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Self-Decoupled Square Patch Antenna Arrays by Exciting and Using Mixed Electric/Magnetic Coupling Between Adjacent Radiators

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Chinese Journal of Electronics 2025年第2期34卷 483-494页

作者： Liu Qianwen Zhu Lei Lu Wenjun College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau 999078 China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing 210003 China

This article presents and develops a simple decoupling method for the planar square patch antenna arrays by virtue of mixed electric and magnetic coupling property. Since the resonant modes of TM10 and TM01 are a pair of degenerate modes in the square patch radiator which are intrinsically orthogonal, a superposed mode of them can be generated to possess consistent field distributions along all the four sides of the patch by adjusting the feeding position. By employing such superposed mode, the mutual coupling between two horizontally adjacent patch elements will become identical to that between two vertical ones, indicating an expected possibility that the complex 2-D decoupling problem in a large-scale antenna patch array can be effectively facilitated and simplified to a 1-D one. Subsequently, metallic pins and connecting strip are properly loaded in each square patch resonator, such that appropriate electric and magnetic coupling strengths can be readily achieved and thus the mutual coupling can get highly decreased. A 1 × 2 antenna array with an edge-to-edge separation of 1 mm, which corresponding to 0.0117λ0, is firstly discussed, simulated, and fabricated. The measured results show that the isolation can be highly improved from 4 dB to 17 dB across the entire passband. In final, 1 × 3, 2 × 2, and 4 × 4 antenna array prototypes are constructed and studied for verification of the expansibility and feasibility of the proposed decoupling method to both linear and 2-D antenna arrays.

关键词： Large-scale antenna array Mutual coupling Mixed electric and magnetic coupling effect Self-decoupling Square patch antenna

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Combining Heuristic and Reinforcement Learning to Achieve the Low-latency and High-throughput Receiver-side Congestion Control

arXiv

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arXiv 2025年

作者： Jiang, Xianliang Gong, Guanghui Jin, Guang Faculty of Electrical Engineering and Computer Science of Ningbo University China Eastern Institute of Technology Ningbo China

Traditional congestion control algorithms struggle to maintain the consistent and satisfactory data transmission performance over time-varying networking condition. Simultaneously, as video traffic becomes dominant, the loose coupling between the DASH framework and TCP congestion control results in the un-matched bandwidth usage, thereby limiting video streaming performance. To address these issues, this paper proposes a receiver-driven congestion control framework named Nuwa. Nuwa deploys the congestion avoidance phase at the receiver-side, utilizing one-way queueing delay detection to monitor network congestion and setting specific target delays for different applications. Experimental results demonstrate that, in most cases, with appropriate parameter configuration, Nuwa can improve the throughput of TCP flows 4% to 15.4% and reduce average queueing delay by 6.9% to 29.4%. Furthermore, we also introduce the use of reinforcement learning to dynamically adjust Nuwa’s key parameter k, enhancing Nuwa’s adaptability to the unpredictable environment. © 2025, CC BY-NC-ND.

关键词： Video streaming

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Decoding Digital Dynamics: Attitude Algorithms in Online Gaming Redefining Communication Experiences

Decoding Digital Dynamics: Attitude Algorithms in Online Gam...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ghani, Miharaini Md Mustafa, Wan Azani Bin Hashim, Mohd Ekram Alhafis Hanafi, Hafizul Fahri Al-Farouni, Mohammed Universiti Sains Malaysia School of Communication Penang Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau Malaysia Universiti Pendidikan Sultan Idris Faculty of Art Sustainability and Creative Industry Department of Creative Multimedia Perak Tanjong Malim Malaysia Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Department of Computer Science and Digital Technology Perak Tanjong Malim Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350336474

