Electronics

10 pages, 1519 KB

Open AccessEditor’s ChoiceArticle

Proof-of-Concept Quantitative Monitoring of Respiration Using Low-Energy Wearable Piezoelectric Thread

by Kenta Horie, Muhammad Salman Al Farisi, Yoshihiro Hasegawa, Miyoko Matsushima, Tsutomu Kawabe and Mitsuhiro Shikida

Electronics 2024, 13(23), 4577; https://doi.org/10.3390/electronics13234577 - 21 Nov 2024

Cited by 5 | Viewed by 3064

Abstract

Currently, wearable sensors can measure vital sign frequencies, such as respiration rate, but they fall short of providing quantitative data, such as respiratory tidal volume. Meanwhile, the airflow at the mouth carries both the frequency and quantitative respiratory signals. In this study, we [...] Read more.

Currently, wearable sensors can measure vital sign frequencies, such as respiration rate, but they fall short of providing quantitative data, such as respiratory tidal volume. Meanwhile, the airflow at the mouth carries both the frequency and quantitative respiratory signals. In this study, we propose a method to calibrate a wearable piezoelectric thread sensor placed on the chest using mouth airflow for accurate quantitative respiration monitoring. Prior to human trials, we introduced an artificial ventilator as a test subject. To validate the proposed concept, we embedded a miniaturized tube airflow sensor at the ventilator’s outlet, which simulates human respiration, and attached a wearable piezoelectric thread to the piston, which moves periodically to mimic human chest movement. The integrated output readings from the wearable sensor aligned with the airflow rate measurements, demonstrating its ability to accurately monitor not only respiration rate but also quantitative metrics such as respiratory volume. Finally, tidal volume measurement was demonstrated using the wearable piezoelectric thread. Full article

(This article belongs to the Section Electronic Materials, Devices and Applications)

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22 pages, 10759 KB

Open AccessEditor’s ChoiceArticle

Design of a Cyber-Physical System-of-Systems Architecture for Elderly Care at Home

by José Galeas, Alberto Tudela, Óscar Pons, Juan Pedro Bandera and Antonio Bandera

Electronics 2024, 13(23), 4583; https://doi.org/10.3390/electronics13234583 - 21 Nov 2024

Cited by 3 | Viewed by 2906

Abstract

The idea of introducing a robot into an Ambient Assisted Living (AAL) environment to provide additional services beyond those provided by the environment itself has been explored in numerous projects. Moreover, new opportunities can arise from this symbiosis, which usually requires both systems [...] Read more.

The idea of introducing a robot into an Ambient Assisted Living (AAL) environment to provide additional services beyond those provided by the environment itself has been explored in numerous projects. Moreover, new opportunities can arise from this symbiosis, which usually requires both systems to share the knowledge (and not just the data) they capture from the context. Thus, by using knowledge extracted from the raw data captured by the sensors deployed in the environment, the robot can know where the person is and whether he/she should perform some physical exercise, as well as whether he/she should move a chair away to allow the robot to successfully complete a task. This paper describes the design of an Ambient Assisted Living system where an IoT scheme and robot coexist as independent but connected elements, forming a cyber-physical system-of-systems architecture. The IoT environment includes cameras to monitor the person’s activity and physical position (lying down, sitting…), as well as non-invasive sensors to monitor the person’s heart or breathing rate while lying in bed or sitting in the living room. Although this manuscript focuses on how both systems handle and share the knowledge they possess about the context, a couple of example use cases are included. In the first case, the environment provides the robot with information about the positions of objects in the environment, which allows the robot to augment the metric map it uses to navigate, detecting situations that prevent it from moving to a target. If there is a person nearby, the robot will approach them to ask them to move a chair or open a door. In the second case, even more use is made of the robot’s ability to interact with the person. When the IoT system detects that the person has fallen to the ground, it passes this information to the robot so that it can go to the person, talk to them, and ask for external help if necessary. Full article

(This article belongs to the Special Issue Emerging Artificial Intelligence Technologies and Applications)

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28 pages, 4190 KB

Open AccessEditor’s ChoiceArticle

Know Your Grip: Real-Time Holding Posture Recognition for Smartphones

by Rene Hörschinger, Marc Kurz and Erik Sonnleitner

Electronics 2024, 13(23), 4596; https://doi.org/10.3390/electronics13234596 - 21 Nov 2024

Viewed by 3304

Abstract

This paper introduces a model that predicts four common smartphone-holding postures, aiming to enhance user interface adaptability. It is unique in being completely independent of platform and hardware, utilizing the inertial measurement unit (IMU) for real-time posture detection based on sensor data collected [...] Read more.

This paper introduces a model that predicts four common smartphone-holding postures, aiming to enhance user interface adaptability. It is unique in being completely independent of platform and hardware, utilizing the inertial measurement unit (IMU) for real-time posture detection based on sensor data collected around tap gestures. The model identifies whether the user is holding and operating the smartphone with one hand or using both hands in different configurations. For model training and validation, sensor time series data undergo extensive feature extraction, including statistical, frequency, magnitude, and wavelet analyses. These features are incorporated into 74 distinct sets, tested across various machine learning frameworks—k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF)—and evaluated for their effectiveness using metrics such as cross-validation scores, test accuracy, Kappa statistics, confusion matrices, and ROC curves. The optimized model demonstrates a high degree of accuracy, successfully predicting the holding hand with a 95.7% success rate. This approach highlights the potential of leveraging sensor data to improve mobile user experiences by adapting interfaces to natural user interactions. Full article

(This article belongs to the Special Issue Applied Machine Learning in Intelligent Systems)

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18 pages, 2568 KB

Open AccessEditor’s ChoiceArticle

ATGT3D: Animatable Texture Generation and Tracking for 3D Avatars

by Fei Chen and Jaeho Choi

Electronics 2024, 13(22), 4562; https://doi.org/10.3390/electronics13224562 - 20 Nov 2024

Cited by 1 | Viewed by 2278

Abstract

We propose the ATGT3D an Animatable Texture Generation and Tracking for 3D Avatars, featuring the innovative design of the Eye Diffusion Module (EDM) and Pose Tracking Diffusion Module (PTDM), which are dedicated to high-quality eye texture generation and synchronized tracking of dynamic poses [...] Read more.

We propose the ATGT3D an Animatable Texture Generation and Tracking for 3D Avatars, featuring the innovative design of the Eye Diffusion Module (EDM) and Pose Tracking Diffusion Module (PTDM), which are dedicated to high-quality eye texture generation and synchronized tracking of dynamic poses and textures, respectively. Compared to traditional GAN and VAE methods, ATGT3D significantly enhances texture consistency and generation quality in animated scenes using the EDM, which produces high-quality full-body textures with detailed eye information using the HUMBI dataset. Additionally, the Pose Tracking and Diffusion Module (PTDM) monitors human motion parameters utilizing the BEAT2 and AMASS mesh-level animatable human model datasets. The EDM, in conjunction with a basic texture seed featuring eyes and the diffusion model, restores high-quality textures, whereas the PTDM, by integrating MoSh++ and SMPL-X body parameters, models hand and body movements from 2D human images, thus providing superior 3D motion capture datasets. This module maintains the synchronization of textures and movements over time to ensure precise animation texture tracking. During training, the ATGT3D model uses the diffusion model as the generative backbone to produce new samples. The EDM improves the texture generation process by enhancing the precision of eye details in texture images. The PTDM involves joint training for pose generation and animation tracking reconstruction. Textures and body movements are generated individually using encoded prompts derived from masked gestures. Furthermore, ATGT3D adaptively integrates texture and animation features using the diffusion model to enhance both fidelity and diversity. Experimental results show that ATGT3D achieves optimal texture generation performance and can flexibly integrate predefined spatiotemporal animation inputs to create comprehensive human animation models. Our experiments yielded unexpectedly positive outcomes. Full article

(This article belongs to the Special Issue AI for Human Collaboration)

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38 pages, 13063 KB

Open AccessEditor’s ChoiceReview

Power Converters for Green Hydrogen: State of the Art and Perspectives

by Gianpaolo Vitale

Electronics 2024, 13(22), 4565; https://doi.org/10.3390/electronics13224565 - 20 Nov 2024

Cited by 16 | Viewed by 6225

Abstract

This paper provides a comprehensive review and outlook on power converters devised for supplying polymer electrolyte membrane (PEM) electrolyzers from photovoltaic sources. The produced hydrogen, known as green hydrogen, is a promising solution to mitigate the dependence on fossil fuels. The main topologies [...] Read more.

This paper provides a comprehensive review and outlook on power converters devised for supplying polymer electrolyte membrane (PEM) electrolyzers from photovoltaic sources. The produced hydrogen, known as green hydrogen, is a promising solution to mitigate the dependence on fossil fuels. The main topologies of power conversion systems are discussed and classified; a loss analysis emphasizes the issues concerning the electrolyzer supply. The attention is focused on power converters of rated power up to a tenth of a kW, since it is a promising field for a short-term solution implementing green hydrogen production as a decentralized. It is also encouraged by the proliferation of relatively cheap photovoltaic low-power plants. The main converters proposed by the literature in the last few years and realized for practical applications are analyzed, highlighting their key characteristics and focusing on the parameters useful for designers. Future perspectives are addressed concerning the availability of new wide-bandgap devices and hard-to-abate sectors with reference to the whole conversion chain. Full article

(This article belongs to the Special Issue Advances in Power Converter Design, Control and Applications)

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18 pages, 1757 KB

Open AccessEditor’s ChoiceArticle

End-to-End Deployment of Winograd-Based DNNs on Edge GPU

by Pierpaolo Mori, Mohammad Shanur Rahman, Lukas Frickenstein, Shambhavi Balamuthu Sampath, Moritz Thoma, Nael Fasfous, Manoj Rohit Vemparala, Alexander Frickenstein, Walter Stechele and Claudio Passerone

Electronics 2024, 13(22), 4538; https://doi.org/10.3390/electronics13224538 - 19 Nov 2024

Cited by 6 | Viewed by 3072

Abstract

The Winograd algorithm reduces the computational complexity of convolutional neural networks (CNNs) by minimizing the number of multiplications required for convolutions, making it particularly suitable for resource-constrained edge devices. Concurrently, most edge hardware accelerators utilize 8-bit integer arithmetic to enhance energy efficiency and [...] Read more.

