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Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna Federated Learning 21

Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna...

引用

21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

ISBN: (纸本)9783903176553

We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and uplink over-the-air aggregation, we derive the global model update expression over communication rounds. We then obtain an upper bound on the expected global loss function, capturing the downlink and uplink beamforming and receiver noise effect. We propose a low-complexity joint beamforming algorithm to minimize this upper bound, which employs alternating optimization to breakdown the problem into three subproblems, each solved via closed-form gradient updates. Simulation under practical wireless system setup shows that our proposed joint beamforming design solution substantially outperforms the conventional separate-link design approach and nearly attains the performance of ideal FL with error-free communication links. © 2023 IFIP.

关键词： Beamforming

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YOLOv8 for Bangla License Plate Recognition: Advancing Real-Time Object Detection in Localized Contexts 7

YOLOv8 for Bangla License Plate Recognition: Advancing Real-...

引用

7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Saha, Utsha Ahamed, Imam Uddin Hossain, M. Iffat North Dakota State University Dept. of Computer Science FargoND United States Liverpool John Moores University School of Computer Science and Mathematics Liverpool United Kingdom University of Central Florida Dept. of Electrical and Computer Engineering OrlandoFL United States

ISBN: (纸本)9798350375886

Automatic License Plate Recognition (ALPR) systems play a crucial role in traffic management and security applications, and the development of accurate and efficient ALPR models is essential for their success. This study investigates the application of YOLOv8, a state-of-the-art object detection architecture, for recognizing Bangladeshi license plates. A comprehensive dataset, combining images from various vehicle categories and environments, was curated and augmented to train five variants of the YOLOv8 model: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv81, and YOLOv8x. The models were rigorously evaluated using metrics such as mean Average Precision (mAP), precision, recall, and F 1 -score. The results demonstrate the effectiveness of YOLOv8 in detecting and recognizing Bangladeshi license plates, with YOLOv8x achieving the highest mAP50 and mAP5095 scores of 0.96 and 0.75, respectively. The study highlights the adaptability of YOLOv8 to the unique characteristics of Bangladeshi license plates and provides insights into model selection criteria based on computational resources and performance requirements. Future research directions include expanding the dataset to cover a wider range of environments and integrating the YOLOv8 models into a comprehensive ALPR system for enhanced traffic management and security applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Ethogram-Based Personalization of Human Activity and Agility from Radar μ D Signatures

Ethogram-Based Personalization of Human Activity and Agility...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Kurtoglu, Emre Salehin, Sultanus Amin, Moeness G. Kan, Irene P. McKay, Michelle A. Gurbuz, Sevgi Z. The University of Alabama Dept. of Electrical and Computer Engineering United States Villanova University Dept. of Electrical and Computer Engineering United States Villanova University Dept. of Psychological and Brain Sciences United States College of Nursing Villanova University United States

ISBN: (纸本)9798350351552

Radar has garnered great interest for remote health monitoring due to its ambient operation, effectiveness in the dark, and inability to make visual recordings of private scenes/faces. However, the current state-of-the-art in human activity recognition (HAR) focuses on the classification of persistent gaits, such as walking, and ignores the transitions between activities. The characterization of a person's ability to transition between postural states is highly individual and influenced by the person's physical and mental health. This paper presents a personalized, ethogram-based approach to HAR, which jointly characterizes the agility of transitions in addition to activity classification. We develop a multi-input multi-task learning (MIMTL) approach to simultaneously classify both human activity and agility. Our proposed approach yields accuracies of over 98% and 90% for the joint characterization tasks. Various interventions affecting gait are applied to show how the proposed approach can lead to agility-based detection of changes in gait. © 2024 IEEE.

