Journal Description
Electronics
Electronics
is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2(Electrical and Electronic Engineering) CiteScore - Q2 (Electrical and Electronic Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Electronics include: Magnetism, Signals, Network and Software.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data
Electronics 2024, 13(9), 1771; https://doi.org/10.3390/electronics13091771 (registering DOI) - 03 May 2024
Abstract
Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge
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Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.
Full article
(This article belongs to the Special Issue The Future of IoT: Advanced AI Based IoT Technologies and Applications)
Open AccessArticle
Multispectral Pedestrian Detection Based on Prior-Saliency Attention and Image Fusion
by
Jiaren Guo, Zihao Huang and Yanyun Tao
Electronics 2024, 13(9), 1770; https://doi.org/10.3390/electronics13091770 (registering DOI) - 03 May 2024
Abstract
Detecting pedestrians in varying illumination conditions poses a significant challenge, necessitating the development of innovative solutions. In response to this, we introduce Prior-AttentionNet, a pedestrian detection model featuring a Prior-Attention mechanism. This model leverages the stark contrast between thermal objects and their backgrounds
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Detecting pedestrians in varying illumination conditions poses a significant challenge, necessitating the development of innovative solutions. In response to this, we introduce Prior-AttentionNet, a pedestrian detection model featuring a Prior-Attention mechanism. This model leverages the stark contrast between thermal objects and their backgrounds in far-infrared (FIR) images by employing saliency attention derived from FIR images via UNet. However, extracting salient regions of diverse scales from FIR images poses a challenge for saliency attention. To address this, we integrate Simple Linear Iterative Clustering (SLIC) superpixel segmentation, embedding the segmentation feature map as prior knowledge into UNet’s decoding stage for comprehensive end-to-end training and detection. This integration enhances the extraction of focused attention regions, with the synergy of segmentation prior and saliency attention forming the core of Prior-AttentionNet. Moreover, to enrich pedestrian details and contour visibility in low-light conditions, we implement multispectral image fusion. Experimental evaluations were conducted on the KAIST and OTCBVS datasets. Applying Prior-Attention mode to FIR-RGB images significantly improves the delineation and focus on multi-scale pedestrians. Prior-AttentionNet’s general detector demonstrates the capability of detecting pedestrians with minimal computational resources. The ablation studies indicate that the FIR-RGB+ Prior-Attention mode markedly enhances detection robustness over other modes. When compared to conventional multispectral pedestrian detection models, Prior-AttentionNet consistently surpasses them by achieving higher mean average precision and lower miss rates in diverse scenarios, during both day and night.
Full article
(This article belongs to the Section Computer Science & Engineering)
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Open AccessArticle
TXAI-ADV: Trustworthy XAI for Defending AI Models against Adversarial Attacks in Realistic CIoT
by
Stephn Ojo, Moez Krichen, Meznah A. Alamro and Alaeddine Mihoub
Electronics 2024, 13(9), 1769; https://doi.org/10.3390/electronics13091769 (registering DOI) - 03 May 2024
Abstract
Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT
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Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT and countering emerging risks require constant updates and monitoring of these devices. Machine learning (ML), in combination with Explainable Artificial Intelligence (XAI), has become an essential component of the CIoT ecosystem due to its rapid advancement and impressive results across several application domains for attack detection, prevention, mitigation, and providing explanations of such decisions. These attacks exploit and steal sensitive data, disrupt the devices’ functionality, or gain unauthorized access to connected networks. This research generates a novel dataset by injecting adversarial attacks into the CICIoT2023 dataset. It presents an adversarial attack detection approach named TXAI-ADV that utilizes deep learning (Mutli-Layer Perceptron (MLP) and Deep Neural Network (DNN)) and machine learning classifiers (K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), ensemble voting, and Meta Classifier) to detect attacks and avert such situations rapidly in a CIoT. This study utilized Shapley Additive Explanations (SHAP) techniques, an XAI technique, to analyze the average impact of each class feature on the proposed models and select optimal features for the adversarial attacks dataset. The results revealed that, with a 96% accuracy rate, the proposed approach effectively detects adversarial attacks in a CIoT.
