Point Cloud Wall Projection for Realistic Road Data Augmentation.

Several approaches have been developed to generate synthetic object points using real LiDAR point cloud data for advanced driver-assistance system (ADAS) applications. The synthetic object points generated from a scene (both the near and distant objects) are essential for several ADAS tasks. However, generating points from distant objects using sparse LiDAR data with precision is still a challenging task.

Sybil Attack-Resistant Blockchain-Based Proof-of-Location Mechanism with Privacy Protection in VANET.

In this paper, we propose a Proof-of-Location (PoL)-based location verification scheme for mitigating Sybil attacks in vehicular ad hoc networks (VANETs). For this purpose, we employ smart contracts for storing the location information of the vehicles. This smart contract is maintained by Road Side Units (RSUs) and acts as a ground truth for verifying the position information of the neighboring vehicles.

The Design of a Low-Noise CMOS Image Sensor Using a Hybrid Single-Slope Analog-to-Digital Converter.

In this study, we describe a low-noise complementary metal-oxide semiconductor (CMOS) image sensor (CIS) with a 10/11-bit hybrid single-slope analog-to-digital converter (SS-ADC). The proposed hybrid SS-ADC provides a resolution of 11 bits in low-light and 10 bits in high-light. To this end, in the low-light section, the digital-correlated double sampling method using a double data rate structure was used to obtain a noise performance similar to that of the 11-bit SS-ADC under low-light conditions, while maintaining linear in-out characteristics.

Confidence-Guided Frame Skipping to Enhance Object Tracking Speed.

Object tracking is a challenging task in computer vision. While simple tracking methods offer fast speeds, they often fail to track targets. To address this issue, traditional methods typically rely on complex algorithms.

A 0.73 dB Multi-Gain Low Noise Amplifier Design with Fast Mode-Switching for 5G/4G Applications.

In this paper, a sub-1dB Low Noise Amplifier (LNA) with several gain modes, including amplification and attenuation modes required for the fifth and fourth generations (5G/4G) of mobile network applications, is proposed. Its current consumption is adaptive for every gain mode and varies to lower currents for lower amplifications due to the importance of current consumption for mobile network applications. The proposed LNA features an innovative architecture with a three-core input structure supporting multi-gain modes, achieving high gain and ultra-low noise performance.

Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review.

Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed at improving the interpretability and utility of nuclear medicine protocols. We discuss advanced image generation algorithms designed to recover details from low-dose scans, uncover information hidden by specific radiopharmaceutical properties, and enhance the sensing of physiological processes.

Pulse Shape Discrimination of n/γ in Liquid Scintillator at PMT Nonlinear Region Using Artificial Neural Network Technique.

Reactor-emitted electron antineutrinos can be detected via the inverse beta decay reaction, which produces a characteristic signal: a two-fold coincidence between a prompt positron event and a delayed neutron capture event within a specific time frame. While liquid scintillators are widely used for detecting neutrinos reacting with matter, detection is difficult because of the low interaction of neutrinos. In particular, it is important to distinguish between neutron (n) and gamma (γ) signals.

Damage Localization and Severity Assessment in Composite Structures Using Deep Learning Based on Lamb Waves.

In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures.

Sudden Fall Detection of Human Body Using Transformer Model.

In human activity recognition, accurate and timely fall detection is essential in healthcare, particularly for monitoring the elderly, where quick responses can prevent severe consequences. This study presents a new fall detection model built on a transformer architecture, which focuses on the movement speeds of key body points tracked using the MediaPipe library. By continuously monitoring these key points in video data, the model calculates real-time speed changes that signal potential falls.

Performance Analysis and Design Principles of Wireless Mutual Broadcast Using Heterogeneous Transmit Power for Proximity-Aware Services.

