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Integration of Q-Learning with Maximal Ratio Combining to Enhance IoT Communications in Dynamic Fading Environments 12

Integration of Q-Learning with Maximal Ratio Combining to En...

引用

12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Sharma, Rashmi Upadhyay, Abhay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Nagpur Campus Pune India

ISBN: (纸本)9798350387315

This paper serves to show how Q-learning can be used in complement with Maximal Ratio Combining to better IoT communication systems under dynamic fading, high-density networks, and severe interference sources. The ML-enhanced MRC demonstrated promising improvements in signal strength and quality, allowing for performance that was either above or at least equal to the regular MRC. The ML-enhanced presented the added benefit of dynamic adaptation, modifying its performance to meet real-life needs by adapting to issues such as increased density and interference. These results support that integrating Q-learning into MRC-based frameworks significantly increases the reliability and efficiency of IoT communication protocols. Future investigations will aim to improve ML algorithms more appropriate for devices with few resources and evaluate their potential with state-of-the-art technology to foster future applications across intelligent IoT networks. These approaches have the potential to support more efficient operations and increased connectivity in IoT systems. © 2024 IEEE.

关键词： Fading MRC Q-Learning Wireless

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Interactive Educational Game Development Using Modified Single Stage Detector 28

Interactive Educational Game Development Using Modified Sing...

引用

28th International computer Science and engineering Conference, ICSEC 2024

作者： Usmanto, Kristiawan Dwi Budiyanta, Nova Eka Yuniarno, Eko Mulyanto Adi, Cyprianus Kuntoro Universitas Sanata Dharma Dept. of Informatics Yogyakarta Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Surabaya Indonesia Universitas Katolik Indonesia Atma Jaya Dept. of Electrical Engineering Jakarta Indonesia Institut Teknologi Sepuluh Nopember Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350366860

This research develops a real-Time hand gesture detection system using the You Only Look Once (YOLO) algorithm for simple math learning applications aimed at children with special needs. This application is a simple drag and drop game using hand gesture indicators. The system utilizes computer vision and deep learning technology to recognize hand gestures in videos which are then converted into interactive inputs in learning media. System testing involves hyperparameter tuning methods, including learning rate, image size, and momentum adjustments. The test results show that the YOLO algorithm with hyperparameter tuning method with hyperparameter values of learning rate 0.0001, image size 320, and momentum 0.937 is effective in detecting hand gestures with an mAP@0.5: 0. 95 score of 93.27%. The test results provide the potential for using a real-Time hand gesture detection system as an interesting and interactive learning method for children with disabilities, especially for those who have limitations in communication. © 2024 IEEE.

关键词： Gesture recognition

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Adaptive Strategies for Maximizing Spectral Efficiency in dual branch MRC Systems Facing non-Uniform Fading Patterns 12

Adaptive Strategies for Maximizing Spectral Efficiency in du...

引用

12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

ISBN: (纸本)9798350387315

The presented research paper investigates the development and performance of adaptive strategies for nonuniform fading patterns in dual-branch Maximal Ratio Combining systems. The study employs key enabling strategies, including power control, adaptive modulation, and antenna selection, with the purpose of improving the spectral efficiency and reliability of wireless communication systems. Conducting simulations in MATLAB and Python, this paper presents a comprehensive analysis using heatmaps, 3D surface plots, and dynamic simulation outputs to show the effect of the strategies under different Signal-to-Noise Ratios and fading conditions. The outcomes, illustrated in a clear-cut manner in the visuals, reveal that the adaptive strategies lead to a drastic reduction in the Bit Error Rates and a more optimal distribution of power across the branches, ensuring highly effective performance even in the case of the most challenging non-uniform fading patterns. Ultimately, the research findings show sight into the necessity of adaptive strategies to ensure proper coverage and quality of communication in non-uniform fading conditions, which novel research could benefit from. © 2024 IEEE.

关键词： MATLAB

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Congestion Probabilities in a Mobile Hotspot Supporting Quasi-Random Traffic 5

Congestion Probabilities in a Mobile Hotspot Supporting Quas...

引用

5th International Workshop on Microwave Theory and Technology in Wireless Communications, MTTW 2023

作者： Vlasakis, Marinos Moscholios, Ioannis Sarigiannidis, Panagiotis Logothetis, Michael University of Peloponnese Dept. Informatics & Telecommunications Tripolis Greece University of W. Macedonia Dept. Electrical & Computer Engineering Kozani Greece University of Patras Dept. Electrical & Computer Engineering Patras Greece

ISBN: (纸本)9798350393491

In this paper, we consider a vehicle that has an access point of fixed capacity. The vehicle accommodates a finite number of users who generate calls (quasi-random process). Each call requires a single bandwidth unit to be serviced for an exponentially distributed service time. The applied call admission policy depends on which phase the vehicle is. Two phases are taken into account: a moving and a stop one. During the moving phase, only new calls are serviced under the complete sharing policy. During the stop phase, both new and handover calls are being serviced, while the latter are being prioritized utilizing a probabilistic bandwidth reservation policy. The analytical determination of the various performance metrics of the system presented in this paper is accurate and is based on three-dimensional Markov chains. © 2023 IEEE.