Full article
(This article belongs to the Special Issue Recent Trends and Applications of Artificial Intelligence)
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Open AccessArticle
Optimizing the Timeliness of Hybrid OFDMA-NOMA Sensor Networks with Stability Constraints
by
Wei Wang, Yunquan Dong and Chengsheng Pan
Electronics 2024, 13(9), 1768; https://doi.org/10.3390/electronics13091768 - 03 May 2024
Abstract
In this paper, we analyze the timeliness of a multi-user system in terms of the age of information (AoI) and the corresponding stability region in which the packet rates of users lead to finite queue lengths. Specifically, we consider a hybrid OFDMA-NOMA system
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In this paper, we analyze the timeliness of a multi-user system in terms of the age of information (AoI) and the corresponding stability region in which the packet rates of users lead to finite queue lengths. Specifically, we consider a hybrid OFDMA-NOMA system where the users are partitioned into several groups. While users in each group share the same resource block using non-orthogonal multiple access (NOMA), different groups access the fading channel using orthogonal frequency division multiple access (OFDMA). For this system, we consider three decoding schemes at the service terminals: interfering decoding, which treats signals from other users as interference; serial interference cancellation, which removes signals from other users once they have been decoded; and the enhanced SIC strategy, where the receiver attempts to decode for another user if decoding for a previous user fails. We present the average AoI for each of the three decoding schemes in closed form. Under the constraint of the stable region, we find the minimum AoI of each decoding scheme efficiently. The numerical results show that by optionally choosing the decoding scheme and transmission rate, the hybrid OFDMA-NOMA outperforms conventional OFDMA in terms of both system timeliness and stability.
Full article
(This article belongs to the Special Issue Featured Advances in Real-Time Networks)
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Open AccessArticle
Fast Coding Unit Partitioning Algorithm for Video Coding Standard Based on Block Segmentation and Block Connection Structure and CNN
by
Nana Li, Zhenyi Wang and Qiuwen Zhang
Electronics 2024, 13(9), 1767; https://doi.org/10.3390/electronics13091767 - 02 May 2024
Abstract
The recently introduced Video Coding Standard, VVC, presents a novel Quadtree plus Nested Multi-Type Tree (QTMTT) block structure. This structure enables a more flexible block partition and demonstrates enhanced compression performance compared to its predecessor, HEVC. However, The introduction of the new structure
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The recently introduced Video Coding Standard, VVC, presents a novel Quadtree plus Nested Multi-Type Tree (QTMTT) block structure. This structure enables a more flexible block partition and demonstrates enhanced compression performance compared to its predecessor, HEVC. However, The introduction of the new structure has led to a more complex partition search process, resulting in a considerable increase in time complexity. The QTMTT structure yields diverse Coding Unit (CU) block sizes, posing challenges for CNN model inference. In this study, we propose a representation structure termed Block Segmentation and Block Connection (BSC), rooted in texture features. This ensures that partial CU blocks are uniformly represented in size. To address different-sized CUs, various levels of CNN models are designed for prediction. Moreover, we introduce a post-processing method and a multi-thresholding scheme to further mitigate errors introduced by CNNs. This allows for flexible and adjustable acceleration, achieving a trade-off between coding time complexity and performance. Experimental results indicate that, in comparison to VTM-10.0, our “Fast” scheme reduces the average complexity by 57.14% with a 1.86% increase in BDBR. Meanwhile, the “Moderate” scheme reduces average complexity by 50.14% with only a 1.39% increase in BDBR.