As proximity-aware services among devices such as sensors, IoT devices, and user equipment are expected to facilitate a wide range of new applications in the beyond 5G and 6G era, managing heterogeneous environments with diverse node capabilities becomes essential. This paper analytically models and characterizes the performance of heterogeneous random access-based wireless mutual broadcast (RA-WMB) with distinct transmit (Tx) power levels, leveraging a marked Poisson point process to account for nodes' various Tx power. In particular, this study enables the performance of RA-WMB with heterogeneous Tx power to be represented in terms of the performance of RA-WMB with a common Tx power by deriving an equivalent Tx power based on the probability distribution of heterogeneous Tx power and the path loss exponent.

Doubly Structured Data Synthesis for Time-Series Energy-Use Data.

As the demand for efficient energy management increases, the need for extensive, high-quality energy data becomes critical. However, privacy concerns and insufficient data volume pose significant challenges. To address these issues, data synthesis techniques are employed to augment and replace real data.

Hardware-Assisted Low-Latency NPU Virtualization Method for Multi-Sensor AI Systems.

Recently, AI systems such as autonomous driving and smart homes have become integral to daily life. Intelligent multi-sensors, once limited to single data types, now process complex text and image data, demanding faster and more accurate processing. While integrating NPUs and sensors has improved processing speed and accuracy, challenges like low resource utilization and long memory latency remain.

MIRA-CAP: Memory-Integrated Retrieval-Augmented Captioning for State-of-the-Art Image and Video Captioning.

Generating accurate and contextually rich captions for images and videos is essential for various applications, from assistive technology to content recommendation. However, challenges such as maintaining temporal coherence in videos, reducing noise in large-scale datasets, and enabling real-time captioning remain significant. We introduce MIRA-CAP (Memory-Integrated Retrieval-Augmented Captioning), a novel framework designed to address these issues through three core innovations: a cross-modal memory bank, adaptive dataset pruning, and a streaming decoder.

Designing a Multivariate Belt Conveyor Idler Stall Detection and Identification System with Scalability Analysis.

Belt conveyor idlers are freely rotating idlers supporting the belt of a conveyor, and can induce severe frictional damage to the belt as they fail. Therefore, fast and accurate detection of idler faults is crucial for the effective maintenance of belt conveyor systems. In this article, we implement and evaluate the performance of an idler stall detection system based on a multivariate deep learning model using accelerometers and microphone sensor data.

Development of Test Cases for Automated Vehicle Driving Safety Assessment Using Driving Trajectories.

For consumers to have confidence in the safety of automated vehicles (AVs), AVs must be assessed using systematically developed scenarios to verify driving safety and reliability. In particular, verification using scenarios has been widely performed for the assessment and certification of AVs. This study aims to develop test cases based on driving trajectories to assess the driving safety of AVs.

Formal Security Reassessment of the 5G-AKA-FS Protocol: Methodological Corrections and Augmented Verification Techniques.

The 5G-AKA protocol, a foundational component for 5G network authentication, has been found vulnerable to various security threats, including linkability attacks that compromise user privacy. To address these vulnerabilities, we previously proposed the 5G-AKA-Forward Secrecy (5G-AKA-FS) protocol, which introduces an ephemeral key pair within the home network (HN) to support forward secrecy and prevent linkability attacks. However, a re-evaluation uncovered minor errors in the initial BAN-logic verification and highlighted the need for more rigorous security validation using formal methods.

Energy-Efficient Dynamic Enhanced Inter-Cell Interference Coordination Scheme Based on Deep Reinforcement Learning in H-CRAN.

The proliferation of 5G networks has revolutionized wireless communication by delivering enhanced speeds, ultra-low latency, and widespread connectivity. However, in heterogeneous cloud radio access networks (H-CRAN), efficiently managing inter-cell interference while ensuring energy conservation remains a critical challenge. This paper presents a novel energy-efficient, dynamic enhanced inter-cell interference coordination (eICIC) scheme based on deep reinforcement learning (DRL).

