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6G Communication New Paradigm: The Integration of Unmanned Aerial Vehicles and Intelligent Reflecting Surfaces

IEEE Communications Surveys and Tutorials 2025年

作者： Ning, Zhaolong Li, Tengfeng Wu, Yu Wang, Xiaojie Wu, Qingqing Yu, Fei Richard Guo, Song Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing400065 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai Shanghai200240 China Carleton University School of Information Technology OttawaONK1S 5B6 Canada The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

With the continuous development of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs), their combination has become foundational technologies to complement the terrestrial network by providing communication enhancement services for large-scale users. This article provides a comprehensive overview of IRS-assisted UAV communications for 6th-Generation (6G) networks. First, the applications supported by IRS-assisted UAV communications for 6G networks are introduced, and key issues originated from applications supported by IRSs and UAVs for 6G networks are summarized and analyzed. Then, prototypes and main technologies related to the integration of IRSs and UAVs are introduced. Driven by applications and technologies of IRS-assisted UAV communications, existing solutions in the realms of energy-constrained communications, secure communications, and enhanced communications are summarized, and corresponding empirical lessons are provided. Finally, we discuss some research challenges and open issues in IRS-assisted UAV communications, offering directions for the future development. © 1998-2012 IEEE.

关键词： Aircraft communication

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Traffic light detection using ensemble learning by boosting with color-based data augmentation

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International Journal of Transportation Science and technology 2024年

作者： Lin, Huei-Yung Chen, Yong-Ci Department of Computer Science and Information Engineering National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Road Taipei106344 Taiwan Department of Electrical Engineering National Chung Cheng University 168 University Road Min-Hsiung Chiayi621301 Taiwan

Recent advancements in deep neural networks have significantly improved the detection and recognition of traffic lights for advanced driver assistance systems (ADAS). Traditional methods often rely on identifying traffic light boxes and then recognizing individual signal lights, which can be problematic due to variations in bulb arrangements across different regions. To address this limitation, we propose a novel traffic light detection method that directly recognizes individual signal lights. Our two-stage approach combines data augmentation and ensemble learning to achieve high detection rates. By learning color characteristics from validation sets, we can effectively identify signal light candidates with a 97.26% accuracy rate. Subsequent classification results in a recognition accuracy of 98.6%, surpassing the performance of existing state-of-the-art traffic light detection algorithms. Code and dataset are available at https://***/981124/yolov7_traffic_light_detect. © 2024 Tongji University and Tongji University Press

关键词： Advanced driver assistance systems

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A Novel and Advantageous Recovery Solution for Deadlock Problem of Flexible Manufacturing Systems Based on Petri Nets Modeling Theory 9

A Novel and Advantageous Recovery Solution for Deadlock Prob...

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9th International Conference on Applied System Innovation, ICASI 2023

作者： Tseng, Ching-Yun Chen, Ju-Chin Pan, Yen-Liang National Kaohsiung University of Sciences and Technology Department of Electronic Engineering Taiwan National Kaohsiung University of Sciences and Technology Department of Computer Science and Information Engineering No. 415 Jiangong Rd. Sanmin Dist. Kaohsiung City807618 Taiwan General Education Center Air Force Academy Mathematics and Physics Division No. Sisou 1 Jieshou W. Rd. Gangshan Dist. Kaohsiung City820009 Taiwan

ISBN: (纸本)9798350398380

Instead of the mass production industry in the past, over these decades, precision and automatic manufacturing have become more and more widely accepted in the production area. With highly variable productivity and flexibility, Flexible Manufacturing Systems (FMS) can decrease production costs and increase efficiency. Due to its resource sharing, unexpected system deadlock may occur in some situations. In research on system deadlock control, lots of existing literature use deadlock prevention as the primary control method, while it could block resources transporting and downscale the system reachability graph. This paper adopts a deadlock recovery policy as the direct control strategy based on control transition technology. This kind of control strategy and its benefit could be demonstrated through classical systems of simple sequential processes with resources (S3PR) nets and their Petri nets model. © 2023 IEEE.

