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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

IEEE Wireless Communications Letters 2025年第6期14卷 1618-1622页

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： Aircraft communication

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Intelligent Target Cell Selection Algorithm for Low Latency 5G Networks 2nd

Intelligent Target Cell Selection Algorithm for Low Latency ...

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2nd EAI International Conference on Computational Intelligence and Communications, CICom 2021

作者： Nyangaresi, Vincent Omollo Al Sibahee, Mustafa A. Abduljabbar, Zaid Ameen Alhassani, Abdulhadi Abduljaleel, Iman Qays Abood, Enas Wahab Faculty of Biological & Physical Sciences Tom Mboya University College Homabay Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen China Communications Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq Department of Mathematics College of Science University of Basrah Basrah Iraq

ISBN: (纸本)9783031195228

Security and efficiency during the handover process are critical challenges in 5G heterogeneous networks owing to its strict low latency and security requirements. The 5G networks support many services and communication paradigms. Examples of these applications include as Internet of Things (IoT), vehicular ad hoc networks (VANETs), device-to-device communication (D2D), among others. As such, security lapses in the core network can easily escalate to the supported communication devices and networks and vice versa. Unfortunately, legacy cellular networks deploy the received signal strength indicator (RSSI) as the only parameter during the handover process. This results in increased handover latencies, packet losses, and high handover failure probability. Consequently, other protocols have been introduced that deploy additional handover parameters such as data rates, service costs, battery power, bandwidth, and energy consumption. However, these schemes only deal with efficiency of the handoff process but rarely consider security and privacy issues. Conversely, security and privacy protocols rarely address efficiency aspects of the handover process. In this paper, an algorithm that leverages on artificial neural networks coupled with fuzzy logic for target cell selection is presented. Based on the obtained results, there is a 56.1% reduction in handover latency and a 38.8% reduction in packet losses when the proposed scheme is deployed. In terms of security, it upholds both backward and forward key secrecy. In addition, desynchronization and replay and attacks are effectively thwarted in the proposed algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： 5G mobile communication systems

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Adversarial-aware Machine Learning for Enhancing Smart Grid Security 25

Adversarial-aware Machine Learning for Enhancing Smart Grid ...

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25th International Middle East Power System Conference, MEPCON 2024

作者： Sodiq, Yusuf Ademola Gaber, Tarek Amin, Reham Jimenez-Aranda, Angel Galeela, Mohamed The University of Salford School of Science Engineering & Environment Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Computer Sciences Department Ismailia Egypt The University of Salford Centre for Sustainable Innovation Salford Business School Manchester United Kingdom TNEI Service LTD Manchester United Kingdom Cairo University Faculty of Engineering Electrical Power Engineering Department Egypt

ISBN: (纸本)9798350379648

Smart grid systems are essential components of modern power infrastructures, where machine learning has found wide-ranging applications, particularly in the development of intrusion detection systems. However, such systems remain vulnerable to adversarial attacks, including data poisoning techniques like label flipping attack. This paper proposes a novel, adversarial-aware machine learning-based intrusion detection system that is robust against label flipping attacks, thereby enhancing the integrity and reliability of the energy distribution network. The proposed solution was evaluated using the ICS cyber-attack dataset from the University of Queensland, with multiple models undergoing progressive testing. Initially, the base model employed three key algorithms: Random Forest, K-Nearest Neighbors, and XGBoost. These models were subjected to simulated label flipping attacks with varying intensities (10%-50%), which caused a significant decline in performance. To counter this, a countermeasure technique was integrated into the system, successfully restoring model accuracy and achieving a consistent accuracy of 75.96% across all attack intensities. Among the algorithms, Random Forest demonstrated the greatest resilience, showing a notable recovery after mitigation. This study underscores the critical role of preventive defense strategies and robust machine learning algorithms in safeguarding smart grids against cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1176-1193页

作者： Xiaoling Wang Qi Kang MengChu Zhou Qi Deng Zheng Fan Haoyue Liu Department of Control Science and Engineering Tongji University Shanghai China College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

To realize Industry 5.0, manufacturers face various optimization problems that seldom appear in isolation. Evolutionary MultiTasking (EMT) is an effective method to solve multiple related problems by extracting and utilizing common knowledge. Knowledge transfer is the key to the effectiveness of EMT. Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance. There is plentiful knowledge in assistant tasks, and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks. EMT is thus confronted with a challenge to find appropriate knowledge. This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks. During the evolution process, better-performing candidates are supposed to have advantages in exploitation. Therefore, assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge. Specifically, the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population. Considering that target and assistant sub-populations have different characteristics, we use domain adaptation to reduce their distribution discrepancies. In this way, the trained classifier can classify assistant individuals more accurately, and truly useful knowledge can be selected for target tasks. The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems.

关键词： Learning systems Diversity reception Machine learning Learning (artificial intelligence) Multitasking Fifth Industrial Revolution Optimization Knowledge transfer Faces Convergence

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Lyapunov Characterization for ISS of Impulsive Switched systems

arXiv

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arXiv 2024年

作者： Ahmed, Saeed Bachmann, Patrick Trenn, Stephan Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands The Institute of Mathematics University of Würzburg Germany The Jan C. Willems Center for Systems and Control The Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Faculty of Science and Engineering University of Groningen Netherlands

In this study, we investigate the ISS of impulsive switched systems that have modes with both stable and unstable flows. We assume that the switching signal satisfies mode-dependent average dwell and leave time conditions. To establish ISS conditions, we propose two types of time-varying ISS-Lyapunov functions: one that is non-decreasing and another one that is decreasing. Our research proves that the existence of either of these ISS-Lyapunov functions is a necessary and sufficient condition for ISS. We also present a technique for constructing a decreasing ISS-Lyapunov function from a non-decreasing one, which is useful for its own sake. Our findings also have added value to previous research that only studied sufficient conditions for ISS, as our results apply to a broader class of systems. This is because we impose less restrictive dwell and leave time constraints on the switching signal and our ISS-Lyapunov functions are time-varying with general nonlinear conditions imposed on them. Moreover, we provide a method to guarantee the ISS of a particular class of impulsive switched systems when the switching signal is unknown. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions

