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A survey on user-centric cell-free massive MIMO systems

Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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An Adaptive Tracking control Approach for a Quadrotor Transporting a Cable-Suspended Payload

An Adaptive Tracking Control Approach for a Quadrotor Transp...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jianzhe Fu Yanmei Jia Zongyang Lv Yuhu Wu School of Control Science and Engineering Dalian University of Technology Dalian China School of Science Dalian Minzu University Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This study presents an adaptive control system for controlling the motion of a quadrotor transporting a cable-suspended payload. The technique uses adaptive updating rules to estimate the unknown disturbance operating on the pay-load. This work employs the Lyapunov method and LaSalle’s invariance theorem to examine and demonstrate that the suggested algorithm ensures the control system’s asymptotic stability. The simulation at the end of the paper validates the performance of the constructed adaptive controller.

关键词： Asymptotic stability Estimation error Automation Stability analysis Closed loop systems Adaptive control Payloads

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Fusion of Time and Frequency Domain Attributes for the Automated Recognition of Emotion using EEG Recordings 21

Fusion of Time and Frequency Domain Attributes for the Autom...

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21st OITS International Conference on Information technology, OCIT 2023

作者： Maithri, M. Raghavendra, U. Gudigar, Anjan Sambrama Praharaj, Samir Kumar Manipal Institute of Technology Manipal Academy of Higher Education Department of Mechatronics Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Instrumentation and Control Engineering Manipal576104 India North Carolina State University Department of Electrical and Computer Engineering United States Manipal Academy of Higher Education Kasturba Medical College Manipal Department of Psychiatry Manipal India

ISBN: (纸本)9798350358230

Emotions have a significant impact on a person's behavior. Expression reflects how people perceive events, interactions, and judgment. It is possible to identify emotions and categorize them using several techniques, including electroencephalography (EEG) signals, gestures, facial expressions, speech patterns, etc. However, emotion recognition using physiological signals has gained popularity owing to its authenticity. In this study, 54 attributes were extracted from each EEG channel to gather emotional information. These attributes were then fed to multiple classifiers and the results were compared. The study attained a maximum accuracy of 89.52% for channel T7 - 24 using ensemble bagged tree classifiers. © 2023 IEEE.

关键词： Emotion Recognition

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computer Vision Approaches for Automated Bee Counting Application

arXiv

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arXiv 2024年

作者： Bilik, Simon Janakova, Ilona Ligocki, Adam Ficek, Dominik Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset. © 2024, CC BY.

关键词： Object detection

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UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-Generation Framework

UNav-Sim: A Visually Realistic Underwater Robotics Simulator...

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International Conference on Advanced Robotics (ICAR)

作者： Abdelhakim Amer Olaya Álvarez-Tuñón Halil İbrahim Uğurlu Jonas Le Fevre Sejersen Yury Brodskiy Erdal Kayacan Department of Electrical Engineering and Computer Engineering Aarhus University Aarhus Denmark EIVA a/s Skanderborg Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

Underwater robotic surveys can be costly due to the complex working environment and the need for various sensor modalities. While underwater simulators are essential, many existing simulators lack sufficient rendering quality, restricting their ability to transfer algorithms from simulation to real-world applications. To address this limitation, we introduce UNav-Sim, which, to the best of our knowledge, is the first simulator to incorporate the efficient, high-detail rendering of Unreal Engine 5 (UE5). UNav-Sim is open-source 1 1 https://***/open-airlab/UNav-Sim and includes an autonomous vision-based navigation stack. By supporting standard robotics tools like ROS, UNav-Sim enables researchers to develop and test algorithms for underwater environments efficiently.

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Stress Matrix-Based Formation control of Multi-Agent Systems with Discrete-Time Communication

Stress Matrix-Based Formation Control of Multi-Agent Systems...

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International Conference on Systems and control (ICSC)

作者： Okechi Onuoha Brendan Chijioke Ubochi Suleiman Kurawa Zezhi Tang Changshun Wu Yi Dong School of Physics Engineering and Computer Science (SPECS) University of Hertfordshire UK The Department of Electrical and Electronic Engineering Federal University of Technology Akure Nigeria The Department of Electrical Engineering University of Manchester UK The Department of Automatic Control and Systems Engineering University of Sheffield UK University Grenoble Alpes Grenoble France The School of Electronics & Computer Science University of Southampton UK

ISBN: (数字)9798331531812

ISBN: (纸本)9798331531829

This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws.

关键词： Target tracking Simulation Accelerated aging control systems Formation control Stability analysis Stress Time-varying systems Multi-agent systems

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Robust control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Machine Learning and Intelligent Network System based Covid 19 Health Care System 3

Machine Learning and Intelligent Network System based Covid ...

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3rd International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2022

作者： Basha, Amjath Ali Jakul Saravanan, K. Mangayarkarasi, T. Balarengadurai, C. University of Technology and Applied Sciences Ibra Sultanate of Oman RCC - ENGG Department Computer Engineering Electrical and Electronic Section Department of Engineering Oman R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Control Engineering Tamil Nadu Chennai India Vidyavardhaka College of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9781665462754

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency. © 2022 IEEE.

关键词： COVID-19

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Deep Learning based Defect Detection Algorithm for Solar Panels 5

Deep Learning based Defect Detection Algorithm for Solar Pan...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Li, Jiaqi Li, Hongxu Wu, Yifan Zhou, Hailiang Manfredi, Luigi Li, Peng Zhang, Hong Hebei Province Yanshan University Department of Key Lab of Industrial Computer Control Engineering Qinhuangdao066004 China University of Alberta Department of Computing Science EdmontonABT6G 2R3 Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. China United Kingdom Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China

ISBN: (纸本)9798350307320

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots. © 2023 IEEE.

关键词： Solar panels

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QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs

arXiv

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arXiv 2024年

作者： Wang, Pengyu Yang, Zhaohua Yang, Nachuan Wang, Zikai Li, Jialu Zhang, Fan Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shi, Ling Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Control Science and Engineering Shandong University Shandong China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada

Safety-critical intelligent cyber-physical systems, such as quadrotor unmanned aerial vehicles (UAVs), are vulnerable to different types of cyber attacks, and the absence of timely and accurate attack detection can lead to severe consequences. When UAVs are engaged in large outdoor maneuvering flights, their system constitutes highly nonlinear dynamics that include non-Gaussian noises. Therefore, the commonly employed traditional statistics-based and emerging learning-based attack detection methods do not yield satisfactory results. In response to the above challenges, we propose QUADFormer, a novel Quadrotor UAV Attack Detection framework with transFormer-based architecture. This framework includes a residue generator designed to generate a residue sequence sensitive to anomalies. Subsequently, this sequence is fed into a transformer structure with disparity in correlation to specifically learn its statistical characteristics for the purpose of classification and attack detection. Finally, we design an alert module to ensure the safe execution of tasks by UAVs under attack conditions. We conduct extensive simulations and real-world experiments, and the results show that our method has achieved superior detection performance compared with many state-of-the-art methods. (Video1) Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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