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Trajectory Control and Fair Communications for Multi-UAV Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Tarekegn, Getaneh Berie Tesfaw, Belayneh Abebe Juang, Rong-Terng Saha, Dola Tarekegn, Robel Berie Lin, Hsin-Piao Tai, Li-Chia Nycu Department of Electrical and Computer Engineering 1001 University Road Hsinchu300 Taiwan Nycu Institute of Electrical and Control Engineering 1001 University Road Hsinchu300 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Institute of Aerospace and System Engineering Taipei10608 Taiwan University at Albany Suny Department of Electrical and Computer Engineering AlbanyNY12222 United States

Due to their highly flexible deployment and agility features, unmanned aerial vehicles (UAVs) serving as aerial base stations are increasingly being used in challenging environments, including emergency communication, traffic offloading, and failures of existing communications infrastructure. A reliable and effective communication service requires 3D deployment and autonomous UAV trajectory optimization in each time slot. While most prior approaches focus on trajectory design that maximizes communication coverage or network throughput without considering fairness as well as computation time. This paper presents a multi-UAV trajectory control and fair communication (TCFC) scheme that maximizes ground user data rate and communication coverage while ensuring service fairness in a UAV-aided wireless communications system. The proposed TCFC scheme employs a two-stage learning approach. Firstly, it develops a gated recurrent unit-based link quality estimation model to assess each user's link quality over time. Then, a federated multi-agent deep reinforcement learning (FedMADRL) algorithm is utilized to continuously adjust the trajectory of the UAVs, optimizing communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. The results show that the proposed TCFC scheme reduces computation time by 26.91% and provides comparable network performance with the baseline methods while improving DRL agents' privacy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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Collaborative Deadline-sensitive Multi-task Offloading in Vehicular-Cloud Networks

Collaborative Deadline-sensitive Multi-task Offloading in Ve...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Prasanna Kumar Sushma S A K Chandrasekaran Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka Surathkal India Cloud and Smart System Services Lab Siddaganga Institute of Technology Tumakuru

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

With the growing technological advancements in the Internet and advanced functionalities in vehicular networks, it becomes crucial to execute tasks quickly and efficiently. However, the limited onboard computational capacity and vehicle mobility make it challenging to accomplish latency-sensitive tasks efficiently. Task offloading provides a promising solution to overcome these challenges. Cloud data centers provide efficient solutions, but returning the results to the vehicles takes longer due to the large physical distance. Leveraging edge servers to execute latency-sensitive tasks provides a fast, interactive response and less transmission cost. However, in a dynamic network, vehicles will be in constant motion with varying speeds, resulting in frequent handoffs from one base station to another. Our proposed work aims to select the optimal nodes to perform binary offloading with minimum cost using the collaborative vehicular network. We use a greedy-based offloading approach to address these challenges and achieve better quality-of-service and quality-of-experience in a dynamic environment to minimize costs, delay reduction ratio, and satisfaction ratio. The proposed work outperforms the baseline by 60.44%, 53.43% in reducing total system cost, delay reduction ratio, and 36% improvement in the satisfaction ratio compared to baseline algorithms.

关键词： Costs Heuristic algorithms Dynamics Collaboration Quality of service Multitasking Minimization Delays Servers Vehicle dynamics

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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics

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Microsystems & Nanoengineering 2025年第1期11卷 465-476页

作者： Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad Center for Cyber-Physical Systems-System on Chip Lab Khalifa UniversityAbu Dhabi 127788UAE Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering Khalifa UniversityAbu Dhabi 127788UAE Division of Engineering New York UniversityAbu Dhabi 129188UAE Department of Mechanical and Nuclear Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Chemistry Khalifa UniversityAbu Dhabi 127788UAE

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing *** this study,we present an approach that integrates an iontronic fluidic memristive(IFM)device with low input impedance and a triboelectric nanogenerator(TENG)based on ferrofluid(FF),which has high input *** incorporating contact separation electromagnetic(EMG)signals with low input impedance into our FF TENG device,we enhance the FF TENG’s performance by increasing energy harvesting,thereby enabling the autonomous powering of IFM devices for self-powered ***,replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic *** fluidic devices,composed of soft-matter materials,dynamically adjust their conductance by altering the solution *** developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane(PDMS)structures,utilising a fluidic interface of FF and polyacrylic acid partial sodium salt(PAA Na^(+)).The confined ion interactions in this system induce hysteresis in ion transport across various frequencies,resulting in significant ion memory *** IFM successfully replicates diverse electric pulse patterns,making it highly suitable for neuromorphic ***,our system demonstrates synapse-like learning functions,storing and retrieving short-term(STM)and long-term memory(LTM).The fluidic memristor exhibits dynamic synapse-like features,making it a promising candidate for the hardware implementation of neural *** TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials,further enhanced by intricate chemical designs for self-powered electronics.