关键词： Electronic health record

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Entropy-weighted Voting Method for Diffusion-based Semantic Segmentation 14

Entropy-weighted Voting Method for Diffusion-based Semantic ...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Jeong, Seonggyun Heo, Yong Seok Ajou University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Ajou University Dept. of Electrical and Computer Engineering Dept. of Artificial Intelligence Suwon Korea Republic of

ISBN: (纸本)9798350313277

Recently, semantic segmentation methods leveraging image generation models have garnered significant attention. In particular, approach based on Diffusion models (DDPM) that utilize mid-level activations from the diffusion network with a majority voting of distributions from several light multi-layer perceptron (MLP) have shown better performance compared to GAN-based approaches. However, utilizing a simple majority voting system is suboptimal. In this paper, we propose a novel voting method for DDPM-based semantic segmentation. Our method introduces a weighted sum of distributions, where the weights are determined by the entropy of the class prediction results obtained from each MLP model. We conduct experiments on various datasets, including LSUN-Bedroom, FFHQ-256, LSUN-Cat, and LSUN-Horse. The results demonstrate that our proposed method achieves better mean Intersection over Union (mIoU) scores compared to previous work. © 2023 IEEE.

关键词： diffusion entropy semantic segmentation voting

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Emotion-Aware Fake News Detection on Social Media with BERT Embeddings

Emotion-Aware Fake News Detection on Social Media with BERT ...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Al-Alshaqi, Mohammed Rawat, Danda B. Liu, Chunmei Howard University Dept. of Electrical Engineering and Computer Science WashingtonDC United States

ISBN: (纸本)9798350369359

Fake news dissemination on social media poses a significant threat to the integrity of information and public discourse. This research proposes an emotion-aware fake news detection model using BERT embeddings. Leveraging the power of BERT, our model captures contextual relations in text, enabling accurate classification of fake news. Through experimentation with different BERT models, 'bert-large-cased' emerges as the top-performing variant, achieving a remarkable training accuracy of 98% and an F1 score of 0.77. Integrating emotion-aware features enhances the model's efficacy in identifying fake news while minimizing false positives and negatives. Our study contributes to the field of fake news detection, offering a potent tool for safeguarding social media from disinformation. © 2023 IEEE.

关键词： Embeddings

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Voltage Sag Investigation for Induction Motor Startup in Distribution Systems using Solid State Transformer 15

Voltage Sag Investigation for Induction Motor Startup in Dis...

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15th International Renewable Energy Congress, IREC 2025

作者： Ghambirlou, Khaled Abosnina, Adel Ali Moschopoulos, Gerry Western University Dept. Electrical and Computer Engineering London Canada College of Electrical and Electronics Technology Benghazi Libya

ISBN: (纸本)9798331516772

Dealing with voltage sags during motor startup is crucial because they can delay power restoration and lead to widespread failures for industrial loads and distribution systems. A novel approach is introduced to investigate voltage sag in distribution networks using solid-state transformers (SSTs) instead of traditional ones. The induction motor startup model is employed to simulate voltage sags, accurately representing the characteristics influenced by conventional industrial loads. The inherent capabilities of SSTs are leveraged to mitigate these voltage sag issues effectively. This study focuses on analyzing voltage sags caused by the activation of induction motors in power systems, with particular emphasis on SST technology, which actively regulates voltage during motor starting by controlling reactive power. The selected distribution system is implemented and tested using MATLAB/Simulink. The results will be compared with previous research on voltage sag during motor startup, demonstrating the improvements and effectiveness of using SSTs in managing power quality issues. This study emphasizes the potential of SSTs to improve the stability and reliability of power systems during the challenging conditions of motor startup. © 2025 IEEE.

关键词： Distribution transformers

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An Efficient Natural Merge Sort Incorporating Array Decomposition 26

An Efficient Natural Merge Sort Incorporating Array Decompos...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Gharami, Kanchon Rashid, Kazi Mehrab Rabbi, Golam Amin, Farhana Shatabda, Swakkhar Shovan, S.M. Mehedi Hasan, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh United International University Dept. of Computer Science & Engineering Dhaka Bangladesh Missouri University of Science & Technology Dept. of Computer Science & Engineering Missouri United States

