Electronics, Volume 14, Issue 9

In this study, we investigate the relationship between breathing patterns and cardiac autonomic nervous activity during spontaneous breathing. Electrocardiograms and respiratory signals were simultaneously monitored in six subjects for 5 min while in...

To shorten the development cycle of integrated circuit (IC) chips, third-party IP cores (3PIPs) are widely used in the design phase; however, these 3PIPs may be untrusted, creating potential vulnerabilities. Attackers may insert hardware Trojans (HTs...

Serial communication enables communication between devices by providing high speed and efficiency in data transfer within modern communication systems. It has a wide range of applications, including business, healthcare, education, industry, and cons...

In order to achieve precise vector control of permanent magnet synchronous motors and maintain reliability during operation, it is necessary to obtain more accurate rotor position and rotor angular velocity. However, the installation of sensors can l...

Pediatric pneumonia remains a critical global health challenge requiring accurate and interpretable diagnostic solutions. Although deep learning has shown potential for pneumonia recognition on chest X-ray images, gaps persist in understanding model...

A circularly polarized (CP) textile antenna is investigated for concurrent on- and off-body wireless communications in the 2.38 GHz medical body area network and 5.8 GHz industrial, scientific, and medical bands in the wireless body area network. The...

Extracting robust features against geometric transformations and adversarial perturbations remains a critical challenge in deep learning. Although capsule networks exhibit resilience through vector-encapsulated features and dynamic routing, they suff...

Traditional sparse code multiple access (SCMA) systems, which transmit user codewords through fixed subcarrier allocations, exhibit vulnerability to external jamming and interference. To address this challenge, we propose a novel SCMA codebook design...

In highway scenarios, factors such as high-speed vehicle movement, lighting conditions, and positional changes significantly affect the quality of trajectories in multi-object tracking. This, in turn, impacts the trajectory clustering process within...

This paper proposes a method for recommending parent–child travel destinations and planning travel routes tailored to children of different ages. The method inputs basic information about the attractions (such as ticket prices, geographical loc...

Millimeter-wave signals experience substantial path loss when penetrating common building materials, hindering seamless indoor coverage from outdoor networks. To address this limitation, we present the 28-GHz “Meta-Window”, a mass-produci...

FPGA-RICH is an FPGA-based online partial particle identification system for the NA62 experiment employing AI techniques. Integrated between the readout of the Ring Imaging Cherenkov detector (RICH) and the low-level trigger processor (L0TP+), FPGA-R...

With the growing integration of the Internet of Things (IoT), low-altitude intelligent networks, and vehicular networks, smart city traffic systems are gradually evolving into an air–ground integrated intelligent monitoring framework. However,...

Accurate water body detection is essential for autonomous navigation and operational planning of unmanned surface vehicles (USVs). To address model adaptability to ambiguous boundaries caused by diverse scenarios and climatic conditions, this study p...

In response to the decreased accuracy in person detection caused by densely populated areas and mutual occlusions in public spaces, a human head-detection approach is employed to assist in detecting individuals. To address key issues in dense scenes&...

This paper presents a novel hierarchical voltage control framework for distribution networks to mitigate voltage violations by coordinating distributed energy storage systems (DESSs). The framework establishes a two-layer architecture that integrates...

This study presents a zero-shot object detection framework for corner casting detection in shipping container operations, leveraging edge computing for intelligent robotic perception and control. The proposed system integrates Grounding DINO on a Ras...

Video emotion recognition faces significant challenges due to the strong spatiotemporal coupling of dynamic expressions and the substantial variations in cross-scale motion patterns (e.g., subtle facial micro-expressions versus large-scale body gestu...

This paper addresses critical challenges in renewable energy research, particularly under the difficult operational conditions caused by the military conflict in Ukraine. Despite significant infrastructure loss due to the armed conflict (13% of solar...

Multi-signature applications allow multiple signers to collaboratively generate a single signature on the same message, which is widely applied in blockchain to reduce the percentage of signatures in blocks and improve the throughput of transactions....

Museum use of artificial intelligence (AI) is becoming increasingly common, but its contribution to museum attendance is yet to be confirmed. This paper investigates whether the adoption of AI impacts museum visitation using data from 19 museums. Sta...

Large amounts of high-quality data are required to train artificial intelligence (AI) models; however, curating such data through human intervention remains cumbersome, time-consuming, and error-prone. In particular, erroneous annotations and statist...

Out-of-distribution (OOD) detection is crucial for identifying samples that deviate from the training distribution, thereby enhancing the reliability of deep neural network models. However, existing OOD detection methods primarily address semantic sh...

Amid rapid global aviation development and increasingly stringent safety standards, aerostats demonstrate vast potential in environmental monitoring, communication relay, cargo transportation, and other applications. However, their operational safety...

The Multiple Random Empirical Kernel Learning Machine (MREKLM) typically generates multiple empirical feature spaces by selecting a limited group of samples, which helps reduce training duration. However, MREKLM does not incorporate data distribution...