Attention-Based Malware Detection Model by Visualizing Latent Features Through Dynamic Residual Kernel Network.

In recent years, significant research has been directed towards the taxonomy of malware variants. Nevertheless, certain challenges persist, including the inadequate accuracy of sample classification within similar malware families, elevated false-negative rates, and significant processing time and resource consumption. Malware developers have effectively evaded signature-based detection methods.

Child-Centric Robot Dialogue Systems: Fine-Tuning Large Language Models for Better Utterance Understanding and Interaction.

Dialogue systems must understand children's utterance intentions by considering their unique linguistic characteristics, such as syntactic incompleteness, pronunciation inaccuracies, and creative expressions, to enable natural conversational engagement in child-robot interactions. Even state-of-the-art large language models (LLMs) for language understanding and contextual awareness cannot comprehend children's intent as accurately as humans because of their distinctive features. An LLM-based dialogue system should acquire the manner by which humans understand children's speech to enhance its intention reasoning performance in verbal interactions with children.

Time-Series Representation Feature Refinement with a Learnable Masking Augmentation Framework in Contrastive Learning.

In this study, we propose a novel framework for time-series representation learning that integrates a learnable masking-augmentation strategy into a contrastive learning framework. Time-series data pose challenges due to their temporal dependencies and feature-extraction complexities. To address these challenges, we introduce a masking-based reconstruction approach within a contrastive learning context, aiming to enhance the model's ability to learn discriminative temporal features.

Exploring Technology Acceptance of Healthcare Devices: The Moderating Role of Device Type and Generation.

The increasing adoption of healthcare devices necessitates a deeper understanding of the factors that influence user acceptance in this rapidly evolving area. Therefore, this study examined the factors influencing the technology acceptance of healthcare devices, focusing on radar sensors and wearable devices. A total of 1158 valid responses were used to test hypotheses, mediation, and moderation effects using SmartPLS 4.

Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework.

Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud.

DNA-PRESENT: An Improved Security and Low-Latency, Lightweight Cryptographic Solution for IoT.

The vast interconnection of resource-constrained devices and the immense amount of data exchange in the Internet of Things (IoT) environment resulted in the resurgence of various security threats. This resource-constrained environment of IoT makes data security a very challenging task. Recent trends in integrating lightweight cryptographic algorithms have significantly improved data security in the IoT without affecting performance.

Development and Evaluation of Five-in-One Vaccine Microneedle Array Patch for Diphtheria, Tetanus, Pertussis, Hepatitis B, and Type b: Immunological Efficacy and Long-Term Stability.

: The development of a five-in-one vaccine microneedle patch (five-in-one MN patch) aims to address challenges in administering vaccines against Diphtheria (DT), Tetanus (TT), Pertussis (wP), Hepatitis B (HBsAg), and type b (Hib). Combining multiple vaccines into a single patch offers a novel solution to improve vaccine accessibility, stability, and delivery efficiency, particularly in resource-limited settings. : The five-in-one MN patch consists of four distinct microneedle arrays: DT and TT vaccines are coated together on one array, while wP, HepB, and Hib vaccines are coated separately on individual arrays.

Cationic Hydroxyethyl Cellulose Nanocomplexes and RANK siRNA/Zoledronate Co-Delivery Systems for Osteoclast Inhibition.

Background/objectives: In this study, HECP2k polymer, polyethylenimine2k (PEI2k)-modified hydroxyethyl cellulose (HEC) was utilized to form the nanocomplexes with receptor activator of nuclear factor k-B (RANK) siRNA and zoledronate (Zol) for osteoclast inhibition. HECP2k/(RANK siRNA + Zol) nanocomplexes prepared by simple mixing were anticipated to overcome the low transfection efficiency of siRNA and the low bioavailability of Zol.

Methods: The characterization of both HECP2k/(pDNA + Zol) nanocomplexes and HECP2k/(RANK siRNA + Zol) nanocomplexes was performed.