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Algorithm for Deriving Position Measurement Based on Automatically Saved Image Matching

Transactions of the Korean Institute of Electrical Engineers 2025年第3期74卷 491-499页

作者： Hwang, Dong-Yun Seo, Kyeong-Wook Lee, Min-Ho Kim, Yong-Hun Song, Jin-Woo Dept. of Artificial Intelligence and Robotics Sejong University Korea Republic of Dept. of Intelligent Mechatronics Engineering Sejong University Korea Republic of

In this paper, we propose an algorithm that equips a drone with a monocular camera and a laser range finder to derive position measurements for smooth mission execution and return to the home position, even in situations where GNSS is not available. This algorithm stores images and create an orthoimage when GNSS is available, and from the time the GNSS signal is lost, it matches the current image with the database image based on image feature point matching to determine the drone's current location and to employ return to home mission. We validated the algorithm with data acquired from actual drone flights and confirmed that the proposed algorithm could be used practically and effectively with sufficient accuracy for return to home mission. © The Korean Institute of Electrical Engineers.

关键词： Drones

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Defense and Recovery Strategies for Flash-Based Storage Under Ransomware Attacks: A Survey

Defense and Recovery Strategies for Flash-Based Storage Unde...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Yin, Junhong Kang, Kyungtae Major in. Bio Artificial Intelligence Hanyang University Dept. Computer Science and Engineering Ansan Korea Republic of Hanyang University Dept. Artificial Intelligence Ansan Korea Republic of

ISBN: (纸本)9798331510756

As ransomware evolves, traditional OS-based de-tection mechanisms face growing challenges, particularly from advanced attacks exploiting system vulnerabilities and escalating privileges. The continuous evolution of ransomware attacks has driven researchers to explore storage-level defense strategies, focusing on the potential of Solid-State Drives (SSDs) to enhance data protection and recovery. This paper presents a compre-hensive survey of the current state and future developments in SSD firmware-level ransomware detection and recovery, based on literature published between 2015 and 2024. Firmware-level I/O monitoring helps detect ransomware by identifying the typical 'read-encrypt-overwrite' pattern. This low-level access offers an advantage in detecting ransomware activity. However, conventional detection methods are often prone to false pos-itives. To enhance detection accuracy, some systems integrate machine learning models within the SSD controllers, analyzing I/O request patterns and entropy values of written data to assess potential threats. The Garbage Collection(Gc)mechanism plays a crucial role in data recovery by retaining older versions of data until reclaimed, which can aid in restoring encrypted content. Similarly, backing up legitimate I/O operations incurs performance overhead. To address this, some systems utilize techniques such as neural networks and remote backups to reduce unnecessary performance degradation. Future research should prioritize developing lightweight solutions tailored for SSDs with limited computational resources. Emphasis should be placed on enhancing the timeliness of real-time detection to effectively respond to the rapidly evolving nature of ransomware attacks. © 2025 IEEE.

关键词： Firmware

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Efficient Path Planning for Emergency Medical Services With Stable Connectivity Demands

Efficient Path Planning for Emergency Medical Services With ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Wen-Pin Hu, Xuan-Zhang Nguyen, Van-Linh Nguyen, Lan-Huong Hwang, Ren-Hung Dept. of Computer Science and Information Engineering Chiayi Taiwan College of Artificial Intelligence Taiwan

ISBN: (纸本)9798350351255

Currently, online map navigation applications are essential to our daily demands, such as route finding in a big city or booking a taxi trip. Further, the applications also play a pivotal role in finding access to the nearest medical aid in big cities with complex traffic networks. However, existing commercial map programs are unable to provide route suggestions in areas where a continuous network connection is assured, which is essential for distant emergency medical services. This study introduces a highly effective personalized shortest-path algorithm for finding the fastest path of an emergency vehicle (EV) while also ensuring stable network connections for potential remote robotic surgery. By using a customized A-star algorithm, the experimental findings demonstrate that this technique can outperform current search approaches with a 5% shorter path length per 5km. For rescue missions in a large city, the enhancement is remarkable and has the potential to save lives. Due to high compatibility with the existing search algorithms, this method can easily be included as a critical feature for existing commercial map apps. © 2024 IEEE.

关键词： Robotic surgery

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of Science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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Incremental Learning for Enhancing Misbehavior Detection in Multi-Access Vehicular Networks 100

Incremental Learning for Enhancing Misbehavior Detection in ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Nguyen, Lan-Huong Nguyen, Van-Linh Hwang, Ren-Hung College of Artificial Intelligence Tainan Taiwan Dept. of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798331517786

Autonomous cars are already the driving force behind achieving the goal of intelligent mobility in smart cities. However, providing internet access for automobiles introduces additional risks of distributing false information. By broadcasting fake sharing data, an attack vehicle has the potential to mislead surrounding cars and trigger catastrophic accidents. This work introduces a novel incremental learning model for misbehavior detection in vehicular communications. Unlike previous methods which mostly relied on the pre-trained model, in this work, we develop accumulative learning capability for the misbehavior detection engines at vehicles or distributed roadside units. The model can assist in obtaining new knowledge from continuous learning. The simulation results indicate that the proposed model outperforms the state-of-the-art studies by up to 12% in terms of detection accuracy and 45% faster in continuous processing, particularly in the single detector mode. © 2024 IEEE.

关键词： 5G mobile communication systems

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High-Throughput Median Filtering for Large Kernel Sizes on CUDA 17

High-Throughput Median Filtering for Large Kernel Sizes on C...

