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arXiv 2024年

作者： Liu, Saichao Sun, Geng Li, Jiahui Liang, Shuang Wu, Qingqing Wang, Pengfei Niyato, Dusit The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Information Science and Technology Northeast Normal University Changchun130117 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Dalian University of Technology Dalian116024 China

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Palmprint ROI Cropping Based on Enhanced Correlation Coefficient Maximisation Algorithm

Palmprint ROI Cropping Based on Enhanced Correlation Coeffic...

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Khalid, Noor Aldeen A. Ahmad, Muhammad Imran Mandeel, Thulfiqar H. Isa, Mohd Nazrin Md Ahmad, Raja Abdullah Raja Al-Dabagh, Mustafa Zuhaer Nayef Universiti Malaysia Perlis Faculty of Electronic Engineering Tecnology Arau Perlis02600 Malaysia College of Information Engineering Technology Imam Ja'afar Al-Sadiq University Al Muthanna Samawah Iraq College of Science Knowledge University Department of Computer Science Erbil Iraq

ISBN: (纸本)9781665437660

This paper proposes new technique to extract the Region of Interest (ROI) of palmprint biometric image while removing the distortion between images such as translation or rotation during ROI extraction. A similarity measure known as Enhanced Correlation Coefficient (ECC) is used in the proposed approach for better ROI extraction and image alignment, which helps to evaluate and determine the distortion. The objective of image alignment approaches are to find the deformation or transformation that minimizes the incongruities between images. After applying ECC algorithm the Region of Interest (ROI) is extracted from the palmprint by using moore neighbors algorithm, on the other hand, to verify and validate the efficacy of the recommended method the PolyU palmprint dataset II was used. The results show the high accuracy is 99.8% in deriving the ROI and developing a robust ROI cropping system successfully. © 2021 IEEE.

关键词： Correlation coefficient Databases Biometrics (access control) Conferences Distortion Systems engineering and theory Distortion measurement

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Cognitive Radio Networks: An AI Enabled Approach

Cognitive Radio Networks: An AI Enabled Approach

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： S. Prema Gautam Solaimalai S. Lalithambikai S. Gayathri Priya Sowmya PD Sathish Thanikodi Department of Information Technology Mahendra Engineering College Namakkal Tamil Nadu India U.S. Bank Georgia United States of America Department of Information Technology Knowledge Institute of Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Tamil Nadu India Department of Computer Science & Applications REVA University Bengaluru Karnataka India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

As the need for data transfer in wireless networks grows, cognitive radios are likely to meet a large part of that need. Cognitive radio nodes can sense what's going on around them, figure out what's best for using the radio spectrum by allocating resources in terms of time, frequency, and space, and then act on that information. AI and machine learning are often combined with cognitive radio to create smart and flexible sharing, which makes real-time operations more effective. This research talks about cognitive radio networks and their goals, problems, resources, and limits. There is a research into how learning affects cognitive radios and methods for AI and machine learning are shown. This section gives an outline of the newest research on how machine learning can be used in cognitive radios. A number of AI techniques are used to organize the literature review, such as neural networks, fuzzy logic, case-based reasoning, entropy, Bayesian analysis, Markov models, multi-agent systems, and artificial bee colony algorithms. Cognitive radio uses and possible learning barriers are also talked about.

关键词： Fuzzy logic Time-frequency analysis Wireless networks Neural networks Machine learning Real-time systems Mathematical models

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Deep learning based DeepFake video detection

Deep learning based DeepFake video detection

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Smart Computing and Application (ICSCA), International Conference on

作者： Sarra Guefrachi Marwa Ben Jabra Naif A. Alsharabi Mohamed Tahar Ben Othman Yasser O. Alharabi Abdulsalam Alkholidi Habib Hammam Faculty of Engineering Université de Moncton) Moncton NB Canada Spectrum of Knowledge Production & Skills Development Sfax Tunisia Computer Engineering University of Ha'il Computer Science Amran University Yemen Ha'il KSA Department of Computer Science College of Computer) Qassim University Buraydah Saudi Arabia line Computer Science and Information College of computer sc. & engneering Ha'il University Ha'il KSA College Department of Software Engineering Canadian Institute of Technology Tirana Canada International Institute of Technology and Management Commune d'Akanda Libreville BP Ga-bon Dept of Electrical and Electronic Eng. Science School of Electrical Engineering University of Johannes-burg Tirana Canada

Recent advances in DeepFake face-swapping technology have made it simple to create fake videos that appear remarkably real. Since it has been employed in numerous instances for deceit, extortion, and the falsification of facts, its widespread use has generated a huge social, security, and political risk. Its use on websites and social media has become more widespread. Detecting this crime is becoming more and more important due to the potential harm false videos may inflict on a global scale. This research offers a method for building a deep learning model that really can tell the difference between authentic and false videos. The article describes how to create new models based on the VGG16 neural network, a previously created neural network that does image categorization, using transfer learning in the computer vision field. Deep learning is still becoming better at both producing and spotting DeepFakes. DeepFake detection algorithms are developed using dated public datasets, and as a result, they may become obsolete with time. and require continual updating. The research findings are encouraging, and our results reached an accuracy rate of over 90%.