ISBN: (纸本)9798350359015

Modifying an algorithm that has been established over many years and making it even faster has always been a fascinating and challenging area in the field of algorithms, which motivated us to take the challenge of improving the performance of Knuth's NaturalMergeSort by reducing the runtime with considering both ascending and descending runs. The way we optimize it is by taking advantage of both ascending and descending runs, i.e., increasing the potential of the decomposition method compared to the existing algorithm. The proposed algorithm was implemented in C++, and the experiment was conducted with some random and manually prepared datasets that resulted in improving the worst case of NaturalMergeSort by an exceedingly large margin of 97.5%, demonstrating the efficiency and flexibility of our algorithm. Even for the average case, our proposed algorithm beats Knuth's NaturalMergeSort by a slight margin, and it also outperforms traditional merge sort with 17.5% improvements. The performance and efficiency of our algorithm have been recorded and presented in graphical form by comparing time and space complexity with other competitor sorting algorithms. © 2023 IEEE.

关键词： MergeSort NaturalMergeSort Sorting Sorting Algorithm

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ElHa Net: A Pattern Recognition Neural Network 8

ElHa Net: A Pattern Recognition Neural Network

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Babiker, Elsadig Ahmed Mohamed Adlan, Hanan Hassan Ali Prince Sattam Bin Abdulaziz University College of Computer Engineering and Science Dept. of Computer Science Al-Kharj Saudi Arabia Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Dept. of Computer Science Riyadh Saudi Arabia Dept. of Computer Science Faculty of Mathematical Sciences United Kingdom

ISBN: (纸本)9798331540104

Neural Networks are the state-of-the-art models that derive intelligent systems. Generalization of the neural network model is a judge of how well an architecture mimic human intelligence. Self-extraction neural networks become the dominant neural networks, especially in autonomous systems such as intelligent robotics, autonomous vehicles, ṫ etc. This paper presents a neural network based on distance measure, ElHaNet Neural Network. ElHaNet network is a self-extraction neural network and is capable of pattern recognition. ElHaNet architecture composed of an extraction phase and a classification phase. Neurons in the extraction network capture patterns in the presented input. The classification network performs on the input patterns to be classified according to the problem in hand. The developed architecture is bench marked against well-known architectures for hand written digit recognition. The performance of the network is found to compete favorably with state-of-the-art models in the literature. © 2024 IEEE.

关键词： Neural network models

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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2024 International Conference on electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Cardiovascular Disease Prediction Utilizing Machine Learning and Feature Selection with Clonal Selection Algorithm 5

Cardiovascular Disease Prediction Utilizing Machine Learning...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rahman, Wahidur Aneek, Riasaad Haque Moinuddin, Md. Sakib, Md. Shadman Iqbal, Md. Sadiq Rahman, Mohammad Motiur Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Z.H. Sikder University of Science and Technology Dept. of Computer Science and Engineering Bhedarganj Shariatpur Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Mawlana Bhashani Science and Technology University Dept. of Computer Science and Engineering Tangail Bangladesh

ISBN: (纸本)9798350394290

A substantial and rising number of patients suffer from cardiovascular diseases, including heart attacks, heart failure, and other related illnesses. This case surge places increasing pressure on healthcare professionals and administrators while patients grapple with growing medical costs. To address these challenges, an automated system is necessary within the healthcare sector. In this paper, we present a cardiovascular health monitoring system that incorporates Machine Learning techniques. The study employed feature selection techniques on a secondary dataset. The study has used a nature-inspired technique, namely the Clonal Selection Algorithm (CSA) and Maximum Relevance Minimum Redundancy (mRMR) technique, to identify the most prominent feature in the detection of cardio-vascular illness. This approach was combined with a collection of Machine Learning classifiers. A group of experimental data has been listed to assess the suggested model's effectiveness. The research findings indicated a maximum accuracy rate of 100% when employing the proposed algorithms and orientations. The study also discusses the performance analysis for CSA and mRMR using a set of performance evaluation matrices. Based on the obtained results, it can be inferred that the suggested model will likely exhibit a high level of effectiveness in identifying cardiovascular diseases. © 2023 IEEE.

关键词： Feature Selection

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