnostic Meta-Learning (MAML) and Almost No Inner Loop (ANIL), enable machines to learn complex tasks quickly with limited data and based on previous experience. By maintaining the inner loop head of the neural network, ANIL leads to simpler [...] Read more.
Meta-few-shot learning algorithms, such as Model-Agnostic Meta-Learning (MAML) and Almost No Inner Loop (ANIL), enable machines to learn complex tasks quickly with limited data and based on previous experience. By maintaining the inner loop head of the neural network, ANIL leads to simpler computations and reduces the complexity of MAML. Despite its benefits, ANIL suffers from issues like accuracy variance, slow initial learning, and overfitting, hardening its adaptation and generalization. This work proposes “Look-Ahead ANIL” (LaANIL), an enhancement to ANIL for better learning. LaANIL reorganizes ANIL’s internal architecture, integrating parallel computing techniques (to process multiple training examples simultaneously across computing units) and incorporating Nesterov momentum (which accelerates convergence by adjusting the learning rate based on past gradient information and extracting informative features for look-ahead gradient computation). These additional features make our model more state-of-the-art capable and better edge-compatible and thus improve few-short learning by enabling models to quickly adapt to new information and tasks. LaANIL’s effectiveness is validated on established meta-few-shot learning datasets, including FC100, CIFAR-FS, Mini-ImageNet, CUBirds-200-2011, and Tiered-ImageNet. The proposed model achieved an increased validation accuracy by 7 ± 0.7% and a variance reduction by 44 ± 4% in two-way two-shot classification as well as increased validation by 5 ± 0.4% and a variance reduction by 18 ± 2% in five-way five-shot classification on the FC100 dataset and similarly performed well on other datasets. Full article
(This article belongs to the Special Issue Advances in Data Science and Machine Learning)
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21 pages, 6640 KiB  
Article
A Fast Factorized Back-Projection Algorithm Based on Range Block Division for Stripmap SAR
by Yawei Wu, Binbin Li, Bo Zhao and Xiaojun Liu
Electronics 2024, 13(8), 1584; https://doi.org/10.3390/electronics13081584 - 22 Apr 2024
Viewed by 548
Abstract
Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm’s efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining [...] Read more.
Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm’s efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining its computational efficiency. However, the above method is only operated in the azimuth direction, and the computing efficiency needs to be urgently improved in the actual processing process. This paper proposes a fast factorized back-projection algorithm for stripmap SAR imaging based on range block division. The echo data are divided into multiple subblocks in the range direction, and FFBP processing is applied separately to each full-aperture subblock, further enhancing computational efficiency. The paper analyzes the algorithm’s principles, underscores the necessity of integral aperture determination and full-aperture data block processing, provides specific implementation steps, and applies the algorithm to point target simulation and experimental data from a vehicle-mounted ice radar. The experiments validate the algorithm’s efficiency in stripmap SAR imaging. Full article
(This article belongs to the Topic Radar Signal and Data Processing with Applications)
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20 pages, 9479 KiB  
Article
Control Parameters Design of Spraying Robots Based on Dynamic Feedforward
by Yu Chen, Liping Chen, Yu Chen, Jianwan Ding, Yanbing Liu and Dong Yan
Electronics 2024, 13(8), 1583; https://doi.org/10.3390/electronics13081583 - 21 Apr 2024
Viewed by 397
Abstract
The positioning and velocity accuracy of spraying robots determine the quality of the coating, and the influence of the robotic dynamic characteristics on control precision is significant. This paper presents a method of linearizing dynamic characteristics into feedforward coefficients and designs a dual-loop [...] Read more.