关键词： Petri nets

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Streamlined Transmission: A Semantic-Aware XR Deployment Framework Enhanced by Generative AI

arXiv

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arXiv 2024年

作者： Yang, Wanting Xiong, Zehui Quek, Tony Q.S. Shen, Xuemin The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

In the era of 6G, featuring compelling visions of digital twins and metaverses, Extended Reality (XR) has emerged as a vital conduit connecting the digital and physical realms, garnering widespread interest. Ensuring a fully immersive wireless XR experience stands as a paramount technical necessity, demanding the liberation of XR from the confines of wired connections. In this paper, we first introduce the technologies applied in the wireless XR domain, delve into their benefits and limitations, and highlight the ongoing challenges. We then propose a novel deployment framework for a broad XR pipeline, termed "GeSa-XRF", inspired by the core philosophy of Semantic Communication (SemCom) which shifts the concern from "how" to transmit to "what" to transmit. Particularly, the framework comprises three stages: data collection, data analysis, and data delivery. In each stage, we integrate semantic awareness to achieve streamlined transmission and employ Generative Artificial Intelligence (GAI) to achieve collaborative refinements. For the data collection of multi-modal data with differentiated data volumes and heterogeneous latency requirements, we propose a novel SemCom paradigm based on multi-modal fusion and separation and a GAI-based robust superposition scheme. To perform a comprehensive data analysis, we employ multitask learning to perform the prediction of field of view and personalized attention and discuss the possible preprocessing approaches assisted by GAI. Lastly, for the data delivery stage, we present a semantic-aware multicast-based delivery strategy aimed at reducing pixel level redundant transmissions and introduce the GAI collaborative refinement approach. The performance gain of the proposed GeSa-XRF is preliminarily demonstrated through a case study. Copyright © 2024, The Authors. All rights reserved.

关键词： Artificial intelligence

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory Control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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Integrated Sensing and Communication With mmWave Massive MIMO: A Compressed Sampling Perspective

Integrated Sensing and Communication With mmWave Massive MIM...

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作者： Gao, Zhen Wan, Ziwei Zheng, Dezhi Tan, Shufeng Masouros, Christos Ng, Derrick Wing Kwan Chen, Sheng Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology School of Information and Electronics Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University College London Department of Electronic and Electrical Engineering LondonWC1E7JE United Kingdom University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework. © 2002-2012 IEEE.

关键词： Compressed sensing

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Optimal Cost-Aware Paradigm for Off-Grid Green Cellular Networks in Oman

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computers, Materials & Continua 2021年第8期68卷 2665-2680页

作者： Mohammed H.Alsharif Kannadasan Raju Abu Jahid Mahmoud A.Albreem Peerapong Uthansakul Jamel Nebhen Venkatesan Chandrasekaran Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversitySeoul05006Korea Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Electrical and Computer Engineering University of OttawaOttawaK1N 6N5ONCanada Department of Electronics and Communications Engineering A’Sharqiyah UniversityIbra400Oman School of Telecommunication Engineering Suranaree University of TechnologyNakhon RatchasimaThailand Prince Sattam bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

Green wireless networks or energy-efficient wireless networks have gained popularity as a research topic due to the ecological and economic concerns of cellular *** specific power supply requirements for the cellular base station,such as cost-effectiveness,efficiency,sustainability,and reliability,can be met by utilizing the technological advances in renewable *** are numerous drivers for the deployment of renewable energy technologies and the transition towards green *** energy is free,clean,and abundant in most locations throughout the ***,this work proposes a novel framework for energy-efficient solar-powered base stations for the Oman site,specifically for off-grid locations where fuel transportation for diesel generator(DG)is a serious *** demonstrate the effectiveness of the proposed system for off-grid sites,the Hybrid Optimization Model for Electric Renewables optimization software is adapted by considering real-time conditions and its technical *** cost factors such as capital cost,salvage cost,replacement cost,operational,and maintenance cost of PV panels,inverters,and batteries also undergo extensive *** the observed results,the total net present cost(NPC)of the proposed system is$27,887,while the net NPC of the DG is estimated at$32,***,the proposed scheme can potentially achieve considerable savings in the operational expenditure at approximately 15.24%.Indeed,these outcomes can provide profound economic,technical,and ecological benefits to the cellular operators of *** also ensures a sizeable reduction in greenhouse gas that supports sustainable green wireless network deployment in remote areas.

关键词： Wireless networks green wireless networks green communications sustainability OPEX

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Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

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computers, Materials & Continua 2022年第4期71卷 159-170页

作者： Dezheng Zhang Qi Jia Shibing Yang Xinliang Han Cong Xu Xin Liu Yonghong Xie School of Computer&Communication Engineering University of Science&Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Inspur Electronic Information Industry Co. Ltd.&State Key Laboratory of High-End Server&Storage TechnologyJinan250101China Surgical Simulation Research Lab Department of SurgeryUniversity of AlbertaEdmontonT6G 2E1AlbertaCanada

Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in *** analyze the reasoning path patterns from symptom to syndromes on the knowledge *** are two kinds of path patterns in the knowledge graph:one-hop and *** one-hop path pattern maps the symptom to syndromes *** two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic *** the different support strengths for the knowledge paths in reasoning,we design a dynamic weight *** utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score *** proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path *** evaluate the method with clinical records and clinical practice in *** preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in ***,the method is robust and explainable under the guide of the knowledge *** could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.