Full article
(This article belongs to the Special Issue Recent Advances in Image/Video Compression and Coding)
Open AccessArticle
No Pain Device: Empowering Personal Safety with an Artificial Intelligence-Based Nonviolence Embedded System
by
Agostino Giorgio
Electronics 2024, 13(9), 1766; https://doi.org/10.3390/electronics13091766 - 02 May 2024
Abstract
This paper presents the development of a novel anti-violence device titled “no pAIn” (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal
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This paper presents the development of a novel anti-violence device titled “no pAIn” (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal safety benefits for individuals across diverse demographics. Operating autonomously, it necessitates no user interaction post-activation. The AI engine conducts real-time speech recognition and effectively discerns genuine instances of aggression from non-violent disputes or conversations. Facilitated by its Internet connectivity, in the event of detected aggression, the device promptly issues assistance requests with real-time precise geolocation tracking to predetermined recipients for immediate assistance. Its compact size enables discreet concealment within commonplace items like candy wrappers, purpose-built casings, or wearable accessories. The device is battery-operated. The prototype was developed using a microcontroller board (Arduino Nano RP2040 Connect), incorporating an omnidirectional microphone and Wi-Fi module, all at a remarkably low cost. Subsequent functionality testing, performed in debug mode using the Arduino IDE serial monitor, yielded successful results. The AI engine exhibited exceptional accuracy in word recognition, complemented by a robust logic implementation, rendering the device highly reliable in discerning genuine instances of aggression from non-violent scenarios.
Full article
(This article belongs to the Special Issue High-Performance Embedded Systems)
Open AccessArticle
Enhancing the Safety of Autonomous Vehicles in Adverse Weather by Deep Learning-Based Object Detection
by
Biwei Zhang, Murat Simsek, Michel Kulhandjian and Burak Kantarci
Electronics 2024, 13(9), 1765; https://doi.org/10.3390/electronics13091765 - 02 May 2024
Abstract
Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle.
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Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle. The suggested approach improves the effectiveness of single-stage object detectors, aiming to advance the performance in perceiving autonomous racing environments and minimizing instances of false detection and low recognition rates. The improved framework is based on the one-stage object-detection model, incorporating multiple lightweight backbones. Additionally, attention mechanisms are integrated to refine the object-detection process further. Our proposed model demonstrates superior performance compared to the state-of-the-art method on the DAWN dataset, achieving a mean average precision (mAP) of 99.1%, surpassing the previous result of 84.7%.
Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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Open AccessArticle
Exploring the Odd–Even Effect, Current Stabilization, and Negative Differential Resistance in Carbon-Chain-Based Molecular Devices
by
Lijun Wang, Liping Zhou, Xuefeng Wang and Wenlong You
Electronics 2024, 13(9), 1764; https://doi.org/10.3390/electronics13091764 - 02 May 2024
Abstract
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The transport properties of molecular devices based on carbon chains are systematically investigated using a combination of non-equilibrium Green’s function (NEGF) and density functional theory (DFT) first-principle methods. In single-carbon-chain molecular devices, a distinct even–odd behavior of the current emerges, primarily influenced by
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The transport properties of molecular devices based on carbon chains are systematically investigated using a combination of non-equilibrium Green’s function (NEGF) and density functional theory (DFT) first-principle methods. In single-carbon-chain molecular devices, a distinct even–odd behavior of the current emerges, primarily influenced by the density of states (DOS) within the chain channel. Additionally, linear, monotonic currents exhibit Ohmic contact characteristics. In ladder-shaped carbon-chain molecular devices, a notable current stabilization behavior is observed, suggesting their potential utility as current stabilizers within circuits. We provide a comprehensive analysis of the transport properties of molecular devices featuring ladder-shaped carbon chains connecting benzene-ring molecules. The occurrence of negative differential resistance (NDR) in the low-bias voltage region is noted, with the possibility of manipulation by adjusting the position of the benzene-ring molecule. These findings offer a novel perspective on the potential applications of atom chains.