The Winograd algorithm reduces the computational complexity of convolutional neural networks (CNNs) by minimizing the number of multiplications required for convolutions, making it particularly suitable for resource-constrained edge devices. Concurrently, most edge hardware accelerators utilize 8-bit integer arithmetic to enhance energy efficiency and reduce inference latency, requiring the quantization of CNNs before deployment. Combining Winograd-based convolution with quantization offers the potential for both performance acceleration and reduced energy consumption. However, prior research has identified significant challenges in this combination, particularly due to numerical instability and substantial accuracy degradation caused by the transformations required in the Winograd domain, making the two techniques incompatible on edge hardware. In this work, we describe our latest training scheme, which addresses these challenges, enabling the successful integration of Winograd-accelerated convolution with low-precision quantization while maintaining high task-related accuracy. Our approach mitigates the numerical instability typically introduced during the transformation, ensuring compatibility between the two techniques. Additionally, we extend our work by presenting a custom-optimized CUDA implementation of quantized Winograd convolution for NVIDIA edge GPUs. This implementation takes full advantage of the proposed training scheme, achieving both high computational efficiency and accuracy, making it a compelling solution for edge-based AI applications. Our training approach enables significant MAC reduction with minimal impact on prediction quality. Furthermore, our hardware results demonstrate up to a 3.4× latency reduction for specific layers, and a 1.44× overall reduction in latency for the entire DeepLabV3 model, compared to the standard implementation. Full article

(This article belongs to the Section Artificial Intelligence)

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16 pages, 29661 KB

Open AccessEditor’s ChoiceArticle

6.5 kV SiC PiN and JBS Diodes’ Comparison in Hybrid and Full SiC Switch Topologies

by Lucas Barroso Spejo, Lars Knoll and Renato Amaral Minamisawa

Electronics 2024, 13(22), 4548; https://doi.org/10.3390/electronics13224548 - 19 Nov 2024

Cited by 5 | Viewed by 3115

Abstract

This work investigates the performance of state-of-the-art non-commercial 6.5 kV Silicon Carbide (SiC) PiN and Junction Barrier Schottky (JBS) diodes in hybrid (Si IGBT with SiC diode) and full SiC (SiC MOSFET with SiC diode) switch topologies. The static and dynamic performance has [...] Read more.

This work investigates the performance of state-of-the-art non-commercial 6.5 kV Silicon Carbide (SiC) PiN and Junction Barrier Schottky (JBS) diodes in hybrid (Si IGBT with SiC diode) and full SiC (SiC MOSFET with SiC diode) switch topologies. The static and dynamic performance has been systematically evaluated at distinct temperatures, gate resistances and currents for each configuration. The SiC PiN diode presented higher current density capability and lower leakage current density than the JBS diode. Moreover, in most cases, the SiC PiN diode-based topologies demonstrated slightly higher total switching losses compared to the SiC JBS diode-based equivalent configurations. A loadability analysis in a three-level NPC converter is presented to evaluate the potential of each configuration in a converter application. The SiC PiN technology presented a 25% power extension compared to the SiC JBS technology with similar efficiency at typical industrial drives switching frequency operation when comparing same-active-area diode technologies. Finally, a long-term reliability test (H3TRB) is presented to demonstrate the SiC PiN diode technology’s potential for operation in harsh environments. Such characteristics show the advantage of the 6.5 kV SiC PiN diode when a high current density (>100 A/cm²), high efficiency and reliability are required. Full article

(This article belongs to the Special Issue Advances in Power Converter Design, Control and Applications)

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26 pages, 7116 KB

Open AccessEditor’s ChoiceArticle

Virtual Generator to Replace Backup Diesel GenSets Using Backstepping Controlled NPC Multilevel Converter in Islanded Microgrids with Renewable Energy Sources

by J. Dionísio Barros, J. Fernando A. Silva and Luis Rocha

Electronics 2024, 13(22), 4511; https://doi.org/10.3390/electronics13224511 - 17 Nov 2024

Cited by 2 | Viewed by 1890

Abstract

This work presents an islanded microgrid energy system that uses backstepping control applied to neutral point clamped (NPC) multilevel converters coupled with batteries to behave as virtual generators, able to absorb surplus renewable energy, therefore increasing the penetration of renewable energy sources. Additionally, [...] Read more.

This work presents an islanded microgrid energy system that uses backstepping control applied to neutral point clamped (NPC) multilevel converters coupled with batteries to behave as virtual generators, able to absorb surplus renewable energy, therefore increasing the penetration of renewable energy sources. Additionally, on a charged battery the virtual generator allows turning-off the backup diesel generator set (GenSet). Aside from improving energy efficiency, the battery-connected multilevel converter aims to regulate frequency, improves power quality, and keeps the microgrid operational in the event of a GenSet failure. The backstepping controlled NPC multilevel converter emulates a virtual generator injecting power to perform as the primary and secondary microgrid frequency controller. Additionally, AC voltage control is implemented, which enables running the islanded microgrid only with multilevel converters, supplied by the battery while integrating solar and wind energy sources. Energy demand and renewable energy forecasts are used to manage the battery state-of-charge. Simulation results, obtained from switched and phasor models show that energy storage and the backstepping frequency control enables the compensation of power fluctuations from renewable energy sources. Furthermore, in the event of the main GenSet failure, the controlled virtual generator keeps the microgrid running for a few minutes, until another GenSet is ready to supply the microgrid. Therefore, the microgrid integration of the battery-connected multilevel converter results in a significant boost in energy efficiency by allowing the disconnection of the backup GenSet. Full article

(This article belongs to the Special Issue Multilevel Converters for Large-Scale Grid-Connected Systems)

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19 pages, 3256 KB

Open AccessEditor’s ChoiceReview

Carbon Quantum Dots as Phosphors in LEDs: Perspectives and Limitations—A Critical Review of the Literature

by Katarzyna Bucka, Robert P. Socha and Marek Wojnicki

Electronics 2024, 13(22), 4481; https://doi.org/10.3390/electronics13224481 - 15 Nov 2024

Cited by 9 | Viewed by 4803

Abstract

The enduring interest in carbon quantum dots (CQDs) as photoluminescent material arises from their significant advantages over inorganic quantum dots (QDs), such as low toxicity and biocompatibility, which enables their application in bioimaging and drug delivery. This review is focused on the use [...] Read more.

The enduring interest in carbon quantum dots (CQDs) as photoluminescent material arises from their significant advantages over inorganic quantum dots (QDs), such as low toxicity and biocompatibility, which enables their application in bioimaging and drug delivery. This review is focused on the use of CQDs for light emitting devices (LED) technology and provides a guide on how to synthesize CQDs that emit blue, green, and red light, which is necessary to produce RGB LEDs. Consideration was given to the precursors, solvents, methods, and conditions of the processes, the excitation wavelength, the emission wavelength, and the photoluminescence quantum yield (QY). These unique, organic nanoparticles have the potential to revolutionize lighting and, above all, the electronics market due to their low cost and eco-friendliness, as well as the possibility of using various precursors, including waste. Full article

(This article belongs to the Special Issue Feature Papers in Electronic Materials)

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24 pages, 15110 KB

Open AccessEditor’s ChoiceArticle

Embedding Hierarchical Tree Structure of Concepts in Knowledge Graph Embedding

by Jibin Yu, Chunhong Zhang, Zheng Hu and Yang Ji

Electronics 2024, 13(22), 4486; https://doi.org/10.3390/electronics13224486 - 15 Nov 2024

Viewed by 5525

Abstract

Knowledge Graph Embedding aims to encode both entities and relations into a continuous low-dimensional vector space, which is crucial for knowledge-driven application scenarios. As abstract entities in knowledge graphs, concepts inherently possess unique hierarchical structures and encompass rich semantic information. Although existing methods [...] Read more.

Knowledge Graph Embedding aims to encode both entities and relations into a continuous low-dimensional vector space, which is crucial for knowledge-driven application scenarios. As abstract entities in knowledge graphs, concepts inherently possess unique hierarchical structures and encompass rich semantic information. Although existing methods for jointly embedding concepts and instances achieve promising performance, they still face two issues: (1) They fail to explicitly reconstruct the hierarchical tree structure of concepts in the embedding space; (2) They ignore disjoint concept pairs and overlapping concept pairs derived from concepts. In this paper, we propose a novel concept representation approach, called Hyper Spherical Cone Concept Embedding (HCCE), to explicitly model the hierarchical tree structure of concepts in the embedding space. Specifically, HCCE represents each concept as a hyperspherical cone and each instance as a vector, maintaining the anisotropy of concept embeddings. We propose two variant methods to explore the impact of embedding concepts and instances in the same or different spaces. Moreover, we design score functions for disjoint concept pairs and overlapping concept pairs, using relative position relations to incorporate them seamlessly into our geometric models. Experimental results on three benchmark datasets show that HCCE outperforms most existing state-of-the-art methods on concept-related triples and achieves competitive results on instance-related triples. The visualization of embedding results intuitively shows the hierarchical tree structure of concepts in the embedding space. Full article

(This article belongs to the Special Issue Knowledge Representation and Reasoning in Artificial Intelligence)

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14 pages, 3424 KB

Open AccessEditor’s ChoiceArticle

Directorial Editing: A Hybrid Deep-Learning Approach to Content-Aware Image Retargeting and Resizing

by Elliot Dickman and Paul Diefenbach

Electronics 2024, 13(22), 4459; https://doi.org/10.3390/electronics13224459 - 14 Nov 2024

Cited by 2 | Viewed by 1984

Abstract

Image retargeting is a common computer graphics task which involves manipulating the size or aspect ratio of an image. This task often presents a challenge to the artist or user, because manipulating the size of an image necessitates some degree of data loss [...] Read more.