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17th International Conference on Machine Vision, ICMV 2024

作者： Yildirim, Samed Topal, Cihan Dept. Computer Engineering Istanbul Technical University Istanbul Turkey Dept. Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781510688278

In this paper, we propose a novel approach to median filtering, a widely employed technique for mitigating specific types of noise in images and signals. While median filtering is effective, its computational demands, especially with larger filter sizes, pose challenges for real-time applications. To address this, we present a highly efficient, optimized, and parallelized median filtering algorithm tailored for CUDA platform. Leveraging a histogram-based method and operating on 8-bit data, our approach outperforms sorting-based alternatives, particularly for larger filter sizes. Quantitative experiments demonstrate substantial performance improvements, with our algorithm achieving speedup up to 20x and 59x compared to the second best GPU-based and CPU-based algorithms for mid and large filter sizes, respectively. This significant enhancement in processing speed makes our algorithm a compelling choice for real-time applications where rapid noise removal is paramount, thereby extending the practical utility of median filtering in various domains. © 2025 SPIE.

关键词： Median filters

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Revolutionizing Pandemic Healthcare: Mask Detection and Patient Face Recognition 14

Revolutionizing Pandemic Healthcare: Mask Detection and Pati...

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Saraswat, Shipra Singh, Sofia Middha, Parth Thirwani, Parth Rohilla, Harsh Amity University Dept. of Computer Science & Engineering Noida India Amity University Dept. of Artificial Intelligence Noida India

ISBN: (纸本)9798350344837

During the COVID-19 pandemic, Delhi, India, faced a pressing issue where approximately 1,500 COVID-19-positive patients went missing. In public health emergencies, such as pandemics, natural disasters, or other calamities, hospitals, and medical centres experience a sudden influx of patients, and hospital management faces difficulties in keeping track of patients, especially when they need to be moved between facilities or when new temporary healthcare facilities are set up. As a consequence of these challenges, there can be an increase in missing person cases. Patients may be inadvertently separated from their families. The human face is a unique biometric system that can determine the age, gender, mood, of an individual, and even identity for verification purposes. Harbouring the power of deep learning and artificial intelligence, one of the most important applications of computer vision is Patient Identification. In this study, we have proposed a state-of-the-art patient face detection model using a twofold model that uses MTCNN short for 'multi-task cascaded convolution neural network' for face detection and alignment purposes with a FaceNet Convolutional Neural Network (CNN) a renowned face embedding algorithm finally with KNN algorithm as a classifier to get an accuracy of 97.1%. Also, to ensure public safety during the pandemic we have constituted a Resnet34 model for mask detection trained on the Face Mask Detection dataset with an accuracy of 97%. This study not only addresses the immediate challenges of patient identification and safety during crises but also carries implications for broader healthcare applications. The proposed models offer promising avenues for enhancing patient care and security. © 2024 IEEE.

关键词： Face recognition

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Flood Risk Simulation and Mitigation in Coastal Areas: An ArcGIS Pro and Deep Learning Approach 11

Flood Risk Simulation and Mitigation in Coastal Areas: An Ar...

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11th International Conference on Soft Computing and Machine intelligence, ISCMI 2024

作者： Khurshedjon, Farkhodov Lee, Seok-Hwan Lim, Seon-Ja Kwon, Ki-Ryong Pukyong National University Dept. of Artificial Intelligence Convergence Busan Korea Republic of Dong-A University Dept. of Computer and Artificial Intelligence Engineering Busan Korea Republic of Pukyong National University Dept. of IT Convergence and Application Engineering Busan Korea Republic of

ISBN: (纸本)9798331518127

The integration of ArcGIS with Deep Learning models demonstrates a significant advancement in coastal flood prediction and prevention, offering enhanced accuracy and detail in flood risk mapping. Our suggested approach not only improves disaster preparedness and response but also holds promise for broader application across vulnerable coastal regions. Continued refinement and expansion of proposed model will further solidify its role as a critical tool in enhancing coastal flood resilience on a global scale. This study underscores the effectiveness of integrating ArcGIS with Deep Learning models for coastal flood prediction and prevention. The model - whether prioritizing precision, recall, or a balanced approach - offers unique advantages tailored to different application needs. The promising results highlight the potential of this integrated approach to significantly improve disaster preparedness and response in vulnerable coastal areas. Moving forward, further refinement and expansion of this methodology across diverse regions will strengthen its role in enhancing global coastal flood resilience. © 2024 IEEE.

关键词： Prediction models

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A Tool for Security Risk Assessment for APT Attacks: using Scenarios, Security Requirements, and Evidence 31

A Tool for Security Risk Assessment for APT Attacks: using S...

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31st IEEE International Requirements engineering Conference, RE 2023

作者： Park, Sihn-Hye Kim, Dongyoon Lee, Seok-Won Ajou University Dept. of Artificial Intelligence Dept. Software and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350326895

Advanced Persistent Threat (APT) attacks are complex, employing diverse attack elements and increasingly intelligent techniques. This paper introduces a tool for security risk assessment specifically designed for these attacks. This tool assists security teams in systematically analyzing APT attacks to derive adaptive security requirements for mission-critical target systems. Additionally, the tool facilitates the assessment of security risks, providing a comprehensive understanding of their impact on target systems. By leveraging this tool, security teams can enhance defense strategies, mitigating potential threats and ensuring the security of target systems. © 2023 IEEE.

关键词： Risk assessment

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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