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Open AccessArticle
Decentralized Fuzzy Fault Estimation Observer Design for Discrete-Time Nonlinear Interconnected Systems
by
Geun Bum Koo
Electronics 2024, 13(9), 1763; https://doi.org/10.3390/electronics13091763 - 02 May 2024
Abstract
In this paper, a fault estimation technique is proposed for discrete-time nonlinear interconnected systems with uncertain interconnections. To achieve the fault estimation, the decentralized fuzzy observer is adopted based on the Takagi–Sugeno fuzzy model. Based on the estimation error model with the subsystems
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In this paper, a fault estimation technique is proposed for discrete-time nonlinear interconnected systems with uncertain interconnections. To achieve the fault estimation, the decentralized fuzzy observer is adopted based on the Takagi–Sugeno fuzzy model. Based on the estimation error model with the subsystems of the interconnected system and its decentralized fuzzy observer, the fault estimation condition with performance is presented. The main idea of this paper is that a novel inequality condition for performance is used, and the sufficient condition is presented to guarantee the good fault estimation performance. Also, the decentralized fuzzy observer design condition for the fault estimation is converted into linear matrix inequality formats. Finally, a simulation example is provided, and the effectiveness of the proposed fault estimation technique is verified by comparison of the fault estimation performance.
Full article
(This article belongs to the Special Issue Fuzzy Logic and Artificial Intelligence: Emerging Techniques in AI Applications)
Open AccessArticle
Formal Analysis and Detection for ROS2 Communication Security Vulnerability
by
Shuo Yang, Jian Guo and Xue Rui
Electronics 2024, 13(9), 1762; https://doi.org/10.3390/electronics13091762 - 02 May 2024
Abstract
Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 (Robot Operating System
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Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 (Robot Operating System 2) can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this study, we adopted a formal approach to analyze the security of the communication mechanism of ROS2. First, we used a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduced a CIA model based on the established vulnerability model and used linear temporal logic to define its security properties. Then, we designed and implemented a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally tested some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.
Full article
(This article belongs to the Special Issue Cybersecurity Issues in the Internet of Things)
Open AccessArticle
Real-Time Ideation Analyzer and Information Recommender
by
Midhad Blazevic, Lennart B. Sina, Cristian A. Secco, Melanie Siegel and Kawa Nazemi
Electronics 2024, 13(9), 1761; https://doi.org/10.3390/electronics13091761 - 02 May 2024
Abstract
The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although
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The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.
Full article
(This article belongs to the Special Issue Recommender Systems: Approaches, Challenges and Applications (Volume III))
Open AccessArticle
Optimal Parking Space Selection and Vehicle Driving Decision for Autonomous Parking System Based on Multi-Attribute Decision
by
Zhaobo Qin, Mulin Han, Zhe Xing, Hongmao Qin, Ming Gao and Manjiang Hu
Electronics 2024, 13(9), 1760; https://doi.org/10.3390/electronics13091760 - 02 May 2024
Abstract
Autonomous parking systems (APSs) can help drivers complete the task of finding a parking space and the parking operation, which improves driving comfort. Current research on APSs focus on the perception, localization, planning, and control modules, while few pay attention to the decision
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Autonomous parking systems (APSs) can help drivers complete the task of finding a parking space and the parking operation, which improves driving comfort. Current research on APSs focus on the perception, localization, planning, and control modules, while few pay attention to the decision modules. This paper proposes a method for optimal parking space selection and vehicle driving decisions. In terms of selecting the optimal parking space, a multi-attribute decision method is designed considering the type of parking space, walking distance, and other factors. In terms of vehicle driving decisions, we first predict the behavior and trajectory of the target vehicle in a specific scenario, and then use a combination of rule-based and learning-based decision methods for safe and comfortable vehicle driving behavior decisions. Simulation results show that the proposed methods can find the optimal parking space according to the parking lot map and improve the efficiency and smoothness of vehicle driving while ensuring driving safety.