Image retargeting is a common computer graphics task which involves manipulating the size or aspect ratio of an image. This task often presents a challenge to the artist or user, because manipulating the size of an image necessitates some degree of data loss as pixels need to be removed to accommodate a different image size. We present an image retargeting framework which implements a confidence map generated by a segmentation model for content-aware resizing, allowing users to specify which subjects in an image to preserve using natural language prompts much like the role of an art director conversing with their artist. Using computer vision models to detect object positions also provides additional control over the composition of the retargeted image at various points in the image-processing pipeline. This object-based approach to energy map augmentation is incredibly flexible, because only minor adjustments to the processing of the energy maps can provide a significant degree of control over where seams—paths of pixels through the image—are removed, and how seam removal is prioritized in different sections of the image. It also provides additional control with techniques for object and background separation and recomposition. This research explores how several different types of deep-learning models can be integrated into this pipeline in order to easily make these decisions, and provide different retargeting results on the same image based on user input and compositional considerations. Because this is a framework based on existing machine-learning models, this approach will benefit from advancements in the rapidly developing fields of computer vision and large language models and can be extended for further natural language directorial controls over images. Full article

(This article belongs to the Special Issue Deep Learning in Image Processing and Computer Vision)

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14 pages, 617 KB

Open AccessEditor’s ChoiceCommunication

A Novel Mathematical Approach for Inductor-Current Expressions Definition in Multilevel Dual-Active-Bridge Converters

by Eudald Borrell-Pons, Oriol Esquius-Mas, Alber Filba-Martinez and Lluís Trilla

Electronics 2024, 13(22), 4476; https://doi.org/10.3390/electronics13224476 - 14 Nov 2024

Cited by 2 | Viewed by 2137

Abstract

The study of multilevel dual-active-bridge (DAB) converters has garnered significant attention in recent years thanks to their advantages with respect to the conventional two-level (2L) DAB; namely, its greater performance and its capability to operate at higher voltage. The analysis of the converter [...] Read more.

The study of multilevel dual-active-bridge (DAB) converters has garnered significant attention in recent years thanks to their advantages with respect to the conventional two-level (2L) DAB; namely, its greater performance and its capability to operate at higher voltage. The analysis of the converter high-frequency inductor current (

i_{L}

) is crucial, for instance, to compute its root mean square (RMS) value, required to estimate the conduction losses in the converter. The mathematical expression of

i_{L}

is piecewise and multiple variations, i.e., modes, exist depending on the modulation parameter values. This increases the complexity of converter performance analytical study. Thus, a more practical and generalizable expression of

i_{L}

current is desirable. This paper proposes novel compact analytic expressions for the instantaneous and RMS inductor current in the 2L-NL DAB converter, leveraging binary functions to define the piecewise intervals and to identify the mode as a function of the modulation parameter values. The proposed method paves the way for more simple and computationally efficient DAB performance optimization software tools that allow exploring any given converter structures and modulation strategies. Full article

(This article belongs to the Special Issue Efficient and Reliable DC–DC Converters and Related Industrial Electronics)

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15 pages, 3664 KB

Open AccessEditor’s ChoiceArticle

Literacy Deep Reinforcement Learning-Based Federated Digital Twin Scheduling for the Software-Defined Factory

by Jangsu Ahn, Seongjin Yun, Jin-Woo Kwon and Won-Tae Kim

Electronics 2024, 13(22), 4452; https://doi.org/10.3390/electronics13224452 - 13 Nov 2024

Cited by 10 | Viewed by 4017

Abstract

As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized [...] Read more.

As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized options, limiting its ability to meet a wide range of needs. To address this issue, a new manufacturing concept called the software-defined factory has emerged. It is an autonomous manufacturing system that provides reconfigurable manufacturing services to produce tailored products. Reinforcement learning has been suggested for flexible scheduling to satisfy user requirements. However, fixed rule-based methods struggle to accommodate conflicting needs. This study proposes a novel federated digital twin scheduling that combines large language models and deep reinforcement learning algorithms to meet diverse user requirements in the software-defined factory. The large language model-based literacy module analyzes requirements in natural language and assigns weights to digital twin attributes to achieve highly relevant KPIs, which are used to guide scheduling decisions. The deep reinforcement learning-based scheduling module optimizes scheduling by selecting the job and machine with the maximum reward. Different types of user requirements, such as reducing manufacturing costs and improving productivity, are input and evaluated by comparing the flow-shop scheduling with job-shop scheduling based on reinforcement learning. Experimental results indicate that in requirement case 1 (the manufacturing cost), the proposed method outperforms flow-shop scheduling by up to 14.9% and job-shop scheduling by 5.6%. For requirement case 2 (productivity), it exceeds the flow-shop method by up to 13.4% and the job-shop baseline by 7.2%. The results confirm that the literacy DRL scheduling proposed in this paper can handle the individual characteristics of requirements. Full article

(This article belongs to the Special Issue Metaverse and Digital Twins, 2nd Edition)

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26 pages, 2800 KB

Open AccessEditor’s ChoiceArticle

Reflective Dialogues with a Humanoid Robot Integrated with an LLM and a Curated NLU System for Positive Behavioral Change in Older Adults

by Ryan Browne, Mirza Mohtashim Alam, Qasid Saleem, Abrar Hyder, Tatsuya Kudo, Francesca D’Agresti, Martino Maggio, Keiko Homma, Eerik-Juhanna Siitonen, Naoko Kounosu, Kristiina Jokinen, Michael McTear, Giulio Napolitano, Kyoungsook Kim, Junichi Tsujii, Rainer Wieching, Toshimi Ogawa and Yasuyuki Taki

Electronics 2024, 13(22), 4364; https://doi.org/10.3390/electronics13224364 - 7 Nov 2024

Cited by 6 | Viewed by 4197

Abstract

We developed an innovative system that combines Natural Language Understanding (NLU), a curated knowledge base, and the efficient management of a Large Language Model (LLM) to support motivational health coaching. Using Rasa as the core framework, we enhanced it by integrating the GPT-3.5-turbo [...] Read more.

We developed an innovative system that combines Natural Language Understanding (NLU), a curated knowledge base, and the efficient management of a Large Language Model (LLM) to support motivational health coaching. Using Rasa as the core framework, we enhanced it by integrating the GPT-3.5-turbo model. Users opt into reflective dialogues during conversations. When they respond to open-ended questions, their input goes directly to the GPT-3.5-turbo model, allowing for more flexible responses. To provide curated trustworthy content, we integrated a knowledge provision component that searches a PDF-based knowledge base and generates user-friendly responses using Retrieval-Augmented Generation. We tested the system in a real-world scenario by deploying it on a Nao robot in seven older adults’ homes for 1–2 weeks, encouraging positive behavioral changes in some users. Our system serves as a valuable foundation for building an even more integrated, personalized system that can connect with other Application Programing Interfaces (APIs) and integrate with home sensors and edge devices. Full article

(This article belongs to the Special Issue Human-Computer Interactions in E-health)

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13 pages, 7328 KB

Open AccessEditor’s ChoiceArticle

Analysis of Shielding Performance in Double-Layered Enclosures with Integrated Absorbers

by Jong Hwa Kwon, Chang-Hee Hyoung and Hyun Ho Park

Electronics 2024, 13(22), 4345; https://doi.org/10.3390/electronics13224345 - 6 Nov 2024

Cited by 6 | Viewed by 2986

Abstract

Generally, various technologies, including waveguide below cutoff (WBC), gasket sealing, and bonding, are employed in metallic enclosures to achieve the high electromagnetic shielding performance required for EMP protection and EMC countermeasures in shielding structures or facilities. While the shielding structure or facility is [...] Read more.

Generally, various technologies, including waveguide below cutoff (WBC), gasket sealing, and bonding, are employed in metallic enclosures to achieve the high electromagnetic shielding performance required for EMP protection and EMC countermeasures in shielding structures or facilities. While the shielding structure or facility is properly constructed and maintained according to design specifications, its electromagnetic shielding performance can remain at the required level, effectively protecting internal electrical and electronic equipment from external electromagnetic interference. However, unintended apertures often occur during the construction or maintenance of shielding facilities, compromising their shielding performance. Therefore, it is crucial to develop technologies that prevent shielding effectiveness degradation caused by both intentional and unintentional apertures. This paper proposes a structure incorporating a composite absorber (made of dielectric and magnetic absorber) within a double metal panel of enclosure featuring an aperture, aimed at maintaining and improving the facility’s shielding performance. The effectiveness of the proposed structure was validated through numerical simulation. Full article

(This article belongs to the Special Issue Radiation Effects of Advanced Electronic Devices and Circuits, 2nd Edition)

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25 pages, 11090 KB

Open AccessEditor’s ChoiceArticle

Analysis of Molding Defection in IC Packaging and Testing Process

by Bao Rong Chang, Hsiu-Fen Tsai and Chen-Chia Chen

Electronics 2024, 13(22), 4356; https://doi.org/10.3390/electronics13224356 - 6 Nov 2024

Cited by 1 | Viewed by 4075

Abstract

Molding injects a molding compound into a mold to form a protective shell around the wafer. During the injection process, overflow may occur, leading to mold flash, which reduces yield and causes significant manufacturing cost losses. This paper proposes a deep-learning-based method for [...] Read more.