Pneumonia is one of the leading causes of morbidity and mortality in children, making its early detection critical for effective treatment. The objective of this study is to develop and evaluate a hybrid deep learning framework that combines convolut...

Image forgery detection, as an essential technique for analyzing image credibility, has experienced significant advancements recently. However, the forgery detection performance remains unsatisfactory in terms of meeting practical requirements. This...

Fall prevention has always been a crucial topic for injury prevention. Research shows that real-time posture monitoring and subsequent fall prevention are important for the prevention of fall-related injuries. In this research, we determine a real-ti...

Financial fraud detection is an important field in financial technology, and strong and effective machine learning (ML) models are needed to detect fraudulent transactions with high accuracy and reliability. Conventional fraud detection models, like...

In the previous work, we proposed LWGSE-YOLOv4-tiny and LWDSG-ResNet18, leveraging depthwise separable and Ghost Convolutions for fast self-driving control while achieving a detection speed of 24.9 FPS. However, the system fell short of Level 4 auton...

NAND flash memory has been widely adopted as the primary data storage medium in data centers. However, the inherent characteristic of out-of-place updates in NAND flash necessitates garbage collection (GC) operations on NAND flash-based solid-state d...

In distribution networks with high penetration of distributed generators (DGs), traditional fault reconfiguration strategies often fail to achieve maximum load recovery and encounter operational stability challenges. This paper proposes a novel two-s...

Short-term power load forecasting is crucial for safe grid operation. To address the insufficiency of traditional decomposition methods in suppressing high-frequency noise within multi-source noisy time series, this study proposes a hybrid forecastin...

Accurate indoor localization is essential for Internet of Things (IoT) systems and autonomous navigation in the 6G communication system. However, achieving precision in environments affected by signal multipath effects and interference remains a chal...

A cyber–physical multi-robot system integrates robotic agents that share data over communication networks in real time to achieve common objectives by making decisions collectively based on the knowledge of their surroundings. This work introdu...

In human–robot collaborative environments, predicting and explaining robotic grasp failures is crucial for effective operation. While machine learning models can predict failures accurately, they often lack transparency, limiting their utility...

Retinitis Pigmentosa (RP) is a progressive retinal disorder that leads to vision loss and blindness. Accurate staging of RP is crucial for effective treatment planning and disease management. This study aims to develop an objective and reliable metho...

In order to improve the ability to suppress unbalanced voltage in bipolar DC microgrids, a comprehensive power regulation control of a novel shared energy storage system is proposed for a multi-scenario bipolar DC microgrid. The novel shared energy s...

Additive manufacturing is currently regarded as one of the enabling technologies for Space Economy since it allows for the reduction of lead time and costs of payloads and platforms. Typically, metal-based additive manufacturing technologies are cons...

Reducing mutual coupling between radiation elements of compact MIMO vehicle antennas is of fundamental importance to achieve simultaneous high capacity and miniaturization. In this work, we propose a commercial vehicle MIMO antenna composed of two in...

Brain tumor prediction from magnetic resonance images is an important problem, but it is difficult due to the complexity of brain structure and variability in tumor appearance. There have been various ML and DL-based approaches, but the limitations o...

Battery energy storage systems are essential for grid stability and the efficient integration of renewable energy sources. Their performance is influenced by the efficiency of bidirectional converters, particularly under varying load conditions. This...

The inconsistency of individual lithium-ion batteries causes the voltage imbalance of the batteries. An effective voltage-balancing circuit is essential to improve the inconsistency of series-connected batteries. This paper presents an isolated reson...

With the spread of electric vehicles (EVs), the EV load will have a significant impact on the planning and operation of the grid and the operation of the electricity market. Due to the charging and discharging characteristics of EVs, as well as their...

The acoustically actuated antenna technology enables a significant reduction in antenna dimension, facilitating miniaturization of ground-penetrating radar systems in the very high-frequency (VHF) band. However, the current acoustically actuated ante...

This paper presents the integration of memristors into N-path filter architectures to develop reconfigurable N-path filters with a tuneable bandwidth. Two different memristor-based N-path filter designs are proposed and systematically compared. One o...

Anomaly detection, as a critical task in time series data analysis, plays a pivotal role in ensuring industrial production safety, enhancing the precision of climate predictions and improving early warning for ocean disaster. However, due to the high...

Ensuring fairness in machine learning models applied to educational data is crucial for mitigating biases that can reinforce systemic inequities. This paper compares various fairness-enhancing algorithms across preprocessing, in-processing, and post-...

Pose transfer methods often struggle to simultaneously preserve fine-grained clothing textures and facial details, especially under large pose variations. To address these limitations, we propose a model based on the Multi-scale attention guided pose...

This systematic literature review investigates the integration of artificial intelligence (AI) and the Internet of Things (IoT) in marketing, with a focus on their application in enhancing consumer engagement, personalization, and strategic decision-...