Full article
(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition)
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Open AccessArticle
The Influence of the Design of Antenna and Chip Coupling Circuits on the Performance of Textronic RFID UHF Transponders
by
Anna Ziobro, Piotr Jankowski-Mihułowicz, Mariusz Węglarski and Patryk Pyt
Electronics 2024, 13(9), 1759; https://doi.org/10.3390/electronics13091759 - 02 May 2024
Abstract
The objectives of this study were to design, investigate, and compare different designs of coupling circuits for textronic RFID transponders, particularly focusing on magnetic coupling between an antenna and a chip. The configuration of the inductively coupled antenna module and the microelectronic module
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The objectives of this study were to design, investigate, and compare different designs of coupling circuits for textronic RFID transponders, particularly focusing on magnetic coupling between an antenna and a chip. The configuration of the inductively coupled antenna module and the microelectronic module housing the chip can be varied in several ways. This article explores various geometries of coupling circuits and assesses the effects of altering their dimensions on mutual inductance, chip voltage, and the transponder’s read range. The investigation comprised an analytical description of inductive coupling, calculations of mutual inductance and chip voltage based on simulation models of transponders, and laboratory measurements of the read range for selected configurations. The results obtained from this study demonstrate that various designs of textile transponders are capable of achieving satisfactory read ranges, with some configurations extending beyond 10 m. This significant range provides clothing designers with the flexibility to select transponder designs that best meet their specific aesthetic and functional requirements.
Full article
(This article belongs to the Special Issue RF/Microwave Device and Circuit Integration Technology)
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Open AccessFeature PaperArticle
A First-Order Noise-Shaping SAR ADC with PVT-Insensitive Closed-Loop Dynamic Amplifier and Two CDACs
by
Jaehyeon Nam, Youngha Hwang, Junhyung Kim, Jiwoo Kim and Sang-Gyu Park
Electronics 2024, 13(9), 1758; https://doi.org/10.3390/electronics13091758 - 02 May 2024
Abstract
This paper presents a first-order noise-shaping (NS) successive approximation register (SAR) analog-to-digital converter (ADC) with a process, (supply) voltage, and temperature (PVT)-insensitive closed-loop integrator and data-weighted averaging (DWA). The use of a cascode floating inverter amplifier (FIA)-type dynamic amplifier with high gain enables
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This paper presents a first-order noise-shaping (NS) successive approximation register (SAR) analog-to-digital converter (ADC) with a process, (supply) voltage, and temperature (PVT)-insensitive closed-loop integrator and data-weighted averaging (DWA). The use of a cascode floating inverter amplifier (FIA)-type dynamic amplifier with high gain enables an aggressive noise transfer function while minimizing the power consumption associated with the use of an active filter. In the proposed ADC, the residue is generated by a capacitive digital-to-analog converter (CDAC) employing DWA, which is made possible by employing a second CDAC, which operates after the SAR operation is completed. The proposed ADC is designed with a 28 nm CMOS process with 1 V power supply. The simulation results show that the ADC achieves the SNDR of 71.2 dB and power consumption of 228 μW when operated with a sampling rate of 80 MS/s and oversampling ratio (OSR) of 10. The Schreier figure-of-merit (FoM) is 173.6 dB, and Walden FoM is 9.6 fJ/conversion-step.
Full article
(This article belongs to the Special Issue Analog Circuits and Analog Computing)
Open AccessArticle
Edge HPC Architectures for AI-Based Video Surveillance Applications
by
Federico Rossi and Sergio Saponara
Electronics 2024, 13(9), 1757; https://doi.org/10.3390/electronics13091757 - 02 May 2024
Abstract
The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures.
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The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios.