Molding injects a molding compound into a mold to form a protective shell around the wafer. During the injection process, overflow may occur, leading to mold flash, which reduces yield and causes significant manufacturing cost losses. This paper proposes a deep-learning-based method for detecting and predicting the occurrence of mold flash probability to address this issue. First, the paper conducts random forest importance analysis and correlation analysis to identify the key parameters that significantly impact mold flash. This paper uses these key parameters as input signals for the prediction model. The paper introduces an HLGA Transformer to construct an ensemble meta-learning model that predicts the probability of molding defects, achieving a prediction accuracy of 98.16%. The ensemble meta-learning approach proposed in this paper outperforms other methods in terms of performance. The model predictions can be communicated to the system in real time, allowing it to promptly adjust critical machine operation parameters, thereby significantly improving the molding process yield and reducing substantial manufacturing cost losses. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence, Machine Learning, Deep Learning, and Explainable AI (XAI))

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22 pages, 5688 KB

Open AccessEditor’s ChoiceArticle

Closing the Gap Between Electrical and Physical Design Steps with an Analog IC Placement Optimizer Enhanced with Machine-Learning-Based Post-Layout Performance Regressors

by Ricardo Martins

Electronics 2024, 13(22), 4360; https://doi.org/10.3390/electronics13224360 - 6 Nov 2024

Cited by 6 | Viewed by 3213

Abstract

The design of integrated circuits in the analog spectrum is intricate due to the signals’ continuous nature. Additionally, it is strongly affected by the physical implementation of their devices and interconnections on the layout, a design task that has stubbornly defied all automation [...] Read more.

The design of integrated circuits in the analog spectrum is intricate due to the signals’ continuous nature. Additionally, it is strongly affected by the physical implementation of their devices and interconnections on the layout, a design task that has stubbornly defied all automation attempts. In this paper, one limitative factor is identified that must be addressed to finally push automation tools into the analog integrated circuit design flow: accurate assessment of post-layout performance degradation. For this purpose, a performance-driven placement generator highly integrated with off-the-shelf tools already adopted by circuit/layout designers, i.e., circuit simulator, verification tools (layout-versus-schematic) and layout extractor, is proposed. Toward maximum post-layout accuracy, this generator promotes an exhaustive simulation-based synthesis, extracting, simulating and verifying the post-layout functional behavior of every candidate floorplan. Additionally, to bypass the time-consuming extractions/simulations and accelerate synthesis, novel post-layout performance regressors based on different highly accurate machine learning techniques are also being developed. The data used to train them can be directly and conveniently acquired from previous precise post-placement simulations. Experimental results over two analog circuit structures show that a set of performance regressors based on tree-based models, while operating on compressed design spaces, allow for the speeding up of synthesis by more than 20×, which represents a step toward an efficient fully automatic performance-driven analog integrated circuit design flow. Full article

(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)

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10 pages, 3712 KB

Open AccessEditor’s ChoiceArticle

White Light Generation and Stability Analysis of High-Power Blue LDs with Remote YAG Phosphors

by Seung-Min Lee, Jae-Hyoung Ryu and Hong-Shik Lee

Electronics 2024, 13(22), 4336; https://doi.org/10.3390/electronics13224336 - 5 Nov 2024

Cited by 8 | Viewed by 2226

Abstract

This paper presents a comprehensive analysis of white light generation and the associated aging dynamics using high-power blue laser diodes (LDs) combined with transmissive single crystal remote YAG phosphors. By systematically varying input currents (ranging from 0.6 A to 3 A) and phosphor [...] Read more.

This paper presents a comprehensive analysis of white light generation and the associated aging dynamics using high-power blue laser diodes (LDs) combined with transmissive single crystal remote YAG phosphors. By systematically varying input currents (ranging from 0.6 A to 3 A) and phosphor thicknesses (250 μm and 500 μm), this study elucidates the optical and electrical characteristics of LD-phosphor systems under diverse operating conditions. The results highlight the system’s potential for stable and efficient white light generation, making it suitable for high-power lighting applications. Experimental setups included both single LDs and a 4 × 2 LD array. For the single LD, a peak optical output of 4.16 W was achieved at 3 A, corresponding to an initial luminous flux of approximately 700 Lm and a correlated color temperature (CCT) of 4653 K, with minimal color temperature shift observed during a 60 min aging process. The 4 × 2 LD array demonstrated consistent white light output across varying phosphor thicknesses, with maximum luminous fluxes of 1857 Lm at 1.4 A and 2622 Lm at 1.6 A for phosphor thicknesses of 250 μm and 500 μm, respectively. Importantly, the phosphor exhibited excellent thermal stability throughout the aging process, with the CCT maintained within a range of 4600 K to 5500 K. These findings underscore the reliability and applicability of LD-based white light systems in demanding high-power lighting environments, offering a promising alternative to conventional light sources for automotive, industrial, and specialized lighting applications. Full article

(This article belongs to the Section Optoelectronics)

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37 pages, 796 KB

Open AccessEditor’s ChoiceArticle

Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming

by Dionisios N. Sotiropoulos, Gregory Koronakos and Spyridon V. Solanakis

Electronics 2024, 13(21), 4324; https://doi.org/10.3390/electronics13214324 - 4 Nov 2024

Cited by 9 | Viewed by 4533

Abstract

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study [...] Read more.

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models. Full article

(This article belongs to the Special Issue Explainability in AI and Machine Learning)

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18 pages, 3413 KB

Open AccessEditor’s ChoiceArticle

Diagnosing and Characterizing Chronic Kidney Disease with Machine Learning: The Value of Clinical Patient Characteristics as Evidenced from an Open Dataset

by Juan Figueroa, Patrick Etim, Adithyan Karanathu Shibu, Derek Berger and Jacob Levman

Electronics 2024, 13(21), 4326; https://doi.org/10.3390/electronics13214326 - 4 Nov 2024

Cited by 8 | Viewed by 6482

Abstract

Applying artificial intelligence (AI) and machine learning for chronic kidney disease (CKD) diagnostics and characterization has the potential to improve the standard of patient care through accurate and early detection, as well as providing a more detailed understanding of the condition. This study [...] Read more.

Applying artificial intelligence (AI) and machine learning for chronic kidney disease (CKD) diagnostics and characterization has the potential to improve the standard of patient care through accurate and early detection, as well as providing a more detailed understanding of the condition. This study employed reproducible validation of AI technology with public domain software applied to CKD diagnostics on a publicly available CKD dataset acquired from 400 patients. The approach presented includes patient-specific symptomatic variables and demonstrates performance improvements associated with this approach. Our best-performing AI models, which include patient symptom variables, achieve predictive accuracies ranging from 99.4 to 100% across both hold-out and 5-fold validation with the light gradient boosting machine. We demonstrate that the exclusion of patient symptom variables reduces model performance in line with the literature on the same dataset. We also provide an unsupervised learning cluster analysis to help interpret variability among, and characterize the population of, patients with CKD. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications)

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16 pages, 15088 KB

Open AccessEditor’s ChoiceArticle

Impact of Air Gaps Between Microstrip Line and Magnetic Sheet on Near-Field Magnetic Shielding

by Hyun Ho Park, Eakhwan Song, Jiseong Kim and Cheolsoo Kim

Electronics 2024, 13(21), 4313; https://doi.org/10.3390/electronics13214313 - 2 Nov 2024

Cited by 3 | Viewed by 2540

Abstract

This study experimentally analyzed the impact of air gaps between a magnetic sheet and a test board with a microstrip line, which is used to measure the near-field magnetic shielding effectiveness (NSE) of magnetic sheets made of metallic powder. To conduct the measurements, [...] Read more.

This study experimentally analyzed the impact of air gaps between a magnetic sheet and a test board with a microstrip line, which is used to measure the near-field magnetic shielding effectiveness (NSE) of magnetic sheets made of metallic powder. To conduct the measurements, a material fixture equipped with a microstrip line to generate the near magnetic field, a rectangular loop probe, and an automatic probe positioning system capable of moving the loop probe along three axes were designed and fabricated. In addition, to systematically vary the thickness of the gaps, three paper spacers with a thickness of 0.11 mm per paper were used, and a 1.0 mm thick acrylic sheet, along with a specially designed material fixture, was used to press down the magnetic sheets during measurement. The magnetic shielding properties were measured and compared under various air gap conditions using a near-field magnetic loop probe. The effect of the gaps on the shielding performance of the magnetic sheets was quantitatively evaluated for three different magnetic sheets. The experimental results showed that as the gap thickness increased, NSE tended to improve up to a frequency around 1 GHz, while in the higher frequency range of a few GHz, NSE tended to decrease. The physical background of this phenomenon was explained using an equivalent magnetic circuit represented by reluctances for the structure, where the magnetic sheet is placed above the microstrip line with an air gap. This model helps to elucidate how the presence of the air gap affects the near-field magnetic shielding performance. Full article

(This article belongs to the Special Issue Radiation Effects of Advanced Electronic Devices and Circuits, 2nd Edition)

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15 pages, 6981 KB

Open AccessEditor’s ChoiceArticle

Noncontact Monitoring of Respiration and Heartbeat Based on Two-Wave Model Using a Millimeter-Wave MIMO FM-CW Radar

by Mie Mie Ko and Toshifumi Moriyama

Electronics 2024, 13(21), 4308; https://doi.org/10.3390/electronics13214308 - 1 Nov 2024

Cited by 6 | Viewed by 4461

Abstract

This paper deals with the non-contact measurement of heartbeat and respiration using a millimeter-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FM-CW) radar. Monitoring heartbeat and respiration is useful for detecting cardiac diseases and understanding stress levels. Contact sensors are not suitable for these sorts of [...] Read more.

