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Optimizing Satellite Constellations for Global Internet Coverage: A Comparative Analysis of Communication Protocols and Orbital Configuration

Optimizing Satellite Constellations for Global Internet Cove...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Raj, Vivek Kumar, Biresh Thakur, Kanika Mitra, Dipra Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India

ISBN: (纸本)9798350385199

As a way to provide ubiquitous connectivity, satellite constellations are becoming more and more popular due to the growing need for worldwide internet access. In order to provide dependable and seamless worldwide internet coverage, this study investigates how satellite constellations might be optimized by comparing and contrasting communication protocols and orbital topologies. This ambitious aim involves a number of problems and complications, which are explored in the research along with the trade-offs and design considerations that must be made in order for the implementation to be effective. Two main elements form the framework of the comparative analysis. First, by considering factors like latency, data throughput, and coverage efficiency, it evaluates a range of communication protocols, including traditional geostationary satellites, Low Earth Orbit (LEO) constellations, and Medium Earth Orbit (MEO) satellites. Second, to ascertain the effect of various orbital configurations on worldwide internet coverage, the study considers satellite dispersion, orbital altitudes, and coverage patterns. By means of this study, the research endeavors to provide a valuable contribution to the current discussion on satellite constellations for worldwide internet coverage, therefore facilitating well-informed decisions about the design and implementation of these systems. © 2024 IEEE.

关键词： Geostationary satellites

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EEG-driven GAN for Alpha Rhythm Generation in Passive BCI 3

EEG-driven GAN for Alpha Rhythm Generation in Passive BCI

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Cinquetti, Ettore Zanni, Guglielmo Menegaz, Gloria Storti, Silvia F. University of Verona Dept. of Computer Science Italy University of Verona Dept. of Engineering for Innovation Medicine Italy

ISBN: (纸本)9798350378009

Passive brain-computer interface (BCI) based on electroencephalographic (EEG) signals show promise for monitoring human vigilance in critical settings that continuously strain human attention. However, the lack of extensive public datasets limits progress in artificial intelligence research. To overcome this problem, we implemented generative adversarial networks (GANs) to augment existing EEG datasets. We focused on two datasets: a publicly available resting-state dataset and a custom one simulating industrial activities. After extracting time-variant alpha indices using continuous wavelet transform, we compared the generated data with real data using the L2 distance metric and autocorrelation function. We additionally assessed two forecasting models trained on original and augmented datasets, comparing their predictions. The integration of synthetic data led to an improvement in signal behavior prediction, as evidenced by a 32% reduction in mean absolute error for the resting-state dataset. Furthermore, a metric inspired by the Frechét Inception Distance was computed using the forecasting model to discern the distributions of embeddings for the real and generated data, with results showing a strong resemblance between the two. This study challenges the limitation of acquiring domain-specific EEG data and aims to develop a robust signal generation framework. © 2024 IEEE.

关键词： Wavelet transforms

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A Novel Approach for Driver Drowsiness Detection using Deep Neural Networks 2

A Novel Approach for Driver Drowsiness Detection using Deep ...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sharon, K.V. Natarajan, B. Madhumitha, P. Dhanamathi, A. Prabu, S. Saranya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Dept. of Artificial Intell. and Data Science Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India K. Ramakrishnan College of Engineering Dept. of Cse Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver's eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions. © 2024 IEEE.

关键词： computer vision

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A comparative study of different scheduling approaches for splittable latency sensitive tasks in Fog-Cloud environment 2

A comparative study of different scheduling approaches for s...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Sandeep, Kone Sai Koundinya, Challa Aditya Prabhas, Adapa Venkata Swain, Chinmaya Kumar Mishra, Sambit Kumar SRM University Dept. of Computer Science Engineering India

ISBN: (纸本)9798350344882

IoT has revolutionized the way we live and the work we do by connecting different devices through the Internet. In the present scenario, the number of IoT devices are increasing rapidly due to the increase in technology and the increase in the comforts of life. Nowadays we can see that many of them are using IoT devices regularly, it's estimated that by the end of 2030, there will be 30 billion users who will be using IoT applications. These devices send data to the cloud for processing. Due to the distance of the cloud from the IoT devices, the application requests get delayed service responses. So to handle the latency sensitive applications we require the micro cloud service like fog servers deployed near to the data generation points. The fog layer lies between the IoT devices and Cloud which acts as an intermediate layer. This helps in reducing latency of the tasks and provide better performance. As the number of IoT applications keeps on increasing, the resources available with the fog nodes may not handle the upcoming demands. So to overcome these demands, we are using splittable methods to allocate the tasks to Fog/ Cloud nodes more compactly. If a task can be splitted before the deadline into different modules, then we split the given task and allocate those tasks to different fog nodes/ servers and then collecting back the data from the fog nodes/ servers and merging them into a single unit. With the help of this method, we can increase the performance of the system. © 2023 IEEE.

