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Face Recognition via Thermal Imaging: A Comparative Study of Traditional and CNN-Based Approaches 24

Face Recognition via Thermal Imaging: A Comparative Study of...

引用

8th International Conference on Machine Learning and Soft Computing, ICMLSC 2024

作者： Lin, Shinfeng Chen, Luming Chen, Wensheng Chang, Boyu Dept. of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9798400716546

In this article, a face recognition via thermal imaging: a comparative study of traditional and CNN-based approaches is proposed. The methodology comprises two distinct components: traditional face recognition and CNN-based face recognition. In the traditional face recognition, we employ Random Forest (RF) and Support Vector Machine Classifier (SVM) techniques. Conversely, CNN-based face recognition leverages Convolutional Neural Networks (CNN) to identify individuals. Our research involves a comprehensive evaluation conducted across different databases, including the PUCV Drunk Thermal Face (PUCV-DTF) and the UCH Thermal Temporal Face (UCH-TTF) datasets. To emulate real-life scenarios, we introduce elements such as glasses, face mask, and noise into the original thermal images during experimentation. The recognition rates of traditional and CNN-based methods for small size and middle size databases achieve 90% and 100%, respectively, under a challenging condition, wearing glasses and mask. Experimental results demonstrate the feasibility and effectiveness of our proposed method, showcasing its robustness in tackling various challenges. © 2024 Owner/Author.

关键词： Infrared imaging

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Exploring Post-Quantum Cryptographic Algorithms for Secure Data Transmission 3

Exploring Post-Quantum Cryptographic Algorithms for Secure D...

引用

3rd IEEE International Conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Gulomov, Sherzod R. Khudayberganov, Temur R. Ravshanova, Maftuna X. Turdiev, Temur T. Atabayev, Sultonbek S. Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dean of the Faculty of Cyber Security Urgench Uzbekistan Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Information security Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Computer engineering Urgench Uzbekistan Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Information technology Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Computer engineering Urgench Uzbekistan

ISBN: (纸本)9798331516321

The rapid advancement of quantum computing introduces a critical threat to established cryptographic systems, such as RSA and ECC, by enabling quantum attacks those classical systems cannot withstand. This evolving threat creates a necessity for post-quantum cryptographic (PQC) algorithms to ensure secure data transmission in the quantum era. PQC encompasses diverse approaches, including lattice-based, hash-based, code-based, and multivariate polynomial cryptography. Each category offers distinct advantages and challenges: lattice-based algorithms provide robust security but may require substantial computational resources, while hash-based approaches are easier to implement but limited in flexibility. Code-based algorithms deliver strong security with significant computational demand, and multivariate polynomial approaches boast efficiency, though they face scrutiny over quantum resilience. This paper evaluates these PQC algorithms in terms of their security, performance, and feasibility, identifying optimal methods to secure data transmission in a future where quantum threats are prevalent. © 2024 IEEE.

关键词： Quantum cryptography

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Strategies for Helping Anchor-Based Trackers Learn re-ID Features for Smart City Surveillance

Strategies for Helping Anchor-Based Trackers Learn re-ID Fea...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Chen, Xiu-Zhi Li, Mu-Chuan Chen, Yen-Lin National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324136

Re-identification has become a crucial issue in computer vision today as it allows for tracking objects in both continuous and discontinuous scenarios. Despite achieving perfect detection results, anchor-based trackers encountered difficulties in effectively learning re-identification features, due to various issues. This research proposes strategies aimed at improving the capability of anchor-based trackers to learn high-quality re-identification (re-ID) features. The model developed through our strategies can extract more distinct features and achieve almost 0.57 Multiple Object Tracking Accuracy (MOTA) on MOT20, even under a limited training dataset. This result indicates that our proposed strategies hold potential for improving the performance of anchor-based trackers. © 2024 IEEE.

关键词： Anchor-Based Tracker Re-Identification Smart City Surveillance

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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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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka

ISBN: (纸本)9798350350821

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure. © 2024 IEEE.

关键词： Global positioning system

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A Multi-Routing Paths' Exploration Algorithm for Multi-paired Communicating Vehicles' Data Transmission Offloading using the Multi-access Edge Computing Architecture 4

A Multi-Routing Paths' Exploration Algorithm for Multi-paire...

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4th European Conference on Communication Systems, ECCS 2024

作者： Huang, Chung-Ming Tsai, Cheng-Han National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350377927

This work proposed a novel method for multiple communicating paired vehicles' data transmission offloading using the Multi-access Edge Computing (MEC) technique. When two paired communicating vehicles are communicating with each other through cellular network's Base Stations (BSs), the data transmission can be offloaded to the Cellular Vehicle-to-Vehicle (C-V2V) path that exists between these two vehicles using 5G NR's sidelink communication;these two paired communicating vehicles can switch back to the Cellular Vehicle-to-Infrastructure (C-V2I) communication to communicate with each other when the corresponding C-V2V path is broken. Using the proposed method, the MEC architecture serves as a centralized computing mechanism to determine the availability of k-hop C-V2V paths for paired communicating vehicles' data transmission offloading. Each vehicle periodically reports its context, such as position, speed, driving direction, and C-V2V sidelinks' Channel Busy Ratios (CBRs), to the MEC server to derive feasible k-hop C-V2V data transmission offloading paths. The simulation result demonstrates that the proposed method has (i) better throughput compared to the method of relying solely on C-V2I communication through BSs and (ii) better throughput, higher offloading fraction and lower average data loss rate comparing with the other k-hop-limited CV2V data transmission offloading method. © 2024 IEEE.

关键词： Edge computing

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Relating Muscle Activity and Mouse Sensitivity in FPS Game Players Using Surface EMG Signals 5

Relating Muscle Activity and Mouse Sensitivity in FPS Game P...