The positioning and velocity accuracy of spraying robots determine the quality of the coating, and the influence of the robotic dynamic characteristics on control precision is significant. This paper presents a method of linearizing dynamic characteristics into feedforward coefficients and designs a dual-loop control system consisting of an inner velocity loop and an outer position loop. The system is divided into three sections: a cascaded section, a feedback section, and a feedforward section. The cascaded section eliminates the nonlinear characteristics of the system; the feedback section ensures the stability of the system; the feedforward section compensates for the internal errors of the system. The main innovation of this paper lies in proposing an offline parameter tuning method, which avoids online parameter adjustments and significantly enhances the real-time performance of the control system. Additionally, this method does not require specific physical information of the system, thus avoiding the cumbersome process of parameter adjustment. The experimental results demonstrate that when facing different high-speed trajectories, the proposed control system exhibits a significant improvement in control accuracy compared to other advanced control schemes. Full article
(This article belongs to the Special Issue The Application of Control Systems in Robots)
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15 pages, 8085 KiB  
Article
A Ka-Band Two-Channel Two-Beam Receiver Based on a Substrate-Integrated Suspended Line
by Hui Xu, Kaixue Ma, Shuantao Li, Gaojian Liu and Yongqiang Wang
Electronics 2024, 13(8), 1582; https://doi.org/10.3390/electronics13081582 - 21 Apr 2024
Viewed by 383
Abstract
To realize the miniaturization and high performance for the key transceiver components of a phased array antenna, we proposed a two-channel two-beam receiver based on a substrate-integrated suspended-line (SISL). Multi-layer composite substrate printing circuit is used in the SISL structure to replace the [...] Read more.
To realize the miniaturization and high performance for the key transceiver components of a phased array antenna, we proposed a two-channel two-beam receiver based on a substrate-integrated suspended-line (SISL). Multi-layer composite substrate printing circuit is used in the SISL structure to replace the metal cavity and passive circuits. The active components are placed in the air cavity in the SISL structure. The substrates forming the cavity are soldered together to ensure the hermetic seal and high performance. The SISL circuits have the advantage of low loss, low cost, light weight, self-packaging, and high inter-channel isolation. The proposed Ka-band two-channel two-beam receiver shows a gain of >28 dB, a noise figure of <2.6 dB, with functions of 6-bit phase shifting and attenuation. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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14 pages, 6484 KiB  
Article
Unveiling Acoustic Cavitation Characterization in Opaque Chambers through a Low-Cost Piezoelectric Sensor Approach
by José Fernandes, Paulo J. Ramísio and Hélder Puga
Electronics 2024, 13(8), 1581; https://doi.org/10.3390/electronics13081581 - 20 Apr 2024
Viewed by 536
Abstract
This study investigates the characterization of acoustic cavitation in a water-filled, opaque chamber induced by ultrasonic waves at 20 kHz. It examines the effect of different acoustic radiator geometries on cavitation generation across varying electrical power levels. A cost-effective piezoelectric sensor, precisely positioned, [...] Read more.
This study investigates the characterization of acoustic cavitation in a water-filled, opaque chamber induced by ultrasonic waves at 20 kHz. It examines the effect of different acoustic radiator geometries on cavitation generation across varying electrical power levels. A cost-effective piezoelectric sensor, precisely positioned, quantifies cavitation under assorted power settings. Two acoustic radiator shape configurations, one with holes and another solid, were examined. The piezoelectric sensor demonstrated efficacy, corroborating with existing literature, in measuring acoustic cavitation. This was achieved through the Fast Fourier Transform (FFT) analysis of voltage data, specifically targeting sub-harmonic patterns, thereby providing a robust method for cavitation detection. Results demonstrate that perforated geometries enhance cavitation intensity at lower power levels, while solid shapes predominantly affect cavitation axially, exhibiting decreased activity at minimal power. The findings recommend using two different shape geometries on the acoustic radiator for efficient cavitation detection, highlighting intense cavitation on radial walls and cavitation generation on the bottom. Due to the stochastic nature of cavitation, averaging data is critical. The spatial limitation of the sensor necessitates prioritizing specific areas over complete coverage, with multiple sensors recommended for comprehensive cavitation pattern analysis. Full article
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33 pages, 4169 KiB  
Article
Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application
by Santuan Qin, Huadie Zeng, Wei Sun, Jin Wu and Junhua Yang
Electronics 2024, 13(8), 1580; https://doi.org/10.3390/electronics13081580 - 20 Apr 2024
Cited by 1 | Viewed by 676
Abstract
In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization [...] Read more.