关键词： Deep learning Deepfakes computer vision Social networking (online) Computational modeling Neural networks Transfer learning

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Robust Visual Odometry Using Semantic Information in Complex Dynamic Scenes 5th

Robust Visual Odometry Using Semantic Information in Complex...

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Wang, Hao Wang, Le Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230009 China Department of Computer Science and Technology Hefei University of Technology Hefei Anhui230009 China

ISBN: (纸本)9789811623356

Traditional vision-based simultaneous localization and mapping (SLAM) technology cannot obtain the semantic information of the surrounding environment, which will cause the robot to fail to complete intelligent grasping, human-computer interaction, and other advanced decision tasks. There are many challenges to solve this problem, such as obtaining less semantic information of the environment with low precision and being unable to deal with dynamic objects in a real environment quickly and effectively. In this paper, a robust visual odometry using semantic information in complex dynamic scenes was designed. Specifically, we present a refined instance segmentation method based on the contextual information of the frame to improve the accuracy of segmentation. On this basis, a feature detection and elimination algorithm for dynamic objects based on instance-level semantic information is proposed to improve the localization accuracy of camera pose. We extensively evaluate our system on the public TUM data set and compare it with ORB-SLAM2 and other methods. Experiments show that our methods greatly improve the localization accuracy of the camera pose and the robustness of the system, which verifies that our system is effective in complex dynamic scenes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cameras

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Developing Robust Cybersecurity Policies and Governance Frameworks in Response to Evolving Legal and Regulatory Landscapes

Developing Robust Cybersecurity Policies and Governance Fram...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Radhika Pandey Shaik Anjimoon V Asha Atul Singla Irfan Khan Zain Alabdeen Hasan Abed DoCSE GyanGanga Institute of Technology and Science Jabalpur M.P India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This research introduces a comprehensive cybersecurity framework consisting of five interlinked algorithms: Threat Intelligence Integration, Risk Assessment and Management, Compliance Mapping, Incident Response Planning, and Employee Training and Awareness. Through comparative performance evaluation and ablation study, the framework demonstrates consistent superiority across key parameters. Visual representations, including bar charts, line charts, and pie charts, provide intuitive insights into effectiveness, adaptability, compliance, and overall performance. The ablation study underscores the synergistic importance of each algorithm, emphasizing the framework’s holistic nature. This research contributes to advancing cybersecurity methodologies, offering a dynamic and adaptive approach to safeguarding against evolving cyber threats.

关键词： Training Performance evaluation Visualization Technological innovation Law Heuristic algorithms Planning

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Intelligent Gates Exploring the Massiveness of Random Access Points in NGMA

Intelligent Gates Exploring the Massiveness of Random Access...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sunil Prashanth Kumar P. Aswini H Pal Thethi K Smitha R. Akhilesh Reddy Ali Albawi Department of Mechanical Engineeering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

With the rapid progress of Next-Generation Mobile Architecture come several obstacles in improving user experience and network performance. Active methods frequently face issues in allocating network resources correctly. Consequently this can lead to mismanagement or poor resource utilization affecting how users experience the network. As access requirements increase with the growth of architecture complexity protecting personal data is becoming more challenging. To tackle these issues the Dynamic Access Point Management (DAPM) framework shows great potential as it delivers a creative strategy for resource allocation and network optimization. This model modifies standard resource management practices to become sharper and more adaptable. Thorough examination is needed to confirm DAPM’s superiority over standard methods regarding the resolution of these problems comprehensively. This research will examine the gains in network efficiency and security outcomes from the sophisticated models of DAPM. The detailed methodology seeks to construct a network framework that is both dependable and secure and will boost NGMA’s operational effectiveness and durability.

关键词： Data privacy Adaptation models Aerodynamics Dynamic scheduling Throughput User experience Stability analysis Security Resource management Standards

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UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

arXiv

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arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

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Cutting-Edge Machine Learning Algorithms for In-Depth Characterization and Efficiency Improvement of Phase Change Heat Transfer Mechanisms

Cutting-Edge Machine Learning Algorithms for In-Depth Charac...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Geetanjli Khambra K Praveena Manjunatha Amit Dutt Irfan Khan Ali Ashoor Issa Department of Computer Applications The Bhopal School of Social Sciences (BSSS) College Bhopal M.P India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study introduces a sophisticated and holistic methodology for the enhanced understanding and optimization of phase change heat transfer mechanisms. The proposed framework integrates Deep Learning Neural Networks (DLNN), Random Forest (RF), Long Short-Term Memory (LSTM) Networks, Gaussian Processes (GP), and Reinforcement Learning (RL) in a synergistic manner, emphasizing the iterative and adaptive nature of each algorithm. A detailed flowchart for each algorithm delineates the process, showcasing their roles in refining predictions and optimizing models. An ablation study underscores the crucial contribution of each component, revealing their collective efficacy. Comparative analyses against existing algorithms highlight the proposed method’s consistent superiority in accuracy, precision, recall, mean squared error, and Rsquared. Resource efficiency metrics further affirm its computational effectiveness. Visualizations provide a comprehensive and intuitive representation of the methodology’s strengths. The proposed framework offers a promising avenue for characterizing and optimizing phase change heat transfer, contributing to advancements in thermal sciences and real-world applications.

关键词： Radio frequency Visualization Thermal expansion Machine learning algorithms Accuracy Neural networks Prediction algorithms

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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

arXiv

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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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