This paper deals with the non-contact measurement of heartbeat and respiration using a millimeter-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FM-CW) radar. Monitoring heartbeat and respiration is useful for detecting cardiac diseases and understanding stress levels. Contact sensors are not suitable for these sorts of long-term measurements due to the discomfort and skin irritation they cause. Therefore, the use of non-contact sensors, such as radars, is desirable. In this study, we obtained heartbeat and respiration information from phase data measured using a millimeter-wave MIMO FM-CW radar. We propose a two-wave model based on a Fourier series expansion and extract respiration and heartbeat information as a minimization problem. This model makes it possible to produce respiration and heartbeat waveforms. The produced heartbeat waveform can be used for estimating the interbeat interval (IBI). Experiments were conducted to confirm the usefulness of the proposed method. Moreover, the estimated results were compared with the contact sensor’s results. The results for both types of sensors were in good agreement. Full article

(This article belongs to the Special Issue Feature Papers in "Microwave and Wireless Communications" Section)

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26 pages, 1397 KB

Open AccessEditor’s ChoiceArticle

Inertial Measurement Unit Self-Calibration by Quantization-Aware and Memory-Parsimonious Neural Networks

by Matteo Cardoni, Danilo Pietro Pau, Kiarash Rezaei and Camilla Mura

Electronics 2024, 13(21), 4278; https://doi.org/10.3390/electronics13214278 - 31 Oct 2024

Cited by 5 | Viewed by 5896

Abstract

This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision [...] Read more.

This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision sensor, used as ground truth. At any re-calibration iteration, differences with respect to the ground truth are approximated by quantization-aware trained tiny neural networks, allowing calibration-loss compensations. Due to the unavailability of aging IMU-MEMS datasets, a synthetic dataset has been produced, taking into account aging effects with both linear and nonlinear calibration loss. Also, field-collected data in conditions of thermal stress have been used. A model relying on Dense and 1D Convolution layers was devised and compensated for an average of 1.97 g and a variance of 1.07 g², with only 903 represented with 16 bit parameters. The proposed model can be executed on an intelligent signal processing inertial sensor in 126.4 ms. This work represents a step forward toward in-sensor machine learning computing through integrating the computing capabilities into the sensor package that hosts the accelerometer and gyroscope sensing elements. Full article

(This article belongs to the Special Issue Edge Computing and Tiny Machine Learning in the Internet of Things: Latest Advances and Applications)

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14 pages, 2899 KB

Open AccessEditor’s ChoiceArticle

A 5 mW 28 nm CMOS Low-Noise Amplifier with Transformer-Based Electrostatic Discharge Protection for 60 GHz Applications

by Minoo Eghtesadi, Gianluca Giustolisi, Andrea Ballo, Salvatore Pennisi and Egidio Ragonese

Electronics 2024, 13(21), 4285; https://doi.org/10.3390/electronics13214285 - 31 Oct 2024

Cited by 4 | Viewed by 4265

Abstract

This paper presents a low-power 60 GHz low-noise amplifier (LNA) designed for Gbit/s applications using 28 nm CMOS technology. The LNA exploits a single-stage pseudo-differential architecture with integrated input transformer for both electrostatic discharge (ESD) protection and simultaneous noise/impedance matching. An effective power-constrained [...] Read more.

This paper presents a low-power 60 GHz low-noise amplifier (LNA) designed for Gbit/s applications using 28 nm CMOS technology. The LNA exploits a single-stage pseudo-differential architecture with integrated input transformer for both electrostatic discharge (ESD) protection and simultaneous noise/impedance matching. An effective power-constrained design strategy is adopted to pursue the lowest current consumption at the minimum noise figure (NF), with the best tradeoff between gain and frequency bandwidth. The LNA, which has been designed to drive an on–off keying (OOK) demodulator, is operated at a supply voltage as low as 0.9 V and achieves a voltage gain of about 21 dB with a 3 dB bandwidth of 2 GHz around 60 GHz. Thanks to the proper impedance transformation at the 60 GHz input, the amplifier exhibits an NF of 6.3 dB, also including the input transformer loss with a very low power consumption of about 5 mW. The adoption of a single-stage topology also allows an excellent input 1 dB compression point (IP_1dB) of −4.7 dBm. The input transformer guarantees up to 2 kV human body model (HBM) ESD protection. Full article

(This article belongs to the Section Circuit and Signal Processing)

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13 pages, 10862 KB

Open AccessEditor’s ChoiceArticle

Quantum Effects Induced by Defects in Thin-Film Structures: A Hybrid Modeling Approach to Conductance and Transmission Analysis

by Mariusz Mączka, Grzegorz Hałdaś, Stanisław Pawłowski and Ewa Korzeniewska

Electronics 2024, 13(21), 4230; https://doi.org/10.3390/electronics13214230 - 29 Oct 2024

Cited by 3 | Viewed by 1513

Abstract

This study investigated the possibility of quantum effects arising from defects resulting from the use of textronic electroconductive thin films and evaluated their impact on control characteristics. A hybrid model, where the classical approach to determine stationary fields based on the boundary element [...] Read more.

This study investigated the possibility of quantum effects arising from defects resulting from the use of textronic electroconductive thin films and evaluated their impact on control characteristics. A hybrid model, where the classical approach to determine stationary fields based on the boundary element method was combined with a quantum mechanical approach using nonequilibrium Green’s functions, was created. The results of conductance and transmission coefficient simulations for different types of defects in the studied structure and a wide range of temperatures assuming two different control modes are presented. Based on the results, the conditions for the occurrence of quantum effects on the surface of conducting paths containing defects were specified, and their impact on conductance in the quantum mechanical approach was estimated. Full article

(This article belongs to the Section Electronic Materials, Devices and Applications)

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34 pages, 9855 KB

Open AccessEditor’s ChoiceArticle

Cost-Effective Power Management for Smart Homes: Innovative Scheduling Techniques and Integrating Battery Optimization in 6G Networks

by Rana Riad Al-Taie and Xavier Hesselbach

Electronics 2024, 13(21), 4231; https://doi.org/10.3390/electronics13214231 - 29 Oct 2024

Cited by 6 | Viewed by 2614

Abstract

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal [...] Read more.

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system’s flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments. Full article

(This article belongs to the Special Issue Energy Storage, Analysis and Battery Usage)

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31 pages, 21641 KB

Open AccessEditor’s ChoiceArticle

A Methodology for the Design of a Compliant Electrocardiograph: A Case Study

by Luis Alberto Gordillo-Roblero, Jorge Alberto Soto-Cajiga, Carlos Romo-Fuentes, Luis Felipe Martínez-Soto and Noé Amir Rodríguez-Olivares

Electronics 2024, 13(21), 4238; https://doi.org/10.3390/electronics13214238 - 29 Oct 2024

Cited by 2 | Viewed by 3777

Abstract

This document presents the methodology for designing an electrocardiograph capable of acquiring IEC 60601-2-25-compliant signals. The objective of developing this methodology is to address a signal incompatibility problem that has existed in academia for years, specifically in physiological processing research. This problem is [...] Read more.

This document presents the methodology for designing an electrocardiograph capable of acquiring IEC 60601-2-25-compliant signals. The objective of developing this methodology is to address a signal incompatibility problem that has existed in academia for years, specifically in physiological processing research. This problem is related to the signal’s sampling rate and/or noise levels, and it becomes evident when one signal processing method is intended to work with another, either as a subsequent or simultaneous process. Even though matching algorithms can be implemented to remedy this incompatibility problem, the ultimate solution is the standardization of signals, which depends exclusively on the standardization of hardware. The signal incompatibility problem is urgent to solve because it makes the integration and scalability of different academic works difficult, preventing academia from reaching the stage of development that commercial equipment displays in automatic interpretation procedures. The design methodology presented in this document addresses the stated problem by creating an open-source hardware device capable of acquiring compliant signals, with careful consideration given to Signal Integrity and EMC concepts—a methodology that can be extended to other physiological acquisition systems. The expedited availability of the device’s design documentation and fabrication files is also an advantage of this work. Full article

(This article belongs to the Special Issue Electronic Devices for Bio-Medical Applications)

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15 pages, 1916 KB

Open AccessEditor’s ChoiceArticle

Charge Transport Characteristics in Doped Organic Semiconductors Using Hall Effect

by Seema Morab, Manickam Minakshi Sundaram and Almantas Pivrikas

Electronics 2024, 13(21), 4223; https://doi.org/10.3390/electronics13214223 - 28 Oct 2024

Cited by 6 | Viewed by 2185

Abstract

Numerical computations through the finite element method (FEM) are used to determine the impact of doping on carrier concentration and recombination between charges in time for organic semiconductor diodes having low mobility. The Hall effect is used to determine the effects of doping [...] Read more.

Numerical computations through the finite element method (FEM) are used to determine the impact of doping on carrier concentration and recombination between charges in time for organic semiconductor diodes having low mobility. The Hall effect is used to determine the effects of doping on the performance and reliability of organic semiconductor devices by accurately modeling these processes. In this work, the number density of charge carriers and Hall voltages are computed for n-type doped semiconductors with two different recombination processes, such as non-Langevin and Langevin-type. The findings reveal that in the Langevin system with

β^{'} = 1

, the number density of charge carriers is almost five and four times lower compared with the non-Langevin system with

β^{'} = 0.01

for increasing dopant concentrations of

N_{p_{d}}

= 1 and 3, respectively. The Langevin system also had lower Hall voltages than the steady-state and non-Langevin systems for different magnetic fields with dopants, and the non-Langevin system had nearly identical Hall voltages as the steady-state case. The outcome of the current work provides insights into charge transportation mechanisms in low-mobility doped organic semiconductors with Hall effect measurements to improve device efficiency. Full article

(This article belongs to the Section Semiconductor Devices)

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25 pages, 956 KB

Open AccessEditor’s ChoiceArticle

Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm Optimization

by Tsutomu Kumazawa, Munehiro Takimoto, Yasushi Kodama and Yasushi Kambayashi

Electronics 2024, 13(21), 4199; https://doi.org/10.3390/electronics13214199 - 25 Oct 2024

Viewed by 1651

Abstract

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is [...] Read more.