关键词： Fog computing

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Intelligent Behavior Recognition based on Gyroscope and Accelerometer

Intelligent Behavior Recognition based on Gyroscope and Acce...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Pin-Yu Lu, Yung-Han Lin, Chun-Ting He, Bo-Xian Pai, Tun-Wen Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

In an aging society, the rate of dementia would be dramatically increased. It is urgent to provide intelligent facilitative devices and appropriate medical care for those who with dementia symptoms, especially for the elders. Accordingly, the primary research target of this study is to automatically detect and monitor behavior of the elderly suffered from dementia. Although previous research has been conducted to identify various human movement patterns, the experimental data were all received from cameras. Furthermore, these related applications are not specifically designed to identify the behavior of the elderly with dementia. In order to improve previous research methods, our study adopted wearable multi-axis sensors to collect corresponding motion data with protected privacy. In addition to collecting raw data containing time series signals, frequency and spatial domain features were applied to detect human behaviors, and Random Forest algorithm was applied to construct a motion prediction model. The classification system for real-time motion recognition contained two main actions of users including holding/putting hand gestures and walking model. Finally, an additional camera on the chest was designed to be triggered and to take a shot according to previous settings. It could enhance practical applicability of the proposed intelligent system to assist the elderly with dementia to find her/his lost objects. This study developed an app for recording individual behaviors, checking the location and time of lost objects, and even recording activity information to provide healthcare professionals with decision-making suggestions for subsequent precision medical care. © 2023 IEEE.

关键词： Accelerometers

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DMDA: A Computational Resource Allocation Approach for IoT Devices in Fog Computing 4

DMDA: A Computational Resource Allocation Approach for IoT D...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Pramono, Luthfan Hadi Shen, Shan-Hsiang National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506490

Fog Computing enables the efficient offloading of computational tasks from IoT Devices to Fog nodes, enhancing processing efficiency and reducing latency. This paper introduces the Dynamic Matching with Deferred Acceptance (DMDA) algorithm, designed to optimize the allocation of GPU and CPU resources within Fog Computing environments, specifically for smart city applications. The DMDA algorithm allocates resources to IoT Devices based on parameters such as data type, computational load, priority, and responsiveness. Devices processing image or video data are given priority for GPU allocation, while other devices are assigned to CPUs based on their cumulative performance scores. Devices with lower priority are reassigned to CPU resources or excluded when resource availability is exhausted, significantly enhancing performance under high-demand scenarios. The algorithm employs a Mixed-Integer Linear Programming (MILP) model to maximize the aggregate score of allocated devices while adhering to resource constraints. Experiments conducted with 50 to 1,000 devices demonstrate that both the total Device Score rate and Allocated Device rate reach 100%, indicating optimal allocation. Empirical findings show that the DMDA algorithm improves resource utilization, reduces processing times, and ensures effective workload distribution across large-scale IoT deployments. © 2024 IEEE.

关键词： Fog computing

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Multilingual Sentiment Analysis using DistilBert

Multilingual Sentiment Analysis using DistilBert

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Gupta, Nishi Bhattarai, Mandip Chavan, Abhishek Pimpri Chinchwad College of Engineering Dept. of Applied Science and Humanities Maharastra Pune India Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Maharastra Pune India

ISBN: (纸本)9798331517953

In today's virtual age, studying feelings and sentiments through textual content and emojis is essential. This research article explores a way to use natural language processing (NLP) techniques to interpret feelings from textual content and emoji content. We used the distilbert-base-uncased-finetuned-sst2english version, a small variant model of BERT, to categorize feelings as positive or negative. Our experiments confirmed that the version executed extraordinarily properly throughout more than one language, with especially great effects on Indonesian textual content. Our assessment confirmed that the version has an excellent accuracy of91.50% and an F1 rating of 91.44% for Indonesian, highlighting its wonderful overall performance in this language. It additionally plays properly in English, French, German, Arabic, and Italian, even though overall performance may also range through language. These effects reveal the version's effectiveness for duties, social media monitoring, client comments evaluation, and inventory marketplace fashion assessment. We finish with a conclusion about the results of those findings. © 2024 IEEE.