关键词： Traditional Chinese medicine automated diagnosis knowledge graph Naïve Bayes syndrome differentiation

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An Intelligent System Based on Heart Rate Variability Measures and Machine Learning Techniques for Classification of Normal and Growth Restricted Children 6th

An Intelligent System Based on Heart Rate Variability Measur...

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6th International Conference on Wireless Technologies, Embedded and Intelligent Systems, WITS 2020

作者： Al-Jedaani, Abdulrhman Wassil Aziz, Wajid Alshdadi, Abdulrahman A. Alqarni, Mohammed Nadeem, Malik Sajjad Ahmed Wailoo, Mike P. Schlindwein, Fernando S. College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer and Information Technology University of Azad Jammu and Kashmir Muzaffarabad AJK13100 Pakistan University of Leicester University Road LeicesterLE1 7RH United Kingdom

ISBN: (纸本)9789813368927

Growth restricted children have higher predisposition of developing metabolic syndrome, type-2 diabetes, hypertension and cardiovascular problems in later life. Numerous intelligence systems that have proved their effectiveness for detection of cardiac abnormalities to support medical diagnosis. Previous studies used heart rate variability (HRV) analysis techniques for distinguishing normal and growth restricted children, however those studies did not use intelligent systems for this purpose. The aim of present study is to develop an intelligent system using HRV analysis measures and machine learning (ML) techniques for early detection of cardiac abnormalities in growth restricted children. We performed two sets of experiments using interbeat interval time series data of the normal and growth restricted children and different combinations of individual characteristics of the subjects. Several ML algorithms such as linear discriminant analysis (LDA), support vector machine with linear and sigmoid kernels (SVML and SVMS), random forest (RF), and RPart are used for developing intelligent system to classify normal and growth restricted children. We evaluated the performance of the classifiers using sensitivity, specificity, area under receiver operator characteristic curve and total accuracy. The results reveal that the LDA is robust for classifying normal and LBW-IUGR children with 100% accuracy at all cross validation formulations. The SVMS and LDA revealed highest accuracy, whereas, RF and Rpart were robust for classifying LBW-IUGR and ABW_IUGR. Our findings show that the intelligent system developed using HRV analysis markers and ML techniques could be a reliable tool for identifying future risk of cardiac abnormalities in IUGR children. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Intelligent systems

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Predicting Malware Classification and Family using Machine Learning: A Cuckoo Environment Approach with Automated Feature Selection

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Procedia computer Science 2024年 235卷 2434-2451页

作者： Niveditha S Prianka Rr Sathya K Shreyanth S Nandhagopal Subramani Balakrishnan Deivasigamani Karthikeyan S Department of Biotechnology Rajalakshmi Engineering College Thandalam Chennai Tamil Nadu India 602105 Senior Assistant Professor Department of AIML New Horizon College of Engineering Bengaluru Karnataka India 560103 Assistant Professor senior grade-1 School of Computer Science and Engineering VIT Vellore Tamilnadu India 632014 MTech in Data Science and Engineering Birla Institute of Technology and Science Pilani Rajasthan India 333031 Assistant Professor Department of CSE(AI and ML) KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India 641407 Assistant Professor Department of Information Technology KSR Institute for Engineering and Technology

Malware’s increasing menace in the digital realm needs the development of powerful detection and classification systems. This study presents a unique method for predicting malware category and family using machine learning, leveraging the Cuckoo environment and automated feature selection. To undertake an exhaustive examination of malware activity, we combined the Cuckoo environment with cutting-edge machine learning methods, such as the k-Nearest Neighbors (KNN) technique. The dynamic analysis of malware samples within the Cuckoo environment, which captured their interactions with the execution environment, provided remarkable insights into their malicious behaviors. The Boruta method aided automated feature selection, improving the feature set and optimizing model performance. A comparison with existing models yielded striking findings. Notably, Test Case 3 surpassed earlier cases by incorporating Automated Feature Selection + Cross-Validation. In identifying phishing attempts, it displayed outstanding specificity (90%), precision (93%), recall (96%), and an impressive F1-Score (92%), indicating its proficiency in reliably recognizing this frequent threat. Test Case 3 showed considerable increases as well, with a surprising 7.0% increase in precision and a notable 14.6% increase in recall when compared to Test Case 1. Furthermore, in the field of ransomware detection, Test Case 4, which solely focused on Automated Feature Selection, obtained excellent results, with 91% specificity, 92% accuracy, and a recall rate of 93%. These developments highlight the importance of the Boruta algorithm in optimizing the model’s performance.

关键词： Malware Classification Malware Family Prediction Machine Learning (ML) Random Forest Classifier (RFC) Cuckoo Environment Cybersecurity k-Nearest Neighbors (KNN) Boruta Algorithm Automated Feature Selection

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