The proliferation of online gaming has given rise to vast virtual worlds where communication and social interaction take on new dimensions. At the forefront of this digital frontier are "attitude algorithms"- advanced computational models designed to detect, interpret, and respond to the emotional states and behavioral patterns of players. These algorithms have the potential to fundamentally redefine the nature of communication experiences within online gaming environments. This study examines the impact of attitude algorithms on player interactions and social dynamics in popular multiplayer online games. Through a combination of data analysis, user surveys, and in-game observations, we explore how these algorithms shape how players express themselves, form connections, and navigate complex social landscapes within virtual gaming worlds. Our findings suggest that attitude algorithms introduce a new layer of emotional intelligence and responsiveness to online gaming communities. By tailoring in-game experiences based on players' attitudes and behaviors, these algorithms can foster more engaging and personalized forms of communication. However, we also uncover potential concerns regarding privacy, manipulation, and the erosion of authentic human-to-human interactions. Ultimately, this research sheds light on the profound impact of attitude algorithms, highlighting their capacity to redefine the boundaries of communication and social dynamics in the digital realm. As these technologies continue to evolve, it becomes increasingly important to understand their implications and guide their development in a responsible and ethical manner, ensuring that they enhance rather than diminish the richness of human connections in online gaming and beyond. © 2023 IEEE.

关键词： Dynamics

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Multi-View Human Activity Recognition in Ambient Assisted Living Using Lightweight Deep Learning Models

Multi-View Human Activity Recognition in Ambient Assisted Li...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Ahsanul Bari Hezerul Abdul Karim Fahmid Al Farid Mina Asaduzzaman Farshid Amirabdollahian Sarina Mansor Faculty of Engineering Multimedia University Cyberjaya Malaysia Faculty of Information Science and Technology Multimedia University Melaka Malaysia Faculty of Engineering and Computer Science the University of Hertfordshire Hatfield

ISBN: (数字)9798331529437

ISBN: (纸本)9798331529444

Human Activity Recognition (HAR) is crucial for the development of intelligent assistive technologies in Ambient Assisted Living (AAL) environments. This paper proposes an innovative method for Multi-View Human Activity Recognition (MV-HAR) using lightweight deep learning models, specifically MobileNet and Cyclone-CNN (CCNet), to achieve quick and precise activity detection. Utilizing the Robot House Multi-View Human Activity Recognition (RHM-HAR) dataset, which contains four different views-front, back, ceiling (omni), and mobile robot-our models effectively address challenges related to viewpoint variation and motion dynamics. The dataset includes 14 multi-view daily living action classes, providing a balanced set of synchronized human actions suitable for multi-domain neural network learning. MobileNet and CCNet are employed for their high recognition accuracy, computational efficiency, and real-time application capabilities in AAL scenarios. We propose a Mutual Information (MI)-based method to assess the redundancy and relevance of each viewpoint, ensuring the fusion of multi-view data with minimum redundancy and maximum relevance. Benchmarking results demonstrate that multi-view combinations significantly enhance recognition performance compared to single-view models, particularly in complex activities involving high levels of movement.

关键词： Deep learning Ambient assisted living Accuracy Computational modeling Termination of employment Assistive technologies Transformers Computational efficiency Human activity recognition Robots

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Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

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computer Systems Science & engineering 2023年第11期47卷 2187-2200页

作者： Ali Altalbe Abdul Rehman Javed Department of Computer Science Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data *** advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’*** user data is frequently used to train machine learning models for healthcare systems to track the health status of *** healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI *** paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain *** extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different *** lessen the over-fitting aspect,every client reviewed the outcomes in three *** proposed model identifies brain disorders without jeopardizing privacy and *** results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.

关键词： Privacy preservation brain disorder detection Parkinson’s disease diagnosis federated learning healthcare machine learning

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Determining Network Performance Through Bandwidth Optimization in Underwater Fiber Optic Communication 3

Determining Network Performance Through Bandwidth Optimizati...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Diwakar, Mandar P. Beemkumar, N. Yadav, Dhananjay Mishra, Varun Khandari, Harsimrat Divya, K. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Department of Mechanical Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

Information the overall performance of underwater fiber optic (UWFO) communique networks is vital for a hit deployment of this technology in an expansion of applications. That allows you to achieve most efficient community overall performance, bandwidth optimization must be hired. This paper examines distinctive bandwidth optimization techniques and how they're used to improve conversation among UWFO elements. The proposed approach utilizes adaptive modulation and coding techniques, as well as advanced error correction coding schemes, to efficiently utilize the available bandwidth and minimize signal degradation. Through extensive simulations and experiments, we demonstrate the effectiveness of our approach in improving network performance, including increased data transfer rates and reduced error rates, compared to traditional methods. Our findings have significant implications for the design and operation of underwater fiber optic communication systems, potentially enhancing their potential for a wide range of applications in marine environments. In the end, several destiny studies guidelines are diagnosed to in addition enhance the overall performance and reliability of UWFO conversation networks. © 2023 IEEE.