In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance. Full article
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22 pages, 2739 KiB  
Article
Revealing IoT Cryptographic Settings through Electromagnetic Side-Channel Analysis
by Muhammad Rusyaidi Zunaidi, Asanka Sayakkara and Mark Scanlon
Electronics 2024, 13(8), 1579; https://doi.org/10.3390/electronics13081579 - 20 Apr 2024
Viewed by 529
Abstract
The advancement of cryptographic systems presents both opportunities and challenges in the realm of digital forensics. In an era where the security of digital information is crucial, the ability to non-invasively detect and analyze cryptographic configurations has become significant. As cryptographic algorithms become [...] Read more.
The advancement of cryptographic systems presents both opportunities and challenges in the realm of digital forensics. In an era where the security of digital information is crucial, the ability to non-invasively detect and analyze cryptographic configurations has become significant. As cryptographic algorithms become more robust with longer key lengths, they provide higher levels of security. However, non-invasive side channels, specifically through electromagnetic (EM) emanations, can expose confidential cryptographic details, thus presenting a novel solution to the pressing forensic challenge. This research delves into the capabilities of EM side-channel analysis (EM-SCA), specifically focusing on detecting both cryptographic key lengths and the algorithms employed utilizing a machine-learning-based approach, which can be instrumental for digital forensic experts during their investigations. Through meticulous data processing and analysis, the Support Vector Machine (SVM) model, among others, demonstrated a notable accuracy of 94.55% in distinguishing between AES and ECC cryptographic operations. This capability significantly enhances digital forensic methodologies, offering a novel avenue for noninvasively uncovering encrypted data’s cryptographic settings. By identifying key lengths and algorithms without invasive procedures, this research contributes substantially to the advancement of forensic investigations in encrypted environments. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications)
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14 pages, 3905 KiB  
Article
Classification of Microscopic Hyperspectral Images of Blood Cells Based on Lightweight Convolutional Neural Network
by Jinghui Fang
Electronics 2024, 13(8), 1578; https://doi.org/10.3390/electronics13081578 - 20 Apr 2024
Viewed by 576
Abstract
Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging [...] Read more.
Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging techniques. Addressing challenges such as the computational burden of existing convolutional neural networks (CNNs) and imbalances in sample data, this paper introduces a lightweight GhostMRNet for the classification of microscopic hyperspectral images of human blood cells. The proposed model employs Ghost Modules to replace conventional convolutional layers and a cascading approach with small convolutional kernels for multiscale feature extraction, aiming to enhance feature extraction capabilities while reducing computational complexity. Additionally, an SE (Squeeze-and-Excitation) module is introduced to selectively allocate weights to features in each channel, emphasizing informative features and efficiently achieving spatial–spectral feature extraction in microscopic hyperspectral imaging. We evaluated the performance of the proposed GhostMRNet and compared it with other state-of-the-art models using two real medical hyperspectral image datasets. The experimental results demonstrate that GhostMRNet exhibits a superior performance, with an overall accuracy (OA), average accuracy (AA), and Kappa coefficient reaching 99.965%, 99.565%, and 0.9925, respectively. In conclusion, the proposed GhostMRNet achieves a superior classification performance at a smaller computational cost, thereby providing a novel approach for blood cell detection. Full article
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17 pages, 1240 KiB  
Article
Intelligent Security Authentication for Connected and Autonomous Vehicles: Attacks and Defenses
by Xiaoying Qiu, Jinwei Yu, Wenbao Jiang and Xuan Sun
Electronics 2024, 13(8), 1577; https://doi.org/10.3390/electronics13081577 - 20 Apr 2024
Viewed by 387
Abstract
The emergence of integrated positioning, communication, and sensing technologies has paved the way for a surge in connected and autonomous vehicles. The control system has been successful in reliable and fast transmission. However, practical applications face security risks, especially data tampering and spoofing [...] Read more.
