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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Diamanti, Maria Kapsalis, Georgios Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350370218

Rate-Splitting Multiple Access (RSMA) has recently emerged as an effective technique for increasing network capacity by smartly controlling the tradeoff between decoding and treating interference as noise. In this paper, aligned with the need for sustainable wireless networks, we study the energy-efficient power and rate allocation of the common and private messages in the downlink of a network where rate-splitting is adopted. The corresponding energy efficiency maximization problem is transformed into a multi-agent Deep Reinforcement Learning (DRL) problem, based on which each private stream transmitted in the downlink constitutes a different DRL agent. The formulated DRL problem is solved by using Deep Q-Learning (DQL) algorithm and training a single Deep Q-Network (DQN) from the cumulative experiences gained from the DRL agents and their exploration of the environment, i.e., the exploration of different private-message power allocations. Numerical results obtained via modeling and simulation verify the effectiveness of the proposed DQL algorithm, demonstrating that it concludes solutions that outperform existing approaches from the literature in the achieved energy efficiency. © 2023 IEEE.

关键词： Deep Reinforcement Learning (DRL) Energy Efficiency Maximization Rate-Splitting Multiple Access (RSMA)

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Cooperative Selection and communications in 6G Ultra-mMTC IoV Networks 4

Cooperative Selection and Communications in 6G Ultra-mMTC Io...

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4th International Symposium on Electrical, Electronics and Information engineering, ISEEIE 2024

作者： Huang, He Huang, Wei College of Communication Technology Chengdu China School of Computer Science and Information Engineering Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China

ISBN: (纸本)9798350355772

Although Cooperative communications (CC) already exists in 4G/5G, performance improvement is just not enough. As 5G standard has gradually established recently, CC has been one of most critical communication technologies which plays a founding role in 6G IoV networks. Therefore, it is important to apply collaborative technology to achieve Internet of Everything (IoV) and improve communication quality significantly in 6G IoV ultra-mMTC networks. In this study, we review that collaborative ideology has been regarded as foundation theory which widely exists in future multiple hybrid scenarios, such as, Cognitive Internet of Things (C-IoT) networks, UAVs (Unmanned Aerial Vehicles) communications, air-space-ground integrated networks, underwater acoustic communication and so on. Besides, we discuss key communication technologies that are closely combined with unique communication scenes, and elaborate critical problems that shall be solved in 6G ultra-mMTC IoV networks. Furthermore, we discuss extreme low complexity and fast ultra-massive relays selection algorithm in all sorts of future 6G scenarios. At last, it is concluded that for arbitrary two communication points (source/destination devices, sensors, relays, IoT nodes and so on), ideology of nodes selection of collaborative communication is theory of foundation to realize IoV and optimize communication transmission performance in future networks. © 2024 IEEE.

关键词： Cooperative communication

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A QoS-aware Handover Mechanism for LEO Satellite Networks Based on Multi-agent DRL 29

A QoS-aware Handover Mechanism for LEO Satellite Networks Ba...

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29th IEEE Asia Pacific Conference on communications, APCC 2024

作者： Liang, Chengchao Guo, Yihang Wu, Zhanglei Chai, Rong Carleton Univ Depart. of Systems and Computer Eng OttawaON Canada Chongqing Univ. Posts & Telecom School of Communication and Information Engineering Chongqing China

ISBN: (纸本)9798331517816

In future space-ground integrated networks, a satellite-based core network can reduce frequent signaling interactions between satellites and ground stations, thereby enhancing network architecture and supporting global communications. Users can achieve end-to-end communication through the satellitebased User Plane Function (UPF). However, the high dynamics of Low Earth Orbit (LEO) satellites result in frequent inter-satellite handovers, significantly affecting user service continuity. Existing satellite handover strategies are overly simplistic and fail to ensure the Quality of Service (QoS). Additionally, ground users compete for satellite links based on limited observations, leading to network congestion. This paper proposes a loadbalanced, distributed, multi-agent deep reinforcement learning method for satellite handover. We formulate a combinatorial optimization problem to maximize the total utility of user-satellite associations across various service types. Each user acts based on local information and engages in distributed matching with satellites. Simulation results indicate that our method ensures QoS for various service types, optimizes load balancing, and outperforms basic handover strategies in terms of handover success rate and frequency. © 2024 IEEE.

关键词： Satellite links

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Benchmarking Semantic communications for Image Transmission Over MIMO Interference Channels 16

Benchmarking Semantic Communications for Image Transmission ...

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16th International Conference on Wireless communications and Signal Processing, WCSP 2024

作者： Wang, Yanhu Guo, Shuaishuai Dong, Anming Zhao, Hui School of Control Science and Engineering Shandong University China School of Computer Science and Technology Qilu University of Technology China Eurecom Communication Systems Department France

ISBN: (纸本)9798350390643

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-tonoise (SNR) regimes. © 2024 IEEE.

关键词： Radio transceivers

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Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

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Journal of computer and communications 2024年第2期12卷 116-141页

作者： Marios Vlachos Evangelos Dermatas Institute of Communication and Computer Systems Athens Greece Department of Computer Engineering and Informatics University of Patras Patras Greece

Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

关键词： Finger Vein Vessel Enhancement Vessel Network Extraction Non-Uniform Images Binarization Morphological Post-Processing

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Deep Learning for Enhanced Education Quality: Assessing Student Engagement and Emotional States

Deep Learning for Enhanced Education Quality: Assessing Stud...