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Nanostructured Quadric-Confined Quantum Dot–Quantum Well Kesterite Solar Cell: A Pathway for Cost-Effective Photosensitive Devices

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Physica Status Solidi (A) Applications and Materials science 2025年

作者： Mohanty, Smruti Ranjan Palanisamy, Chandrasekar Sahoo, Sudarsan Routray, Soumyaranjan Department of Electronics and Instrumentation Engineering NIT Silchar Assam Silchar788010 India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara391760 India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Nanostructure kesterite-based solar with a combination of multi-quantum dots (QDs) and multi-quantum well (QW) region is proposed to improve photoabsorption rate and conversion efficiency. The scope of kesterite hybrid-confined structure attracts researchers due to their environment-friendly, encouraging optic-electrical characteristics and carrier confinement properties. The optimal design of a quadric-confined QDQW solar cell (QCQDQW) focuses on nanostructures that improve the open-circuit voltage Voc. This article examines the overall performance of QCQDQW structure by implementing both QD (width 10 nm) and QW (10 nm barrier and 5 nm well width) layers (CZGSe/ (Formula presented.)) considering S/Se composition variations (x = 0.2–0.8). Moreover, a thin 5 nm barrier layer is engineered between QD and QW layers, enhancing effective carrier quantization and collection in solar cell. The remarkable efficiency of 17.37% and Voc of 0.945 V is obtained for three QW regions and five QD layers with the highest Jsc value of 21.06 mA. This study encourages a new direction in quantum-confined solar structure. © 2025 Wiley-VCH GmbH.

关键词： Semiconductor quantum dots

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Blockchain-Based Software Subscription and Licenses Management System

Blockchain-Based Software Subscription and Licenses Manageme...

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International Conference on Information Technology Research (ICITR)

作者： Hithru De Alwis Adeesha Wijayasiri Shamila De Silva Kasun De Silva Department of Computer Science and Engineering Faculty of Engineering University of Moratuwa Sri Lanka

Current software licensing models exhibit shortcomings in transparency, security, and adaptability. Addressing these challenges, this study presents a novel blockchain-based licensing system using the Ethereum platform. By employing smart contracts and the ERC721 and ERC20 token standards, the system ensures automated, transparent, and secure license agreement enforcement and facilitates license token operations. Influenced by the rise of subscription licenses and the implications of the UsedSoft court decision, the research designs a blockchain-driven subscription license model, analyses the UsedSoft case’s impact on license transfers, and formulates specialized smart contracts for varied licensing models. The approach signifies a marked advancement in contemporary software licensing practices.

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey 1

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Aeri, Manisha Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, AI, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated. © 2023 IEEE.

关键词： COVID-19

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Internet of Intelligent Things (IoT) - An Overview 1

Internet of Intelligent Things (IoT) - An Overview

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

ISBN: (纸本)9798350338027

The basic goal of the IoT is to permit the online connection and communication of everyday physical objects like appliances, vehicles, and other gadgets. This paves the way for effortless and automated data collection and exchange, as well as interaction between the objects themselves and with humans. The Internet of Everything (IoE) is an expansion of the Internet of Things (IoT) that aims to maximize and improve human life by connecting and making the most of all available resources (humans, information, and technology). The transportation, healthcare, agricultural, energy, and manufacturing sectors are all examples. The IoIT aims to incorporate AI and ML within the IoT's interconnected network of physical items. These objects can then independently behave in response to the information they have gathered and the instructions they have been given. This has the potential to lead to smarter, more efficient systems that can react to new information and human input in real time. The Internet of Things, the Internet of Everything, and the Industrial Internet of Things all have the same overarching goal: to pave the way for the development of more efficient and intelligent systems and processes that can enhance a wide range of human endeavors. However, in order to make sure that these technologies are used in a responsible and ethical way, it will be necessary to carefully analyze their consequences and the issues they provide. © 2023 IEEE.

关键词： Internet of things

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Audio-based tissue classification - preliminary investigation for a needle procedure

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Current Directions in Biomedical engineering 2023年第1期9卷 347-350页

作者： Serwatka, Witold Heryan, Katarzyna Sorysz, Joanna Illanes, Alfredo Boese, Axel Krombach, Gabrielle A. Friebe, Michael Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology al. A. Mickiewicza 30 Kraków Poland Department of Measurement and Electronics AGH University of Science and Technology Kraków Poland Intelligente Katheter Otto-von-Guericke Universtät Magdeburg Germany Department of Diagnostic and Interventional Radiology University Hospital Justus-Liebig-University Giessen Germany Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Kraków Poland

Image-guided and minimally invasive procedures still require confirmation on having reached a target. Intraoperative imaging is not always sufficient or conclusive as it comes with artifacts that can come with a certain amount of ambiguity and inaccurate location information. As an alternative to imaging, we want to explore sounds produced by the biopsy needle tip while advancing and interacting with tissue. In this paper, we show that by analyzing vibroacoustic signals acquired at the proximal end of the needle we are able to differentiate the tissue type. In total, 419 audio samples of 5 tissues were acquired and converted to spectrograms used as input to a convolutional neural network. Using this experimental setup we were able to differentiate the tissue types with an F1 score of 71.64%. Based on these results we were able to demonstrate the feasibility of our approach, as well as the importance of further experiments to ensure that vibroacoustic sounds produced by the needle tip can be a new navigation method. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Needles

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