The emergence of integrated positioning, communication, and sensing technologies has paved the way for a surge in connected and autonomous vehicles. The control system has been successful in reliable and fast transmission. However, practical applications face security risks, especially data tampering and spoofing attacks. To improve the resilience of the system against potential attacks, we attempt to leverage a generative adversarial network learning-assisted authentication framework (GAF). In addition to proposing a new method for validating vehicles, we also introduce a new architectural innovation in the generator–discriminator pair to achieve improved results. The generator sub-network is constructed using an advanced convolutional neural network, whereas the discriminator is designed to leverage global and local information to determine whether a signal is real or fake. On this basis, we propose a signal enhancement-based authentication method, a deep convolutional generative adversarial network (DCGAN). Experimental results using the National Institute of Standards and Technology (NIST) dataset show that the proposed method is effective in denoising and improving the detection performance. Full article
(This article belongs to the Special Issue Control Systems Design for Connected and Autonomous Vehicles)
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19 pages, 4811 KiB  
Article
Improved Fault Diagnosis of Roller Bearings Using an Equal-Angle Integer-Period Array Convolutional Neural Network
by Lin Li, Xiaoxi Yuan, Feng Zhang and Chaobo Chen
Electronics 2024, 13(8), 1576; https://doi.org/10.3390/electronics13081576 - 20 Apr 2024
Viewed by 343
Abstract
This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure the angle intervals between [...] Read more.
This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure the angle intervals between two adjacent data points stayed the same and the signal length was fixed to a pre-determined number of rotation cycles. Then, this one-dimensional signal was transformed into a two-dimensional matrix, where the component of each row was the signal in one period, and the ordinate value of each row represented the corresponding rotation period. Therefore, the row and column indexes of the matrix had a specific meaning instead of simply splitting and stacking the data. Finally, the matrixes were utilized to train the CNN network and test the classification performance. The results show that the classification rate using this technique reaches 95.6%, which is higher than that of 2D CNNs without equal-angle integer-period arrays. Full article
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17 pages, 15461 KiB  
Article
Design and Evaluation of Device Authentication and Secure Communication System with PQC for AIoT Environments
by Yu-Jen Chen, Chien-Lung Hsu, Tzu-Wei Lin and Jung-San Lee
Electronics 2024, 13(8), 1575; https://doi.org/10.3390/electronics13081575 - 20 Apr 2024
Viewed by 467
Abstract
With the rapid development of Internet of Things (IoT) technology, the number of IoT users is growing year after year. IoT will become a part of our daily lives, so it is likely that the security of these devices will be an important [...] Read more.
With the rapid development of Internet of Things (IoT) technology, the number of IoT users is growing year after year. IoT will become a part of our daily lives, so it is likely that the security of these devices will be an important issue in the future. Quantum computing is maturing, and the security threat associated with quantum computing will be faced in the transmissions of IoT devices, which mainly use wireless communication technologies. Therefore, to ensure the protection of transmitted data, a cryptographic algorithm that is efficient in defeating quantum computer attacks needs to be developed. In this paper, we propose a device authentication and secure communication system with post-quantum cryptography (PQC) for AIoT environments using the NTRU and Falcon signature mechanism, which can resist quantum computer attacks and be used in AIoT environments to effectively protect the confidentiality, integrity, and non-repudiation of transmitted data. We also used Raspberry Pi to simulate AIoT devices for implementation. Full article
(This article belongs to the Special Issue Precise Timing and Security in Internet of Things)
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13 pages, 720 KiB  
Article
High Accuracy Localization Scheme Using 1-Bit Side Information: Achievability from a GDoP Perspective
by Suah Park, Jiyoung Hwang, Ilmu Byun and Sang Won Choi
Electronics 2024, 13(8), 1574; https://doi.org/10.3390/electronics13081574 - 20 Apr 2024
Viewed by 515
Abstract
In this paper, we provide a novel methodology for high-precision positioning that utilizes 1-bit additional information, which applies to various positioning techniques. The proposed approach leverages binary information to indicate if a user is within a specified space of interest and refines the [...] Read more.