关键词： Natural language processing systems

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Diabetes Mellitus Diagnosis using Optical Ring Resonators 18

Diabetes Mellitus Diagnosis using Optical Ring Resonators

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Prajwal, Manush Jesy Janet Kumari, J. Uppu, Maanas Mitrahass Chirchi, Vanajaroselin Vishalatchi, S. Darshan, D.N. The Oxford College of Engineering Dept. Computer Science and Engineering Bangalore India The Oxford College of Engineering Dept. of Isc Bangalore India Kalasalingam Academy of Research and Education Dept. of Ece Krishnankoil India

ISBN: (纸本)9789380544519

The research shows the use of optical ring resonators (ORRs) as a promising non-invasive tool for diagnosing diabetes mellitus, potentially replacing invasive procedures. By accurately measuring glucose levels in biological samples through resonance frequency shifts closely linked to varying glucose concentrations, ORRs point to innovative and precise glucose measurement for early diabetes detection. This advancement could transform disease monitoring by reducing patient discomfort from current invasive techniques. The study develops a highly sensitive, non-invasive method for detecting diabetes by analyzing subtle optical signal changes related to glucose levels in urine is around 165-180 mg/dl (normal) and resonant frequency output is around 36.026459 but in case of blood resonant frequency output is around0.348478amusing advanced optical ring resonator technology. The methodology involves designing optical ring resonators capable of detecting minute refractive index variations connected to glucose concentrations. Major findings demonstrate the precision of diagnosing diabetes, showcasing the potential for a rapid and reliable diagnostic tool. Interpretations emphasize the innovative approach's ability to significantly improve early detection, enabling timely intervention and enhanced diabetes management for better healthcare outcomes and quality of life for individuals with diabetes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： diabetes diagnosis diabetes mellitus non-invasive method optical signal ring resonator

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Enhancing Spam Email Detection with a Soft Voting Ensemble of Optimized Machine Learning

Enhancing Spam Email Detection with a Soft Voting Ensemble o...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Tazwar, Asif Daiyan, Md Mahir Jiabul Hoque, Md Saifuddin, Mohammed Khaliluzzaman, Md. International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Spam email detection is crucial for cybersecurity, as it protects user privacy and reduces security risks. The persistent presence of spammers necessitates continuous improvements in spam filtering measures. To address this challenge, this study employs Grid Search Optimizer to fine-tune the parameters of four distinct classifiers: Support Vector Machine (SVM), Random Forest, Naive Bayes, and XGBoost. Soft Voting serves as the final classifier, enhancing overall accuracy by integrating results from optimized classifiers to detect and classify spam emails across the Spam Mails Dataset and Enron1 Dataset. The experimental study compared the proposed model with relevant works and found that hyperparameter tuning and Soft Voting significantly enhanced its performance compared to existing approaches. The proposed ensemble model outperformed individual classifiers, achieving accuracies of 99.32% and 99.12%, respectively. Also, the ensemble model demonstrated an AUC of 1.00 on both datasets, indicating its effectiveness in distinguishing between spam and ham emails. This approach surpasses prior studies in accuracy, generalization, and robustness by innovatively combining Grid Search and Soft Voting. The constructed model exhibits high effectiveness and efficiency in the detection of spam emails. © 2024 IEEE.

关键词： Decision trees

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Multiclass Classification of Cereals Using Artificial Neural Network 11

Multiclass Classification of Cereals Using Artificial Neural...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Thapliyal, Suhani Singh, Tejaswini Nainwal, Ankita Sharma, Garima University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350326147

A diverse variety of finer features of objects may now be detected, processed, analyzed, and displayed by machine learning and imaging systems from their digital photographs in real-time thanks to recent advancements in hardware and software. The food business must keep up with population expansion by raising output and raising product standards. Cereals, which are at the bottom of the food industry's pyramid and are produced for both human and animal use, are directly or indirectly tied to the rise in food production. The cornerstone of human civilization and an integral part of daily living are cereal grains. Food, energy, and raw materials for industry have all become insatiable human needs in recent years, and agricultural production has grown significantly as a result. Also, cereal grains are an essential component of human diets and significant raw materials for both domestic and international trade. Automatic segregation of cereal grains becomes significant with time and advent of new technologies. Manual efforts not only lead to errors but are also less cost efficient. This study shows the potential of machine learning techniques for cereal recognition using computer vision, and it also discusses some problems that need to be fixed or looked into further in order to hasten the use of learning algorithms. © 2023 IEEE.

关键词： Neural networks

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