关键词： Communiqué Conversation Investigates Networks Optimization

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Classification of Malignant or Benign Cancer using Neural Networks

Classification of Malignant or Benign Cancer using Neural Ne...

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International Conference on Advances in Biomedical engineering (ICABME)

作者： Gaby Abou Haidar Roger Achkar David Semaan Michel Owayjan American University of Science and Technology Beirut Lebanon Department of Computer and Communications Engineering Faculty of Engineering and Computer Science

Cancer detection and predetection are being highly researched and studied recently due to the high density spread of this disease. It occurs when abnormal cells divide continuously without limitation where it can spread randomly to different parts of the body. Different types of cancer are recorded targeting the blood, lungs, bones, liver, and many other organs. People with cancer have similar symptoms such as lumps, abnormal bleeding, eating problems, fatigue, weight loss, and some more severe issues. Breast Cancer, which will be addressed in this work, is a type of cancer that hits women in general. It is dangerous especially when cells around the breast area overgrow and cause lumps on the breasts. In order to pre-detect and try to avoid breast cancer., it's important to perform annual mammography check. This work is aimed to create a classification system that uses neural network algorithms to conclude whether a certain tumor is a malignant or a benign tumor. Back Propagation algorithm as well as supervised learning were used and tested with highly satisfying successful classification.

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Deep Learning Scheme for Modulation Classification under Fading Channels 7

Deep Learning Scheme for Modulation Classification under Fad...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Ghauri, Sajjad A. Sarfraz, Mubashar Chhabra, Gunjan Kaushik, Keshav Zafar, Asim Khan, Inam Ullah Isra University School of Engineering & Applied Sciences Islamabad Pakistan Numl University Faculty of Engineering and Computing Islamabad Pakistan Graphic Era Hill University Graphic Era Deemed to be University Department of Cse Uttarakhand Dehradun India Amity University Amity School of Engineering and Technology Punjab India National University of Technology Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350350067

The field of automatic modulation classification using deep neural networks has undergone significant development in recent years. In this research, M-QAM and N-PSK modulation formats have been considered for classification. The deep neural network architecture is directly fed with raw signals that are often corrupted with channel noise and fading effects such as Rayleigh and Nakagami. The proposed classifier structure also incorporates normalized cumulants of higher order from the received signal. The proposed DNN architecture's performance is evaluated by comparing its efficiency for raw data and extracted features-based classification. We have validated the classifier efficiency through three different cases. Our proposed classifier demonstrates remarkable capability in efficiently classifying modulation formats. © 2024 IEEE.

关键词： Signal modulation

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Bistable Beam Splitting in Nonlinear Metagratings

Bistable Beam Splitting in Nonlinear Metagratings

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European Conference on Antennas and Propagation, EuCAP

作者： Naama Cohen Levi Ariel Epstein Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa Israel

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

We present an analytical model for the response of a beam-splitting metagrating (MG) comprised of thin wires loaded by nonlinear capacitors. Through rigorous formulation, we reveal that a bistable switching response may emerge in such a configuration, and identify analytically the parameters governing its formation. We show that by judicious choice of these structural and electromagnetic MG parameters, a distinct hysteresis loop may be observed, switching between high specular reflection to high split efficiency, depending on the input field intensity and history of excitation. These results, verified via full-wave simulations, lay the grounds for the development of MG-based electromagnetic switches for communication and analog computing, while establishing the foundations for analysis and synthesis of advanced nonlinear MGs in the future.

关键词： Varactors Analytical models Wires Scattering Switches Predictive models Reflection Electromagnetics Hysteresis Load modeling

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