In this paper, we provide a novel methodology for high-precision positioning that utilizes 1-bit additional information, which applies to various positioning techniques. The proposed approach leverages binary information to indicate if a user is within a specified space of interest and refines the estimated location information outside this area. By matching the estimated locations outside the area of interest with the valid location information within, this methodology corrects the positional data obtained through any arbitrary positioning technique, aligning the estimated positions with the intended spatial boundaries. Performance analysis metrics, such as Average Positioning Error (APE) and Cumulative Distribution Function for positioning coverage, were employed to assess the effectiveness of the proposed methods. Numerical simulations demonstrate how the proposed method enhances the averaged positioning accuracy, significantly outperforming the conventional time of arrival method. Furthermore, the proposed positioning correction methodology demonstrates validated feasibility applicable to an arbitrary existing positioning method. Full article
(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)
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7 pages, 2281 KiB  
Communication
Channel Potential of Bandgap-Engineered Tunneling Oxide (BE-TOX) in Inhibited 3D NAND Flash Memory Strings
by Taeyoung Cho, Sungyeop Jung and Myounggon Kang
Electronics 2024, 13(8), 1573; https://doi.org/10.3390/electronics13081573 - 20 Apr 2024
Viewed by 540
Abstract
In this study, the channel potential of inhibited strings in 3D NAND flash memory using a bandgap-engineered tunneling oxide (BE-TOX) structure is analyzed. The equivalent oxide thickness (EOT) of the structure using BE-TOX was designed to be the same as the conventional 3D [...] Read more.
In this study, the channel potential of inhibited strings in 3D NAND flash memory using a bandgap-engineered tunneling oxide (BE-TOX) structure is analyzed. The equivalent oxide thickness (EOT) of the structure using BE-TOX was designed to be the same as the conventional 3D NAND flash memory, and the channel potentials of the down coupling phenomenon (DCP) and natural local self-boosting (NLSB) effect were analyzed. As a result, the BE-TOX structure was confirmed to have a higher channel potential in the DCP and NLSB than the conventional structure, making it relatively effective for program disturbance. The main reason for the difference in the channel potential between the BE-TOX and conventional structures is that adjacent cells have different threshold voltages (Vth). When the same program voltage (VPGM) and program time (TPGM) were applied during the program operation, Vth decreased in the BE-TOX structure, which increased the channel potential when DCP and NLSB occurred. Finally, a simulation was conducted by varying the thicknesses of the oxide and nitride in the BE-TOX structure. Despite the EOT being fixed and the thicknesses of both nitride and oxide being varied, the channel potential was affected. Full article
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28 pages, 757 KiB  
Review
A Social Perspective on AI in the Higher Education System: A Semisystematic Literature Review
by Budur Turki Alshahrani, Salvatore Flavio Pileggi and Faezeh Karimi
Electronics 2024, 13(8), 1572; https://doi.org/10.3390/electronics13081572 - 19 Apr 2024
Viewed by 1145
Abstract
The application of Artificial Intelligence in Education (AIED) is experiencing widespread interest among students, educators, researchers, and policymakers. AIED is expected, among other things, to enhance learning environments in the higher education system. However, in line with the general trends, there are also [...] Read more.
The application of Artificial Intelligence in Education (AIED) is experiencing widespread interest among students, educators, researchers, and policymakers. AIED is expected, among other things, to enhance learning environments in the higher education system. However, in line with the general trends, there are also increasing concerns about possible negative and collateral effects. The consequent social impact cannot be currently assessed in depth. Balancing benefits with social considerations according to a socio-technical approach is essential for harnessing the true power of AI in a responsible and trustworthy context. This study proposes a semi-systematic literature review of the available knowledge on the adoption of artificial intelligence (AI) in the higher education system. It presents a stakeholder-centric analysis to explore multiple perspectives, including pedagogical, managerial, technological, governmental, external, and social ones. The main goal is to identify and discuss major gaps and challenges in context, looking at the existing body of knowledge and momentum. AIED should encompass pedagogical, ethical, and social dimensions to be properly addressed. This review highlights a not-always-explicit socio-technical perspective. Additionally, this study reveals a significant lack of empirical systematic evaluation of added value and institutional readiness. Because of the broad scope of the study and the intense ongoing debate on the topic, an exhaustive identification of the current body of knowledge is probably unrealistic, so this study aims mainly to identify the mainstream and major trends by looking at the most recent contributions. Full article
(This article belongs to the Special Issue Advanced Research in Technology and Information Systems)
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