关键词： Vehicles

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Some Notes on Tesla Coil Design and Power Transfer Performances 8

Some Notes on Tesla Coil Design and Power Transfer Performan...

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8th International Conference on Smart and Sustainable Technologies, SpliTech 2023

作者： Blažević, Zoran University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Aritecture Dept. of Electronics and Computer Engineering Split Croatia

ISBN: (纸本)9789532901283

In this paper, we revisit the Tesla Coil, a device that had the pivotal role in the development of radio. The analysis is conducted on the basis of the antenna theory, and the feasibility for its application to wireless power transfer is tested by experiment. It is shown that Tesla coil can very well be used for an efficient transmission of electrical energy without wires at small and mid-ranges. © 2023 University of Split, FESB.

关键词： Antenna feeders

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Advancements in Flood Mapping Methodologies: A Comprehensive Review 6

Advancements in Flood Mapping Methodologies: A Comprehensive...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Chandran, Divya V Anitha, J. Chandran, Divya V Vijayakumar, Amrutha Raveendran, Anjana Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Kalady India

ISBN: (纸本)9798350381689

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-dept. overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events. © 2024 IEEE.

关键词： Floods

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Deep Learning-Based Flood Detection System Using Semantic Segmentation 7

Deep Learning-Based Flood Detection System Using Semantic Se...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandran, Divya V Anitha, J. Anusree, A.R. Nair, Gowri Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350372816

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster's scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance. © 2024 IEEE.

关键词： Semantic Segmentation

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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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Development Of An Image Restoration Algorithm Utilizing Generative Adversarial Networks (GAN’s) For Enhanced Performance In engineering Applications: A Comprehensive Approach To Improving Image Quality And Clarity Through Advanced Machine Learning Techniques

Development Of An Image Restoration Algorithm Utilizing Gene...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Manjunath, T.C. Pavithra, G. Samyama Gunjal, G.H. Ninawe, Swapnil S. Dept. of Electronics & Communication Engineering Rajrajeswari College of Engineering Bangalore India Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science & Engineering University Visvesvaraya College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331505134

Image restoration, a critical task in computer vision and image processing, focuses on recovering degraded or damaged images to their original, high-quality state. This paper introduces an innovative approach to image restoration using Generative Adversarial Networks (GANs). GANs, a prominent deep learning framework, consist of two neural networks—a generator and a discriminator—that compete to produce and evaluate realistic images. The generator creates images, while the discriminator distinguishes between real and generated ones, refining the generator's capability through adversarial training. Leveraging GANs' ability to learn complex image features, the proposed algorithm restores degraded images affected by noise, blur, and low resolution, producing high-quality, realistic results. Simulation outcomes demonstrate significant advancements in image restoration, showcasing GANs as a powerful tool for addressing challenges in this domain. The study underscores the potential of GANs in generating visually appealing restorations and advancing the state-of-the-art in image processing and restoration tasks. © 2024 IEEE.

关键词： Generative adversarial networks

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Medical Image Classification: A Multi Model Approach with Explainable Models 16

Medical Image Classification: A Multi Model Approach with Ex...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Bhattacharhee, Turja Hussain, Syed Ali Sanki, Pradyut Kumar Mandol, Pranab Srm University Dept. of Computer Science and Engineering AP Guntur India Srm University Dept. of Electronics and Communication Engineering AP Guntur India Srm University Dept. of Physics AP Guntur India

ISBN: (纸本)9798331505264

This extensive research delves into the intersection of MRI imaging and deep learning, in the task of identifying and categorizing brain tumors. In addition to models like VGG16 and ResNet101 a designed Convolutional Neural Network (CNN) was developed and thoroughly evaluated showcasing a range of techniques utilized. Augmentation methods were purposefully applied to enrich the dataset, enhancing the models' robustness and adaptability. Evaluation metrics, including the F1 score, recall, accuracy, and precision gave a general picture of the model's performance. Furthermore, leveraging Explainable AI (XAI) techniques such as LIME unveiled insights, into the decision-making processes underlying the models enhancing their interpretability and trustworthiness. The study findings ultimately underscore the potential of learning in revolutionizing automated brain tumor diagnosis and classification poised to enhance patient care pathways and medical diagnostic capabilities significantly. © 2024 IEEE.

关键词： Convolutional neural networks

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