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Transforming Time-Varying to Static Channels: The Power of Fluid Antenna Mobility

IEEE Transactions on Wireless Communications 2025年第6期24卷 4767-4780页

作者： Li, Weidong Yin, Haifan Fu, Fanpo Cao, Yandi Debbah, Merouane Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Khalifa University KU 6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction methods, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. Our analysis shows that for a multi-path channel with a strong line-of-sight (LoS) path, the prediction error of our proposed MPMP method nearly converges to zero, as the number of BS antennas and the port density of the FA are large enough. For a general multi-path channel, we also derive the upper and lower bounds of the prediction error when the number of paths is large enough. When the UEs move at a speed of 60 or 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction methods. © 2025 IEEE. All rights reserved

关键词： Channel estimation

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Energy Efficient Computation Offloading for DNN Tasks in Semantic-Assisted Mobile Edge Computing Systems 24

Energy Efficient Computation Offloading for DNN Tasks in Sem...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Zheng, Guangyuan Wen, Miaowen Tao, Yiwei Li, Qiang Ding, Zhiguo South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Jinan University College of Information Science and Technology Guangzhou510632 China

ISBN: (纸本)9798350363760

In this paper, we focus on semantic-aware computation offloading for executing the deep neural network (DNN) inference tasks in a mobile edge computing (MEC) system. To cope with the challenges of insufficient wireless resources during task offloading, we propose a compress-then-offload mode, in which each user first compresses its raw large-size task data into a small-size semantic feature and then uploads it to the edge server for remote execution. Specifically, we establish the relationship between the compression ratio and computation ratio for different DNN tasks. To achieve energy-efficient offloading, we formulate an optimization problem to minimize the energy consumption of all users by jointly optimizing the compression ratio, computation allocation, uploading time, and DNN layer selection. We propose a low-complexity iterative algorithm to solve the original non-convex problem. Simulation results demonstrate that our proposed semantic-aware computation offloading scheme can substantially reduce users' energy consumption compared to the conventional offloading, especially with scarce wireless resources. © 2024 IEEE.

关键词： Computation offloading

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DyFormer: A CNN-Transformer Network Combining Local and Global Attention for Optical Remote Sensing Object Detection 4

DyFormer: A CNN-Transformer Network Combining Local and Glob...

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4th International Conference on Artificial Intelligence, Virtual Reality and Visualization, AIVRV 2024

作者： Huang, Youxiang Wang, Qin Jiao, Donglai Wang, Shufei Wang, Yu Lu, Zhiyi Ohtsuki, Tomoaki Gui, Guan College of Telecommunications and Information Engineering Njupt Nanjing China Ai Research Center Nanjing Great Information Technology Co. Ltd Nanjing China Keio University Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9798331528744

Remote sensing image (RSI) object detection is crucial for natural disaster management, urban planning, and resource exploration. However, due to significant differences between RSIs and natural images (NIs), existing object detectors designed for NIs are not directly applicable to RSIs. CNN-based models typically require specialized attention modules to relate small targets to global contexts, while Transformer-based models often add modules to enhance detail extraction, increasing computational costs for edge deployment. To address these challenges, we propose DyFormer, a hybrid CNN-Transformer model that combines local and global attention mechanisms for effective feature fusion. Additionally, a feature residual pyramid network (FRPN) is introduced to enhance multi-scale feature integration. We conduct experiments on the open source NWPU VHR-10 dataset to evaluate the proposed DyFormer, achieving a mAP@0.5 of 61.7%, outperforms existing sort-of-the-art methods, demonstrating its effectiveness for RSI object detection tasks. © 2024 IEEE.

关键词： Optical remote sensing

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SwinGALE: fusion of swin transformer and attention mechanism for GAN-augmented liver tumor classification with enhanced deep learning

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International Journal of information technology (Singapore) 2024年第8期16卷 5351-5369页

作者： Bandaru, Sumash Chandra Mohan, G. Bharathi Kumar, R. Prasanna Altalbe, Ali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Engineering Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