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2023 Innovations in Intelligent systems and Applications Conference, ASYU 2023

作者： Gazawy, Qusai Buyrukoglu, Selim Akbas, Ayhan Cankiri Karatekin University Department of Computer Engineering Cankiri Turkey University of Surrey Institute for Communication Systems Guildford United Kingdom

ISBN: (纸本)9798350306590

This paper presents a comprehensive study on the application of deep learning techniques in education to enhance instructional quality. While deep learning has been extensively explored in various domains, its potential for reporting and improving instruction in education remains largely untapped. Student engagement and interest are crucial factors in the learning process and future success. However, students often face challenges in maintaining active participation, hindering their learning experience and overall teaching effectiveness. Current research on utilizing deep learning for assessing student engagement in education is limited. To bridge this gap, this study introduces an innovative approach that employs deep learning models to monitor students' emotional states and interest levels in classes, providing valuable insights to instructors. The emotion detection model achieved an impressive accuracy of 92.16%, enabling accurate assessment of students' emotional responses during educational activities. Moreover, the face recognition model achieved a remarkable accuracy of 98.10%, facilitating reliable identification of students and tracking their presence and engagement. By leveraging these deep learning models, this study aims to establish a student-centered learning environment that enhances education quality. The findings contribute to effective student support and improvement of educational processes by harnessing the power of deep learning techniques in the field of education. © 2023 IEEE.

关键词： Students

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Analysis and Detection of Fake News Using Machine Learning 3

Analysis and Detection of Fake News Using Machine Learning

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sathish Kumar, P. Suthanthiradevi, P. Arul Stephen, C. Ebenezer Abishek, B. Sivakumar, S. Mathiyarasu, M. Computer and Communication Engineering Rajalakshmi institute of technology Chennai124 India Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai603203 India Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai117 India Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai600062 India Electronics and Communication Engineering BIHER Chennai73 India Computer and Communication Engineering Rajalakshmi institute of technology Chennai India

ISBN: (纸本)9798350372120

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society. © 2024 IEEE.

关键词： Machine learning

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Advanced CNN Architectures for Improved Garbage Image Classification: An In-depth Evaluation 9

Advanced CNN Architectures for Improved Garbage Image Classi...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Kanavos, Athanasios Papadimitriou, Orestis Vonitsanos, Gerasimos Maragoudakis, Manolis Mylonas, Phivos University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350342482

Convolutional Neural Networks (CNNs) have become instrumental in advancing image classification, particularly in the context of garbage image classification, a critical component for efficient waste management. This paper introduces a tailored CNN architecture that demonstrates enhanced accuracy in garbage classification tasks, even with constrained datasets. Our architecture incorporates multiple convolutional, max-pooling, and fully connected layers, with dropout regularization strategically applied to curb overfitting and improve model generalization across a varied waste image dataset. Comparative evaluations reveal that our model achieves a significant improvement in accuracy over existing CNN models. The results not only validate the robustness of our approach but also contribute valuable insights toward developing more precise and efficient systems for garbage image classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Autism Detection using InceptionV3 and Security using Blockchain 8

Autism Detection using InceptionV3 and Security using Blockc...

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8th International Conference on Electronics, communication and Aerospace Technology, ICECA 2024

作者： Annie Silviya, S.H. Seetha, R. Raja, S. Sriman, B. Rupesh, S. Gopala Krishnan, J. Rajalakshmi Institute of Technology Computer Science Engineering Chennai India Vellore Institute of Technology Computer Science Engineering and Information Systems Vellore India Panimalar Engineering College Computer Science and Engineering Chennai India Institute of Science and Technology Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&d Chennai India Rajalakshmi Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

The mental health and well-being of children are critical components of their overall development and future success. In India, only 1 in 6 8 children are diagnosed with autism, since monitoring and addressing the mental health issues among children is a challenge because of various factors. Studies have proven that autism detection can be made possible with the assessment of specific facial features that are used as primary markers for the proposed model. The proposed solution aims to leverage technological advancements, such as deep learning, for detecting autism in children using the aforementioned markers. The model gathers data on the children's facial features and predicts any underlying mental disorder. Since the application collects and analyzes sensitive information from the users, an additional security feature is implemented in the proposed solution using blockchain. Key features of the solution include standardized assessment tools, real-time tracking capabilities, personalized interventions, and seamless communication channels between parents, caregivers, and mental health professionals. © 2024 IEEE.

关键词： Diseases

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Advancing Sentiment Analysis of IMDB Movie Reviews with a Hybrid Multinomial Naive Bayes and LSTM Approach 4th

Advancing Sentiment Analysis of IMDB Movie Reviews with a...

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4th International Conference on Novel and Intelligent Digital systems, NiDS 2024

作者： Papadimitriou, Orestis Kanavos, Athanasios Vonitsanos, Gerasimos Maragoudakis, Manolis Mylonas, Phivos Department of Information and Communication Systems Engineering University of the Aegean Samos Greece Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Natural Language Processing (NLP) is increasingly pivotal in the natural sciences, with sentiment analysis emerging as a crucial application in the era of big data. Efficiently and accurately extracting meaningful insights from extensive textual data remains a significant challenge. This study explores sentiment analysis within the context of movie reviews, a domain where understanding nuanced viewer perceptions can influence industry outcomes. Traditional methods often struggle with the complexity of language, where identical phrases may convey different sentiments based on their context. To address these challenges, this research implements a hybrid approach, combining a Multinomial Naive Bayes Classifier and a Long Short-Term Memory (LSTM) model developed in TensorFlow. Our integrated methodology not only advances the robustness of sentiment analysis tools but also achieves a notable accuracy of 96.25%, underscoring its potential to enhance complex NLP tasks in real-world applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Natural language processing systems

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