关键词： iontronic memristive ionic fluidic devices memcapacitive ff teng s ff teng fluidic triboelectric nanogenerator teng based

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

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IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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Efficient Deep Learning Models Revolutionize Doctor’s Training for Point-of-Care Ultrasound

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IEEE Access 2025年 13卷 76038-76046页

作者： Chou, Hsin-Hung Chang, Yi-Chung Lien, Wan-Ching Lin, Lung-Chun Lin, Xing-Zheng Hsu, Ting-En Liu, Yueh-Ping Liu, Li Chan, Yen-Ting Kuan, Feng-Sen National Chi Nan University Department of Computer Science and Engineering Nantou545301 Taiwan National Taiwan University College of Medicine Department of Emergency Medicine Taipei106319 Taiwan National Taiwan University Hospital Department of Emergency Medicine Taipei100225 Taiwan National Taiwan University Hospital Department of Internal Medicine Taipei100225 Taiwan Ministry of Health and Welfare Department of Medical Affairs Taipei115204 Taiwan Show Chwan Health Care System Taipei100225 Taiwan OmniHealth Group Inc. Department of Research Planning Taipei106409 Taiwan Huasin H. T. Ltd. Department of Business Development Taipei106409 Taiwan

Point-of-care ultrasound (PoCUS) is a valuable diagnostic tool for pericardial effusion (PCE). However, the time constraints for trainees and experts pose significant barriers to PoCUS learning. This study aims to develop a deep learning (DL) model for detecting and localizing PCE and to investigate the learning efficacy of using this model as an adjunct in PoCUS training. A total of 101 patients with moderate/large PCE and 104 controls without PCE were included, and the images were extracted from the ultrasound (US) clips. We applied preprocessing techniques, including standardized image sizes and background removal, to reduce interference, and post-processing techniques, including adding filters to refine small effusion regions. We developed three DL models based on U-Net, Res-UNet, and UNet++ and compared their performance. Additionally, 14 emergency medicine residents were recruited to complete classification and segmentation tasks on 10% of randomly selected US images. Personalized feedback from the best-performing DL model was provided. Three months later, the residents annotated another set of images. Their learning performance was also evaluated. The UNet++ algorithm surpassed the other two, attaining an impressive sensitivity of 96%, specificity of 97%, area under the curve (AUC) of 98%, intersection over union (IOU) of 81%, and minimal latency. The overall sensitivity increased by 4% in the classification task after training with the UNet++ model, although there were no statistically significant differences in all evaluation metrics. The UNet++ model achieved a balance between high accuracy, IOU, and latency. Although there were no significant differences in the evaluation metrics after UNet++-assisted learning, the overall sensitivity increased by 4%, indicating an improved ability to recognize true positives and reduce false negatives. Our results demonstrated that AI could enhance the interpretation of rare PoCUS conditions and reduce the time demands o

关键词： Personnel training

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A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer’s disease detection

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Cognitive Neurodynamics 2025年第1期19卷 1-19页

作者： Sercek, Ilknur Sampathila, Niranjana Tasci, Irem Ekmekyapar, Tuba Tasci, Burak Barua, Prabal Datta Baygin, Mehmet Dogan, Sengul Tuncer, Turker Tan, Ru-San Acharya, U.R. Department of Digital Forensics Engineering College of Technology Firat University Elazig Turkey Department of Biomedical Engineering Manipal Academy of Higher Education Manipal India Department of Neurology School of Medicine Firat University Elazig 23119 Turkey Department of Neurology Malatya Training and Research Hospital Malatya 44000 Turkey Vocational School of Technical Sciences Firat University Elazig 23119 Turkey School of Business (Information System) University of Southern Queensland Toowoomba Australia Department of Computer Engineering College of Engineering Erzurum Technical University Erzurum Turkey Department of Cardiology National Heart Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability. © The Author(s) 2025.

关键词： Alzheimer’s disease Brain-computer interface EEG GHPat Goldner-Harary graph Signal classification

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain Technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important computer Science, engineering, and Decision Sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural Science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Marching Page Walks: Batching and Concurrent Page Table Walks for Enhancing GPU Throughput

Marching Page Walks: Batching and Concurrent Page Table Walk...

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IEEE Symposium on High-Performance computer Architecture

作者： Jiwon Lee Gun Ko Myung Kuk Yoon Ipoom Jeong Yunho Oh Won Woo Ro Department of Electrical and Electronic Engineering Yonsei University Seoul Korea Department of Computer Science and Engineering Ewha Womans University Seoul Korea Department of System Semiconductor Engineering Yonsei University Seoul Korea School of Electrical Engineering Korea University Seoul Korea

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Virtual memory, with the support of address translation hardware, is a key technique in expanding programmability and memory management in GPUs. However, the nature of the GPU execution model heavily pressures its translation hardware, particularly due to a discrepancy in the behavior of page table walkers and thousands of concurrently running threads. In GPU workloads, multiple threads simultaneously access a number of pages necessitating a substantial number of translations whereas each walker handles only a single walk request at a time. Such a limitation significantly increases the queueing latency of walk requests, which we observe as a major bottleneck for servicing page table walks in GPUs. To tackle this challenge, we investigate a design of page walkers that facilitates multiple walk requests to be handled together in batches. Then, we make the following observations: 1) allowing a page walker to issue beyond a single memory request significantly improves the throughput of walkers, and 2) GPU applications tend to concurrently access pages in wide address ranges. By leveraging the above implications, we propose Marching Page Walks (MPW) that effectively mitigate the contention in GPU page table walkers. MPW scans pending walk requests to identify ones that can be grouped together. Then, MPW batches these requests and concurrently handles them by issuing multiple memory instructions. Experiments show that MPW reduces the queueing latency of page walks by 86.7% and improves GPU performance by 55.6% over the baseline design.

关键词： Translation Filters Instruction sets Memory management Graphics processing units Throughput Hardware Energy efficiency

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