Liver diseases represent a significant challenge to global healthcare systems, necessitating accurate and timely diagnosis for effective intervention. However, the intricate nature of liver tumor multi-classification remains a daunting obstacle. In this work, we provide a novel framework that integrates state-of-the-art technologies, including Generative Adversarial Networks (GANs), Convolutional Block Attention Module (CBAM), and Enhanced Channel Attention (ECA), within a deep learning architecture. Leveraging the comprehensive Duke Liver dataset, our approach synthesizes GAN-generated data to augment the training dataset and employs attention mechanisms to discern crucial details within medical images. Our ensemble model, incorporating CBAM with VGG19, achieves a remarkable accuracy of 99.29% in liver tumor classification. This research heralds a significant advancement in liver disease diagnosis, offering a promising avenue to improve patient outcomes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Attention mechanisms DCGAN ECA Liver tumour Swin transformer and feature fusion VGG19

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An Empirical and Comparative Study of Graph based Summarization Algorithms 1

An Empirical and Comparative Study of Graph based Summarizat...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Rautaray, Jyotirmayee Panigrahi, Sangram Nayak, Ajit Department of Computer Science and Engineering Bhubaneswar India Department of Computer Science and Information Technology Bhubaneswarline India

ISBN: (纸本)9781665454933

Textual content is growing exponentially on the Internet, and it is also available in the form of countless archives of news articles, scientific studies, legal papers, and other materials. To summarize a text by hand takes a considerable time, a lot of effort, and a great deal of money. As a result, the use of automated text summarization (ATS) or summarization is becoming more popular. Researchers have been working on developing ATS techniques since the 1950s. Text summarization is a branch of natural language processing (NLP) study areas that uses various NLP tools and techniques to provide us with a coherent and concise explanation of large text documents. Extractive, abstractive, and hybrid approaches are used to summarize text. The TextRank, LexRank, and Modified TextRank algorithms are compared in this article. These algorithms are based on graph-based extractive approach in which sentences are represented by vertices and the cosine similarity serves as edge between two sentences. The BBC and CNN news datasets has been used for this study. © 2022 IEEE.

关键词： Natural language processing systems

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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A Secure Communication Protocol for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第1期70卷 601-618页

作者： Navid Ali Khan N.Z.Jhanjhi Sarfraz Nawaz Brohi Abdulwahab Ali Almazroi Abdulaleem Ali Almazroi School of Computer Science and Engineering(SCE) Taylor’s University Lake-side CampusSubang Jaya47500Malaysia School of Information and Technology Monash UniversityMalaysia University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia Department of Computer Science Rafha Community CollegeNorthern Border UniversityArar91431Saudi Arabia

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial *** UAVs and autopilot systems support it,and it provides bi-directional communication between the UAV and Ground Control *** communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to *** order to increase the transfer speed and efficiency,the Mavlink does not encrypt the *** a result,the protocol is vulnerable to various security attacks such as Eavesdropping,GPS Spoofing,and *** this study,we tackle the problem and secure the Mavlink communication *** leveraging the Mavlink packet’s vulnerabilities,this research work introduces an experiment in which,first,the Mavlink packets are compromised in terms of security requirements based on our threat *** results show that the protocol is insecure and the attacks carried out are *** overcomeMavlink security,an additional security layer is added to encrypt and secure the *** encryption technique is proposed that makes the communication between the UAV and GCS *** results show that the Mavlink packets are encrypted using our technique without affecting the performance and *** results are validated in terms of transfer speed,performance,and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink *** achieved results have significant improvement over the literature and Mavlink in terms of security.

关键词： Unmanned aerial vehicles mavlink protocol drones security UAVs communication

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Digital Learning Ecosystem: Online Concept for engineering Education 65

Digital Learning Ecosystem: Online Concept for Engineering E...

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65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Zabasta, Anatolijs Kazymyr, Volodymyr Kunicina, Nadezhda Velihorskyi, Oleksandr Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia Chernihiv Polytechnic National University Information and Computer Systems Department Chernihiv Ukraine Chernihiv Polytechnic National University Radio Engineering and Embedded Systems Department Chernihiv Ukraine

ISBN: (纸本)9798350365771

This article examines the development of a Digital Learning Ecosystem (DLE) for engineering education, supported by online learning, within the framework of Erasmus+ international educational projects. We hypothesize that a DLE can address the challenges posed by rapid technological changes associated with Industry 4.0 and digital transformation, thus transforming engineering education to meet modern industry needs. Building on the earlier Erasmus+ CybPhys project, which established a Sharing Modelling and Simulation Environment (SMSE) for digitalizing online technical courses, the DIGITRANS project aims to enhance this architecture to include remote laboratories through the Sharing Remote Experiment Environment (SREE) platform. The project involves acquiring hardware and open-source software for SREE and establishing two physical remote laboratories. A study conducted in spring 2023, using the technology Acceptance Model framework, indicated positive student perceptions of SMSE, with students believing it would enhance learning outcomes and expressing intentions to use the platform frequently. The efficacy of the DLE in addressing technological advancement challenges will be further evaluated through post-ante assessments and feedback from prospective employers and stakeholders, focusing on the implementation of industry recommendations and the correlation between pre- and post-implementation survey responses from industry and academic experts. © 2024 IEEE.