Full article
(This article belongs to the Special Issue Edge Computing and Tiny Machine Learning in the Internet of Things: Latest Advances and Applications)
Open AccessArticle
A Sentence-Embedding-Based Dashboard to Support Teacher Analysis of Learner Concept Maps
by
Filippo Sciarrone and Marco Temperini
Electronics 2024, 13(9), 1756; https://doi.org/10.3390/electronics13091756 - 02 May 2024
Abstract
Concept mapping is a valuable method to represent a domain of knowledge, also with the aim of supporting educational needs. Students are called upon to construct their own knowledge through a meaningful learning process, linking new concepts to concepts they have already learned,
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Concept mapping is a valuable method to represent a domain of knowledge, also with the aim of supporting educational needs. Students are called upon to construct their own knowledge through a meaningful learning process, linking new concepts to concepts they have already learned, i.e., connecting new knowledge to knowledge they already possess. Moreover, the particular graphic form of a concept map makes it easy for the teacher to construct and interpret both. Consequently, for an educator, the ability to assess concept maps offered by students, facilitated by an automated system, can prove invaluable. This becomes even more apparent in educational settings where there is a large number of students, such as in Massive Open Online Courses. Here, we propose two new measures devised to evaluate the similarity between concept maps based on two deep-learning embedding models: InferSent and Universal Sentence Encoder. An experimental evaluation with a sample of teachers confirms the validity of one such deep-learning model as the baseline of the new similarity measure. Subsequently, we present a proof-of-concept dashboard where the measures are used to encode a concept map in a 2D space point, with the aim of helping teachers monitor students’ concept-mapping activity.
Full article
(This article belongs to the Section Artificial Intelligence)
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Open AccessArticle
Performance Evaluation of the B4 Topology for Implementing Grid-Connected Inverters in Microgrids
by
Enric Torán, Marian Liberos, Iván Patrao, Raúl González-Medina, Gabriel Garcerá and Emilio Figueres
Electronics 2024, 13(9), 1755; https://doi.org/10.3390/electronics13091755 - 02 May 2024
Abstract
The B4 topology is an interesting alternative to the conventional B6 inverter due to its reduced number of parts and lower cost. Although it has been widely used in the past, especially in low-power motor drive applications, its application as a grid-connected inverter
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The B4 topology is an interesting alternative to the conventional B6 inverter due to its reduced number of parts and lower cost. Although it has been widely used in the past, especially in low-power motor drive applications, its application as a grid-connected inverter is an open area of research. In this regard, this paper analyses the feasibility of the B4 inverter topology for grid-connected applications. A versatile 7 kW inverter prototype, which may be configured as B4 and B6, was built, allowing for a comprehensive evaluation of the performance of both topologies. Through an analytical study and experimental tests, the performance of the B4 and B6 topologies was comparatively evaluated in terms of efficiency, total harmonic distortion of line currents, current unbalance, cost, and mean time between failures. The study was carried out in the context of microgrid systems, highlighting their role in the integration of renewable energy and distributed generation.
Full article
(This article belongs to the Special Issue Advancements in Power Electronics Conversion Technologies)
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Open AccessArticle
An Energy-Efficient 12-Bit VCO-Based Incremental Zoom ADC with Fast Phase-Alignment Scheme for Multi-Channel Biomedical Applications
by
Joongyu Kim and Sung-Yun Park
Electronics 2024, 13(9), 1754; https://doi.org/10.3390/electronics13091754 - 02 May 2024
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This paper presents a low-power, energy-efficient, 12-bit incremental zoom analog-to-digital converter (ADC) for multi-channel bio-signal acquisitions. The ADC consists of a 7-stage ring voltage-controlled oscillator (VCO)-based incremental ΔΣ modulator (I-ΔΣM) and an 8-bit successive approximation register (SAR) ADC. The proposed VCO-based I-ΔΣM can
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This paper presents a low-power, energy-efficient, 12-bit incremental zoom analog-to-digital converter (ADC) for multi-channel bio-signal acquisitions. The ADC consists of a 7-stage ring voltage-controlled oscillator (VCO)-based incremental ΔΣ modulator (I-ΔΣM) and an 8-bit successive approximation register (SAR) ADC. The proposed VCO-based I-ΔΣM can provide fast phase-alignment of the ring-VCO to reduce the interval settling time; thereby, the I-ΔΣM can accommodate time-division-multiplexed input signals without phase leakage between consecutive measurements. The SAR ADC also adopts splitting unit capacitors that can support VCM-free tri-level switching and prevent invalid states from the phase frequency detector with minimal logic gates and switches. The proposed ADC has been fabricated in a standard 180 nm standard 1P6M CMOS process, exhibiting a 67-dB peak signal-to-noise ratio, a 74-dB dynamic range, and a Walden figure of merit of 19.12 fJ/c-s, while consuming a power of 3.51 μW with a sampling rate of 100 kS/s.