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5th International Conference on Biomedical engineering, IBIOMED 2024

作者： Prajapati, Vaibhav Turlapaty, Anish C. Sarma, Himangshu Ghorai, Mrinmoy Indian Institute of Information Technology Dept. of Electronics and Communication Engineering Sri City India Indian Institute of Information Technology Dept. of Computer Science and Engineering Sri City India

ISBN: (纸本)9798350389265

In the domain of first-person shooter (FPS) gaming, mouse sensitivity is crucial for performance. This paper presents an analysis showing the correlation between specific muscle activity and mouse movement. We analyzed mouse movements from twelve subjects with varying sensitivity levels. A new dataset electromyography analysis of human activity database 8 (EMAHA-DB8), is introduced which involved four mouse designs and two game modes: Medium (normal movement) and Hard (aggressive movement). Sensitivity settings were standardized using effective dots per inch (eDPI), calculated as Mouse DPI multiplied by in-game sensitivity. Hand muscle activity was measured using five Noraxon Ultium wireless surface electromyography (sEMG) sensors with Ag/AgCl electrodes. Various regression models, including Random Forest, Support Vector Regressor, K-Nearest Neighbors Regressor, MLP Regressor, and Gradient Boost Regressor, were used to predict eDPI from muscle movement patterns. Sequential feature selection identified the top ten features, highlighting the most significant muscles for predicting sensitivity. The results demonstrate that the Extensor Carpi Radialis (ECR) is the most active muscle, followed by the Extensor Digitorum (ED), while playing the chosen FPS game. These findings give insights for ergonomic and personalized equipment design to enhance gaming experience and performance. © 2024 IEEE.

关键词： Decision trees

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Relaxing Super Localization Frame Rate Requirements Utilizing a Novel 2D Interpolation Technique

Relaxing Super Localization Frame Rate Requirements Utilizin...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Tuccio, Giulia Afrakhteh, Sajjad Iacca, Giovanni Demi, Libertario University of Trento Dept. of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350346459

Ultrasound Localization Microscopy (ULM) by-passes the conventional diffraction limit, allowing the imaging of the vasculature at a microscopic resolution through precise localization of intravenously injected microbubbles. Thanks to their resolution, ULM generated images hold the promise to be the next diagnostic frontier for diseases that modify the vascular structure, such as cancer. However, deploying ULM in clinical settings is currently limited by the prolonged acquisition times and high frame rates needed. To relax the high frame rate requirement, we propose a new time-efficient ULM (TEULM) pipeline. The proposed TEULM introduces the radial basis functions (RBFs) as interpolators upstream of the ULM framework, to recover the missing information consequence of a reduced frame rate. We test TEULM using in vivo Rat's Brain and Kidney datasets acquired at a high frame rate (1 KHz). The results confirm the ability of TEULM to reconstruct super-resolution (SR) images with a similarity score (Dice score) of 80% for both datasets when simulating a frame rate acquisition of 100 Hz;i.e., we can relax the frame rate requirement for ULM by a factor 10 while keeping the SR image quality acceptable. © 2023 IEEE.

关键词： Interpolation

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Opcode and N-Gram Based Malware Classification 8

Opcode and N-Gram Based Malware Classification

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Ozcan, Omer Karaaltun, Muhammed Hacibeyoglu, Mehmet Selcuk University Dept. of Information Technologies Konya Turkey Necmettin Erbakan University Dept. of Computer Engineering Konya Turkey

ISBN: (纸本)9798331531492

Malwares are programs that perform unwanted behaviors on computer systems. There are many types of malwares, such as keylogger, ransomware, rootkit, backdoor and trojan horse. These programs are constantly developing and increasing in number. For these reasons, the detection and classification of malware is an area of interest for researchers. Signature-based methods were used at first for the detection and classification of malicious software. However, with the development of malware over time, these methods have lost their function. With the development of static and dynamic detection methods, many parameters began to be used in the detection and classification of malware. In this study, opcode and N-Gram sequences which are static detection and classification methods were used. Firstly, the frequencies of opcode and N-Gram sequences were calculated. Then these two sequences were reduced and combined by the entropy-based knowledge gain method. The resulting new feature vectors were classified using classification algorithms. Accuracy rates of up to 99% were achieved in the classification results. © 2024 IEEE.

关键词： Malware

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Oversample minority classes in Lung Ultrasound using Generative Adversarial Network

Oversample minority classes in Lung Ultrasound using Generat...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Fatima, Noreen Mento, Federico Demi, Libertario University of Trento Dept. of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350346459

The development of a strong methodological strategy focusing on class imbalance issues for Lung Ultrasound (LUS) data is lacking in the state of the art. Traditional oversampling methods such as geometric and affine transformations generate quasi-duplicates of the original data. This problem could be resolved through generative adversarial networks (GANs). However, in classic GANs the generator cannot handle mode collapse issues and the model's performance remains unstable when images in different classes look similar. To address this problem, we proposed a novel approach based on a supervised autoencoder generative adversarial network (SA-GAN), which could help to oversample the minority classes. At first, we build a latent space with the help of a supervised autoencoder (SA) to handle the mode collaspe issues. Then the trained weights of the SA are used to construct the generator and discriminator models. During the adversarial training of both models, the loss function is calculated for real and generated images through a customized gradient penalty loss function. This also allows classifying each image into real or generated, and its corresponding classes. This approach was tested on a dataset comprising 5500 LUS images, which were collected from 35 COVID-19 patients. As a result, SA-GAN provided the 900 generated LUS samples in minority classes to balance with the majority class. The proposed approach significantly improved the classification accuracy by up to 5% using synthetic augmentation through SA-GAN over the classic data augmentation method. © 2023 IEEE.

关键词： Generative adversarial networks

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