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality. Full article

(This article belongs to the Special Issue New Advances in Multi-agent Systems: Control and Modelling)

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13 pages, 721 KB

Open AccessEditor’s ChoiceArticle

Comparison of On-Sky Wavelength Calibration Methods for Integral Field Spectrograph

by Jie Song, Baichuan Ren, Yuyu Tang, Jun Wei and Xiaoxian Huang

Electronics 2024, 13(20), 4131; https://doi.org/10.3390/electronics13204131 - 21 Oct 2024

Cited by 1 | Viewed by 1801

Abstract

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star [...] Read more.

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was >99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level. Full article

(This article belongs to the Section Circuit and Signal Processing)

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16 pages, 8003 KB

Open AccessEditor’s ChoiceArticle

AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality

by Chaelin Seok, Yeongje Park, Junho Baek, Hyeji Lim, Jong-hyuk Roh, Youngsam Kim, Soohyung Kim and Eui Chul Lee

Electronics 2024, 13(20), 4112; https://doi.org/10.3390/electronics13204112 - 18 Oct 2024

Cited by 5 | Viewed by 2169

Abstract

This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This [...] Read more.

This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies. Full article

(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)

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18 pages, 7546 KB

Open AccessEditor’s ChoiceArticle

Measurements of Geometrical Quantities and Selection of Parameters in the Robotic Grinding Process of an Aircraft Engine

by Krzysztof Kurc, Andrzej Burghardt, Magdalena Muszyńska, Paulina Pietruś and Dariusz Szybicki

Electronics 2024, 13(20), 4077; https://doi.org/10.3390/electronics13204077 - 17 Oct 2024

Cited by 3 | Viewed by 4398

Abstract

Aircraft engine blades are produced through various techniques, one of which is precise electrochemical machining (ECM), commonly applied in the aerospace, automotive, and electromechanical industries. This method achieves machining accuracy ranging from 0.1 to 0.3 mm. However, components with complex shapes still require [...] Read more.

Aircraft engine blades are produced through various techniques, one of which is precise electrochemical machining (ECM), commonly applied in the aerospace, automotive, and electromechanical industries. This method achieves machining accuracy ranging from 0.1 to 0.3 mm. However, components with complex shapes still require grinding and polishing. During the grinding of aircraft blades, achieving high precision and maintaining strict dimensional control are essential. This involves monitoring the thickness of the blade at key cross-sections, as well as the radii of the leading and trailing edges, chord lengths, twist angles, and more. The paper introduces a developed robotic blade grinding process featuring iterative laser measurement of geometric parameters. A custom measuring device with laser heads was designed, calibrated, and tested for repeatability. The measurement data were then used to determine the blade feed rate and machining path via a fuzzy logic decision system. The proposed method was validated on a series of PT6 aircraft engine blades in collaboration with Pratt and Whitney Rzeszów. Full article

(This article belongs to the Special Issue Automation, Operation and Maintenance of Control and Communication Systems, 2nd Edition)

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29 pages, 11145 KB

Open AccessEditor’s ChoiceArticle

Total Power Factor Smart Contract with Cyber Grid Guard Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Wind Farm

by Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink and Aaron Werth

Electronics 2024, 13(20), 4055; https://doi.org/10.3390/electronics13204055 - 15 Oct 2024

Cited by 6 | Viewed by 3042

Abstract

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially [...] Read more.

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs. Full article

(This article belongs to the Special Issue Control and Optimization Technologies in Renewable Energy and Integrated Energy Systems)

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30 pages, 18530 KB

Open AccessEditor’s ChoiceArticle

Dimensionality Reduction for the Real-Time Light-Field View Synthesis of Kernel-Based Models

by Martijn Courteaux, Hannes Mareen, Bert Ramlot, Peter Lambert and Glenn Van Wallendael

Electronics 2024, 13(20), 4062; https://doi.org/10.3390/electronics13204062 - 15 Oct 2024

Cited by 3 | Viewed by 6826

Abstract

Several frameworks have been proposed for delivering interactive, panoramic, camera-captured, six-degrees-of-freedom video content. However, it remains unclear which framework will meet all requirements the best. In this work, we focus on a Steered Mixture of Experts (SMoE) for 4D planar light fields, which [...] Read more.

Several frameworks have been proposed for delivering interactive, panoramic, camera-captured, six-degrees-of-freedom video content. However, it remains unclear which framework will meet all requirements the best. In this work, we focus on a Steered Mixture of Experts (SMoE) for 4D planar light fields, which is a kernel-based representation. For SMoE to be viable in interactive light-field experiences, real-time view synthesis is crucial yet unsolved. This paper presents two key contributions: a mathematical derivation of a view-specific, intrinsically 2D model from the original 4D light field model and a GPU graphics pipeline that synthesizes these viewpoints in real time. Configuring the proposed GPU implementation for high accuracy, a frequency of 180 to 290 Hz at a resolution of

2048 \times 2048

pixels on an NVIDIA RTX 2080Ti is achieved. Compared to NVIDIA’s instant-ngp Neural Radiance Fields (NeRFs) with the default configuration, our light field rendering technique is 42 to 597 times faster. Additionally, allowing near-imperceptible artifacts in the reconstruction process can further increase speed by 40%. A first-order Taylor approximation causes imperfect views with peak signal-to-noise ratio (PSNR) scores between 45 dB and 63 dB compared to the reference implementation. In conclusion, we present an efficient algorithm for synthesizing 2D views at arbitrary viewpoints from 4D planar light-field SMoE models, enabling real-time, interactive, and high-quality light-field rendering within the SMoE framework. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)

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18 pages, 10508 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Magnetic Railway Sleeper Detector

by Lukas Heindler, Harald Hüttmayr, Thomas Thurner and Bernhard Zagar

Electronics 2024, 13(20), 4005; https://doi.org/10.3390/electronics13204005 - 11 Oct 2024

Cited by 3 | Viewed by 2130

Abstract

In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and [...] Read more.

In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and generate a safe substructure. Even today, maintenance personnel still have to manually locate the sleepers if they cannot be detected by computer vision systems or visually by the operator. Here we developed a first of its kind magnetic sleeper detector, even able to find sleepers, buried in gravel, undetectable by vision based systems. Our approach of magnetic detection is based on a DC magnetic field excitation and a detector moving with respect to the rail system, including the sleepers and fasteners for mounting the rails. Due to railway application constraints a large air gap between the sensor and the sleeper structure is required, which significantly complicates the magnetic sensing task for robust sleeper detection. The design and optimization of the magnetic circuit was based on extensive 3D simulation studies to ensure highest possible variation in magnetic flux density at the sensor locations for absence and presence of a sleeper. Furthermore, a low noise and high sensitivity electronic circuit has been realized to cope with sensor signal offsets from unknown or changing sensor orientations with respect to the earth’s magnetic field, or magnetic interferences from other trains potentially passing by during active measurements. Since we only want to detect sleepers in close vicinity of the moving sensor system, digital signal processing of the acquired signals can easily compensate for disturbing slowly changing or static field components within real world application scenarios. We demonstrate that magnetic detection of even buried sleepers on railway tracks is possible for distances of up to 172 mm between the sensor and the sleeper. This enables an even higher level of railway maintenance automation previously impossible in certain scenarios. Full article

(This article belongs to the Special Issue Recent Advances and Applications in New Detectors)

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23 pages, 9813 KB

Open AccessEditor’s ChoiceReview

Overview of Reconfigurable Antenna Systems for IoT Devices

by Elena García, Aurora Andújar and Jaume Anguera

Electronics 2024, 13(20), 3988; https://doi.org/10.3390/electronics13203988 - 10 Oct 2024

Cited by 18 | Viewed by 7916

Abstract

The proliferation of Internet of Things (IoT) devices, such as trackers and sensors, necessitates a delicate balance between device miniaturization and performance. This extends to the antenna system, which must be both efficient and multiband operational while fitting within space-constrained electronic enclosures. Traditional [...] Read more.

The proliferation of Internet of Things (IoT) devices, such as trackers and sensors, necessitates a delicate balance between device miniaturization and performance. This extends to the antenna system, which must be both efficient and multiband operational while fitting within space-constrained electronic enclosures. Traditional antennas, however, struggle to meet these miniaturization demands. Reconfigurable antennas have emerged as a promising solution for adapting their frequency, radiation pattern, or polarization in response to changing requirements, making them ideal for IoT applications. Among various reconfiguration techniques (electrical, mechanical, optical, and material-based), electrical reconfiguration reigns supreme for IoT applications. Its suitability for compact devices, cost-effectiveness, and relative simplicity make it the preferred choice. This paper reviews various approaches to realizing IoT reconfigurable antennas, with a focus on electrical reconfiguration techniques. It categorizes these techniques based on their implementation, including PIN diodes, digital tunable capacitors (DTCs), varactor diodes, and RF switches. It also explores the challenges associated with the development and characterization of IoT reconfigurable antennas, evaluates the strengths and limitations of existing methods, and identifies open challenges for future research. Importantly, the growing trend towards smaller IoT devices has led to the development of antenna boosters. These components, combined with advanced reconfiguration techniques, offer new opportunities for enhancing antenna performance while maintaining a compact footprint. Full article

(This article belongs to the Special Issue Smart Antennas and Systems for 5G and Beyond: Latest Advances and Prospects)

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17 pages, 3242 KB

Open AccessEditor’s ChoiceArticle

Analysis of Handwriting for Recognition of Parkinson’s Disease: Current State and New Study

by Kamila Białek, Anna Potulska-Chromik, Jacek Jakubowski, Monika Nojszewska and Anna Kostera-Pruszczyk

Electronics 2024, 13(19), 3962; https://doi.org/10.3390/electronics13193962 - 9 Oct 2024

Cited by 18 | Viewed by 6614

Abstract

One of the symptoms of Parkinson’s disease (PD) is abnormal handwriting caused by motor dysfunction. The development of tablet technology opens up opportunities for an effective analysis of the writing process of people suffering from Parkinson’s disease, aimed at supporting medical diagnosis using [...] Read more.