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Open AccessReview
Sentiment Dimensions and Intentions in Scientific Analysis: Multilevel Classification in Text and Citations
by
Kampatzis Aristotelis, Sidiropoulos Antonis, Diamantaras Konstantinos and Ougiaroglou Stefanos
Electronics 2024, 13(9), 1753; https://doi.org/10.3390/electronics13091753 - 02 May 2024
Abstract
Sentiment Analysis in text, especially text containing scientific citations, is an emerging research field with important applications in the research community. This review explores the field of sentiment analysis by focusing on the interpretation of citations, presenting a detailed description of techniques and
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Sentiment Analysis in text, especially text containing scientific citations, is an emerging research field with important applications in the research community. This review explores the field of sentiment analysis by focusing on the interpretation of citations, presenting a detailed description of techniques and methods ranging from lexicon-based approaches to Machine and Deep Learning models. The importance of understanding both the emotion and the intention behind citations is emphasized, reflecting their critical role in scientific communication. In addition, this study presents the challenges faced by researchers (such as complex scientific terminology, multilingualism, and the abstract nature of scientific discourse), highlighting the need for specialized language processing techniques. Finally, future research directions include improving the quality of datasets as well as exploring architectures and models to improve the accuracy of sentiment detection.
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(This article belongs to the Special Issue Machine Learning Advances and Applications on Natural Language Processing (NLP))
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Open AccessArticle
Ternary Polymer Solar Cells: Impact of Non-Fullerene Acceptors on Optical and Morphological Properties
by
Quentin Eynaud, Tomoyuki Koganezawa, Hidehiro Sekimoto, Mohamed el Amine Kramdi, Gilles Quéléver, Olivier Margeat, Jörg Ackermann, Noriyuki Yoshimoto and Christine Videlot-Ackermann
Electronics 2024, 13(9), 1752; https://doi.org/10.3390/electronics13091752 - 02 May 2024
Abstract
Ternary organic solar cells contain a single three-component photoactive layer with a wide absorption window, achieved without the need for multiple stacking. However, adding a third component into a well-known binary blend can influence the energetics, optical window, charge carrier transport, crystalline order
[...] Read more.
Ternary organic solar cells contain a single three-component photoactive layer with a wide absorption window, achieved without the need for multiple stacking. However, adding a third component into a well-known binary blend can influence the energetics, optical window, charge carrier transport, crystalline order and conversion efficiency. In the form of binary blends, the low-bandgap regioregular polymer donor poly(3-hexylthiophene-2,5-diyl), known as P3HT, is combined with the acceptor PC61BM, an inexpensive fullerene derivative. Two different non-fullerene acceptors (ITIC and eh-IDTBR) are added to this binary blend to form ternary blends. A systematic comparison between binary and ternary systems was carried out as a function of the thermal annealing temperature of organic layers (100 °C and 140 °C). The power conversion efficiency (PCE) is improved due to increased fill factor (FF) and open-circuit voltage (Voc) for thermal-annealed ternary blends at 140 °C. The transport properties of electrons and holes were investigated in binary and ternary blends following a Space-Charge-Limited Current (SCLC) protocol. A favorable balanced hole–electron mobility is obtained through the incorporation of either ITIC or eh-IDTBR. The charge transport behavior is correlated with the bulk heterojunction (BHJ) morphology deduced from atomic force microscopy (AFM), contact water angle (CWA) measurement and 2D grazing-incidence X-ray diffractometry (2D-GIXRD).
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(This article belongs to the Special Issue Recent Advances in New Generation Compound Semiconductor Based Photodetectors and Solar Cells)
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