关键词： Open source software

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IoT-Enabled Secure Energy Management System in Smart Meters with CRFN: A Novel Approach to Billing and Theft Prevention

IoT-Enabled Secure Energy Management System in Smart Meters ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, R. Punitha, S. Subalakshmi, N. Kavitha, K. Jeyakarthic, M. Raak College of Engineering and Technology Department of Computer Science and Engineering Puducherry India D. G. Govt. Arts College for Women Department of Computer Science Tamilnadu Mayiladuthurai India Annamalai University Department of Computer and Information Science Tamilnadu Chidambaram India Annamalai University Department of Electrical & Electronics Engineering Chidambaram India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

In response to the challenges posed by missed electricity bill payments and unauthorized power consumption, this study introduces enhancements to existing smart energy meter systems. The proposed improvements include the integration of advanced security features and automation capabilities, such as refined theft control mechanisms and optimized communication protocols between energy providers and consumers. Leveraging IoT technology, the system enables real-time energy monitoring, automatic billing, and timely payment reminders via SMS. Additionally, innovative theft prevention measures, including automated power disconnection for overdue accounts and theft detection systems, bolster security and reduce losses. The integration of the Convolutive Recurrent Fusion Network (CRFN) algorithm significantly enhances the system's theft detection and prevention capabilities. The CRFN detects anomalies in power consumption that may indicate theft, such as sudden spikes or drops in usage, through advanced pattern recognition. Upon detecting potential theft, the system can trigger automated responses like alerts or power disconnection, reducing the need for manual intervention. The incorporation of a magnetic reed switch further enhances efficiency by automatically controlling power access for consumers who have not paid their bills by the due date. By addressing these challenges and integrating advancements, the proposed framework aims to create a more efficient, secure, and sustainable energy management solution, ensuring accurate billing, preventing unauthorized usage, and promoting environmental responsibility. © 2024 IEEE.

关键词： Reed relays

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Hybrid convolutional neural network (CNN) for Kennedy Space Center hyperspectral image

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Aerospace Systems 2023年第1期6卷 71-78页

作者： Anand, R. Khan, Bilal Nassa, Vinay Kumar Pandey, Digvijay Dhabliya, Dharmesh Pandey, Binay Kumar Dadheech, Pankaj Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science University of Bradford Bradford United Kingdom CSE Department Rajarambapu Institute of Technology Rajaramnagar Maharshtra Uran Islampur India Department of Technical Education IET Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh Lucknow226021 India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttarakhand Udham Singh Nagar India Rajasthan Jaipur India

The upgradation in Deep learning has enabled us to use the advantage of classification techniques into hyperspectral image for analysis of variety of image bands. In recent works, convolutional neural network (CNN) are predominantly used for visual processing of data and its classification. Hyperspectral image classification deals with both spatial and spectral information extracted from the image datasets. 3-D CNN is sparsely used due to their complexity in computation. However, it has the capability to facilitate both spatial and spectral data from the spectrum of remotely sensed images. Usage of 2-D CNN includes only the spatial information and cannot be feasible for classification. The proposed model includes 3-D CNN stacked with 2-D CNN reducing the complexity. This hybrid model validated for the remote sensing datasets of Indian Pines (IP), Pavia University (PU), Salinas (SA) and Kennedy Space Canter (KSC). The conventional principal component analysis (PCA) is applied to remove the spectral redundancy over the HSI datasets. The model incorporates both 2-D and 3-D CNN, collectively called the Hybrid Spectral CNN. It comprises of three 3-D convolutional layers and one 2-D convolution layer with 3FC (fully connected) layers. The results of the Hybrid model are compared with the 2-D CNN and 3-D CNN models. Several statistical tests like F1-score, Recall, Precision etc., are also computed. All these experiments show that the performance of hyperspectral image classification is improved efficiently with this framework. © 2022, Shanghai Jiao Tong University.

关键词： Principal component analysis

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