One of the symptoms of Parkinson’s disease (PD) is abnormal handwriting caused by motor dysfunction. The development of tablet technology opens up opportunities for an effective analysis of the writing process of people suffering from Parkinson’s disease, aimed at supporting medical diagnosis using machine learning methods. Several approaches have been used and presented in the literature that discuss the analysis and understanding of images created during the writing of single words or sentences. In this study, we propose an analysis based on a sequence of sentences, which allows us to assess the evolution of writing over time. The study material consisted of handwriting image samples acquired in a group of 24 patients with PD and 24 healthy controls. The parameterization of the handwriting image samples was carried out using domain knowledge. Using the exhaustive search method, we selected the relevant features for the SVM algorithm performing binary classification. The results obtained were assessed using quality measures, including overall accuracy, which was 91.67%. The results were compared with competitive works on the same subject and seem to be better (a higher level of accuracy with a much smaller number of features than those presented by others). Full article

(This article belongs to the Collection Image and Video Analysis and Understanding)

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12 pages, 5661 KB

Open AccessEditor’s ChoiceArticle

An Adaptive Sliding Mode Control Using a Novel Adaptive Law Based on Quasi-Convex Functions and Average Sliding Variables for Robot Manipulators

by Dong Hee Seo, Jin Woong Lee, Hyuk Mo An and Seok Young Lee

Electronics 2024, 13(19), 3940; https://doi.org/10.3390/electronics13193940 - 5 Oct 2024

Cited by 2 | Viewed by 2630

Abstract

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation [...] Read more.

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance. Full article

(This article belongs to the Special Issue Intelligence Control and Applications of Intelligence Robotics)

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29 pages, 4571 KB

Open AccessEditor’s ChoiceArticle

Natural Language Inference with Transformer Ensembles and Explainability Techniques

by Isidoros Perikos and Spyro Souli

Electronics 2024, 13(19), 3876; https://doi.org/10.3390/electronics13193876 - 30 Sep 2024

Cited by 3 | Viewed by 5985

Abstract

Natural language inference (NLI) is a fundamental and quite challenging task in natural language processing, requiring efficient methods that are able to determine whether given hypotheses derive from given premises. In this paper, we apply explainability techniques to natural-language-inference methods as a means [...] Read more.

Natural language inference (NLI) is a fundamental and quite challenging task in natural language processing, requiring efficient methods that are able to determine whether given hypotheses derive from given premises. In this paper, we apply explainability techniques to natural-language-inference methods as a means to illustrate the decision-making procedure of its methods. First, we investigate the performance and generalization capabilities of several transformer-based models, including BERT, ALBERT, RoBERTa, and DeBERTa, across widely used datasets like SNLI, GLUE Benchmark, and ANLI. Then, we employ stacking-ensemble techniques to leverage the strengths of multiple models and improve inference performance. Experimental results demonstrate significant improvements of the ensemble models in inference tasks, highlighting the effectiveness of stacking. Specifically, our best-performing ensemble models surpassed the best-performing individual transformer by 5.31% in accuracy on MNLI-m and MNLI-mm tasks. After that, we implement LIME and SHAP explainability techniques to shed light on the decision-making of the transformer models, indicating how specific words and contextual information are utilized in the transformer inferences procedures. The results indicate that the model properly leverages contextual information and individual words to make decisions but, in some cases, find difficulties in inference scenarios with metaphorical connections which require deeper inferential reasoning. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence Engineering)

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13 pages, 10496 KB

Open AccessEditor’s ChoiceArticle

A Method for Fingerprint Edge Enhancement Based on Radial Hilbert Transform

by Baiyang Wu, Shuo Zhang, Weinan Gao, Yong Bi and Xiaosong Hu

Electronics 2024, 13(19), 3886; https://doi.org/10.3390/electronics13193886 - 30 Sep 2024

Cited by 4 | Viewed by 2921

Abstract

Fingerprints play a significant role in various fields due to their uniqueness. In order to effectively utilize fingerprint information, it is necessary to enhance image quality. This paper introduces a method based on Radial Hilbert transform (RHLT), which simulates the vortex filter using [...] Read more.

Fingerprints play a significant role in various fields due to their uniqueness. In order to effectively utilize fingerprint information, it is necessary to enhance image quality. This paper introduces a method based on Radial Hilbert transform (RHLT), which simulates the vortex filter using the point spread function (PSF) of spiral phase plate (SPP) with a topological charge

l = 1

, for fingerprint edge enhancement. The experimental results show that the processed fingerprint image has more distinct edges, with an increase in information entropy and average gradient. Unlike classical edge detection operators, the fingerprint edge image obtained by the RHLT method exhibits a lower mean square error (

M S E

) and a higher peak signal-to-noise ratio (

P S N R

). This indicates that the RHLT method provides more accurate edge detection and demonstrates higher noise-resistance capabilities. Due to its ability to highlight edge information while preserving more original features, this method has great application potential in fingerprint image processing. Full article

(This article belongs to the Section Bioelectronics)

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15 pages, 6740 KB

Open AccessEditor’s ChoiceArticle

Modulation Format Recognition Scheme Based on Discriminant Network in Coherent Optical Communication System

by Fangxu Yang, Qinghua Tian, Xiangjun Xin, Yiqun Pan, Fu Wang, José Antonio Lázaro, Josep M. Fàbrega, Sitong Zhou, Yongjun Wang and Qi Zhang

Electronics 2024, 13(19), 3833; https://doi.org/10.3390/electronics13193833 - 28 Sep 2024

Cited by 1 | Viewed by 4185

Abstract

In this paper, we skillfully utilize the discriminative ability of the discriminator to construct a conditional generative adversarial network, and propose a scheme that uses few symbols to achieve high accuracy recognition of modulation formats under low signal-to-noise ratio conditions in coherent optical [...] Read more.

In this paper, we skillfully utilize the discriminative ability of the discriminator to construct a conditional generative adversarial network, and propose a scheme that uses few symbols to achieve high accuracy recognition of modulation formats under low signal-to-noise ratio conditions in coherent optical communication. In the one thousand kilometres G.654E optical fiber transmission system, transmission experiments are conducted on the PDM-QPSK/-8PSK/-16QAM/-32QAM/-64QAM modulation format at 8G/16G/32G baud rates, and the signal-to-noise ratio parameters are traversed under experimental conditions. As a key technology in the next-generation elastic optical networks, the modulation format recognition scheme proposed in this paper achieves 100% recognition of the above five modulation formats without distinguishing signal transmission rates. The optical signal-to-noise ratio thresholds required to achieve 100% recognition accuracy are 12.4 dB, 14.3 dB, 15.4 dB, 16.2 dB, and 17.3 dB, respectively. Full article

(This article belongs to the Special Issue Advances in Optical Communication and Optical Computing)

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12 pages, 34840 KB

Open AccessEditor’s ChoiceArticle

Miniaturized Multiband Substrate-Integrated Waveguide Bandpass Filters with Multi-Layer Configuration and High In-Band Isolation

by Yu Zhan, Yi Wu, Kaixue Ma and Kiat Seng Yeo

Electronics 2024, 13(19), 3834; https://doi.org/10.3390/electronics13193834 - 28 Sep 2024

Cited by 5 | Viewed by 3751

Abstract

This article presents a multiband bandpass filter structure with an in-line topology based on substrate-integrated waveguide (SIW) technology. A multi-layer configuration is employed to achieve circuit miniaturization. By constructing the coupling matrix, the coupling relationships among all resonators are quantitatively characterized, enabling the [...] Read more.

This article presents a multiband bandpass filter structure with an in-line topology based on substrate-integrated waveguide (SIW) technology. A multi-layer configuration is employed to achieve circuit miniaturization. By constructing the coupling matrix, the coupling relationships among all resonators are quantitatively characterized, enabling the extraction of the theoretical frequency response and guiding circuit modeling and optimization. We designed and fabricated a third-order tri-band SIW filter and a third-order quad-band SIW filter, achieving a return loss of nearly 20 dB across all passbands. The close agreement between simulated and measured results validates the proposed design model. Additionally, the high in-band isolation of over 40 dB is demonstrated between all adjacent bands, highlighting the potential applicability of this technology in multiband scenarios. Full article

(This article belongs to the Special Issue RF/MM-Wave/THz Integrated Circuit Design for 5G/6G, Artificial Intelligence, Internet-of-Things, and Future Computing Applications)

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11 pages, 416 KB

Open AccessEditor’s ChoiceArticle

Energy Efficiency Evaluation of Artificial Intelligence Algorithms

by Kalin Penev, Alexander Gegov, Olufemi Isiaq and Raheleh Jafari

Electronics 2024, 13(19), 3836; https://doi.org/10.3390/electronics13193836 - 28 Sep 2024

Cited by 9 | Viewed by 5387

Abstract

This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann’s limit on efforts to [...] Read more.

This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann’s limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies. Full article

(This article belongs to the Special Issue Green Artificial Intelligence: Theory and Applications)

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16 pages, 3278 KB

Open AccessEditor’s ChoiceArticle

Real-Time Wild Horse Crossing Event Detection Using Roadside LiDAR

by Ziru Wang, Hao Xu, Fei Guan and Zhihui Chen

Electronics 2024, 13(19), 3796; https://doi.org/10.3390/electronics13193796 - 25 Sep 2024

Cited by 1 | Viewed by 2581

Abstract

Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging [...] Read more.

Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, object tracking, and object classification. Considering that the background information collected by LiDAR may change over time, an automatic background filtering method that updates the background in real-time has been developed to subtract the background effectively over time. After a standard object clustering and a fast object tracking method, eight features were extracted from the clustering group, including a feature developed to specifically identify wild horses, and a vertical point distribution was used to describe the objects. The classification results of the four classifiers were compared, and the experiments showed that the support vector machine (SVM) had more reliable results. The field test results showed that the developed method could accurately detect a wild horse within the detection range of LiDAR. The wild horse crossing information can warn drivers about the risks of wild horse–vehicle collisions in real-time. Full article

(This article belongs to the Special Issue Advances, Applications, and Challenges of Deep Learning-Aided Computer Vision and Pattern Recognition in Intelligent Transportation)

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23 pages, 3964 KB

Open AccessEditor’s ChoiceArticle

Geometry of Textual Data Augmentation: Insights from Large Language Models

by Sherry J. H. Feng, Edmund M-K. Lai and Weihua Li

Electronics 2024, 13(18), 3781; https://doi.org/10.3390/electronics13183781 - 23 Sep 2024

Cited by 4 | Viewed by 5629

Abstract

Data augmentation is crucial for enhancing the performance of text classification models when labelled training data are scarce. For natural language processing (NLP) tasks, large language models (LLMs) are able to generate high-quality augmented data. But a fundamental understanding of the reasons for [...] Read more.

Data augmentation is crucial for enhancing the performance of text classification models when labelled training data are scarce. For natural language processing (NLP) tasks, large language models (LLMs) are able to generate high-quality augmented data. But a fundamental understanding of the reasons for their effectiveness remains limited. This paper presents a geometric and topological perspective on textual data augmentation using LLMs. We compare the augmentation data generated by GPT-J with those generated through cosine similarity from Word2Vec and GloVe embeddings. Topological data analysis reveals that GPT-J generated data maintains label coherence. Convex hull analysis of such data represented by their two principal components shows that they lie within the spatial boundaries of the original training data. Delaunay triangulation reveals that increasing the number of augmented data points that are connected within these boundaries correlates with improved classification accuracy. These findings provide insights into the superior performance of LLMs in data augmentation. A framework for predicting the usefulness of augmentation data based on geometric properties could be formed based on these techniques. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Natural Language Processing)

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24 pages, 6207 KB

Open AccessEditor’s ChoiceArticle

Dynamic Partitioning of Graphs Based on Multivariate Blood Glucose Data—A Graph Neural Network Model for Diabetes Prediction

by Jianjun Li, Xiaozhe Jiang and Kaiyue Wang

Electronics 2024, 13(18), 3727; https://doi.org/10.3390/electronics13183727 - 20 Sep 2024

Cited by 5 | Viewed by 4115

Abstract

Postprandial Hyperglycemia (PPHG) persistently threatens patients’ health. Therefore, accurate diabetes prediction is crucial for effective blood glucose management. Most current methods primarily focus on analyzing univariate blood glucose data using traditional neural networks, neglecting the importance of spatiotemporal modeling of multivariate data at [...] Read more.

Postprandial Hyperglycemia (PPHG) persistently threatens patients’ health. Therefore, accurate diabetes prediction is crucial for effective blood glucose management. Most current methods primarily focus on analyzing univariate blood glucose data using traditional neural networks, neglecting the importance of spatiotemporal modeling of multivariate data at the node and subgraph levels. This study aimed to evaluate the accuracy of using deep learning (DL) techniques to predict diabetes based on multivariable blood glucose data, aiming to improve resource allocation and decision-making in healthcare. We introduce a Nonlinear Aggregated Graph Neural Network (NLAGNN) that utilizes continuous multivariate historical blood glucose data from multiple patients to predict blood glucose levels over time, addressing the challenge of accurately extracting strong and weak correlation features. We preliminarily propose a Nonlinear Fourier Graph Neural Operator (NFGO) for nonlinear node representation, which effectively reduces meaningless noise. Additionally, a dynamic partitioning of graphs is introduced, which divides the a hypergraph into distinct subgraphs, enabling the further processing of strongly correlated features at the node and subgraph levels, ultimately obtaining the final prediction through layer aggregation. Extensive experiments on three datasets show that our proposed method achieves competitive results compared to existing advanced methods. Full article

(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)

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18 pages, 5504 KB

Open AccessEditor’s ChoiceArticle

Fatigue Driving State Detection Based on Spatial Characteristics of EEG Signals

by Wenwen Chang, Wenchao Nie, Renjie Lv, Lei Zheng, Jialei Lu and Guanghui Yan

Electronics 2024, 13(18), 3742; https://doi.org/10.3390/electronics13183742 - 20 Sep 2024

Cited by 8 | Viewed by 8316

Abstract

Monitoring the driver’s physical and mental state based on wearable EEG acquisition equipment, especially the detection and early warning of fatigue, is a key issue in the research of the brain–computer interface in human–machine intelligent fusion driving. Comparing and analyzing the waking (alert) [...] Read more.

Monitoring the driver’s physical and mental state based on wearable EEG acquisition equipment, especially the detection and early warning of fatigue, is a key issue in the research of the brain–computer interface in human–machine intelligent fusion driving. Comparing and analyzing the waking (alert) state and fatigue state by simulating EEG data during simulated driving, this paper proposes a brain functional network construction method based on a phase locking value (PLV) and phase lag index (PLI), studies the relationship between brain regions, and quantitatively analyzes the network structure. The characteristic parameters of the brain functional network that have significant differences in fatigue status are screened out and constitute feature vectors, which are then combined with machine learning algorithms to complete classification and identification. The experimental results show that this method can effectively distinguish between alertness and fatigue states. The recognition accuracy rates of 52 subjects are all above 70%, with the highest recognition accuracy reaching 89.5%. Brain network topology analysis showed that the connectivity between brain regions was weakened under a fatigue state, especially under the PLV method, and the phase synchronization relationship between delta and theta frequency bands was significantly weakened. The research results provide a reference for understanding the interdependence of brain regions under fatigue conditions and the development of fatigue driving detection systems. Full article

(This article belongs to the Section Bioelectronics)

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21 pages, 15716 KB

Open AccessEditor’s ChoiceArticle

A Novel Wind Power Prediction Model That Considers Multi-Scale Variable Relationships and Temporal Dependencies

by Zhanyang Xu, Hong Zhao, Chengxi Xu, Hongyan Shi, Jian Xu and Zhe Wang

Electronics 2024, 13(18), 3710; https://doi.org/10.3390/electronics13183710 - 19 Sep 2024

Cited by 8 | Viewed by 3978

Abstract

Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. [...] Read more.

Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. In this paper, a multi-scale frequency decomposition module is designed to split the raw data into high-frequency and low-frequency parts. Subsequently, features are extracted from the high-frequency information using a multi-scale temporal graph neural network combined with an adaptive graph learning module and from the low-frequency data using an improved bidirectional temporal network. Finally, the features are integrated through a cross-attention mechanism. To validate the effectiveness of the proposed model, extensive comprehensive experiments were conducted using a wind power dataset provided by the State Grid. The experimental results indicate that the MSE of the model proposed in this paper has decreased by an average of 7.1% compared to the state-of-the-art model and by 48.9% compared to the conventional model. Moreover, the improvement in model performance becomes more pronounced as the prediction horizon increases. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems)

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10 pages, 3467 KB

Open AccessEditor’s ChoiceArticle

Comprehensive Data Augmentation Approach Using WGAN-GP and UMAP for Enhancing Alzheimer’s Disease Diagnosis

by Emi Yuda, Tomoki Ando, Itaru Kaneko, Yutaka Yoshida and Daisuke Hirahara

Electronics 2024, 13(18), 3671; https://doi.org/10.3390/electronics13183671 - 16 Sep 2024

Cited by 7 | Viewed by 3949

Abstract

In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original [...] Read more.

In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original dataset, the augmented dataset and the combined data were mapped using Uniform Manifold Approximation and Projection (UMAP) in both a 2D and 3D space. The same combined interaction network analysis was then performed on the test data. The results showed that, for the test accuracy, the score was 30.46% for the original dataset (unbalanced), whereas for the WGAN-GP augmented dataset (balanced), it improved to 56.84%, indicating that the WGAN-GP augmentation can effectively address the unbalanced problem. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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12 pages, 3787 KB

Open AccessEditor’s ChoiceArticle

Functional Exercise Induces Adaptations in Muscle Oxygen Saturation in Division One Collegiate Butterfly Swimmers: A Randomized Controlled Trial

by Jack Grotke, Austin Alcantara, Joe Amitrano and Dhruv R. Seshadri

Electronics 2024, 13(18), 3680; https://doi.org/10.3390/electronics13183680 - 16 Sep 2024

Cited by 2 | Viewed by 4103

Abstract

This study investigates the impact of a five-week functional exercise intervention designed to enhance the muscular endurance of the posterior shoulder musculature, aiming to mitigate shoulder fatigue and overuse injury. Twelve Division I collegiate butterfly swimmers were recruited and evenly randomized into exercise [...] Read more.

This study investigates the impact of a five-week functional exercise intervention designed to enhance the muscular endurance of the posterior shoulder musculature, aiming to mitigate shoulder fatigue and overuse injury. Twelve Division I collegiate butterfly swimmers were recruited and evenly randomized into exercise (EX) and control (CTRL) groups. Weekly 100-yard butterfly sprints were performed, with Muscle Oxygen Saturation (SmO₂) continuously monitored using a wearable near-infrared spectroscopy (NIRS) device. This study is among the first to utilize wearable NIRS devices to monitor SmO₂ underwater during swimming, demonstrating that a targeted 5-week exercise program significantly improves posterior shoulder endurance, as evidenced by increased Posterior Shoulder Endurance Test (PSET) scores and distinctive SmO₂ adaptations in the EX-group compared to the CTRL group. These findings suggest that targeted dryland exercises can enhance posterior shoulder endurance with long-term implications for potentially reducing injury risk and improving performance. Full article

(This article belongs to the Special Issue New Application of Wearable Electronics)

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