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Set Stabilization of Large-Scale Stochastic Boolean Networks:A Distributed Control Strategy

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 806-808页

作者： Lin Lin Jinde Cao Jianquan Lu Leszek Rutkowski the Department of Mechanical Engineering the University of Hong KongHong Kong China the School of Mathematics Southeast UniversityNanjing 210096China IEEE the School of Mathematics and the Frontiers Science Center for Mobile Information Communication and Security Southeast UniversityNanjing 210096China the School of Mathematics the Southeast UniversityNanjing 210096 the School of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China the Systems Research Institute of the Polish Academy of Sciences Warsaw 01-447 the Institute of Computer Science AGH University of Science and TechnologyKraków 30-059 the Information Technology Institute University of Social SciencesŁódź90-113Poland

Dear Editor,This letter deals with the set stabilization of stochastic Boolean control networks(SBCNs)by the pinning control strategy,which is to realize the full control for systems by imposing control inputs on a fr... 详细信息

关键词： Boolean stabilization letter

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A ROS-2 based Path Planning and Maze Solving of Autonomous Robots

A ROS-2 based Path Planning and Maze Solving of Autonomous R...

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Advances in Computing,Communication, electrical, and Smart systems (iCACCESS), International Conference on

作者： Ahsan Kabir Nuhel Sazzad Hossain Md.Tonim Hassan Iftekhar Haider Ankur Saha Adnan Sabith Taha Asma-UI Husna Ony Electrical and Electronics Engineering American International University-Bangladesh (AIUB) Computer Networks and Systems Security University of Hertfordshire Electrical and Electronics Engineering University of Brahmanbaria (UOB) Computer Science and Engineering American International University-Bangladesh (AIUB) Computer Science and Engineering University of Information Technology and Sciences

The goal of this study is to build a self-driving robot that can effectively navigate mazes by utilizing sophisticated computer vision algorithms with ROS2. Fusion 360 is used to create the robot model, and ROS2 launch files are used to initialize the sensor and control nodes. Attaining optimal path planning and accurate localization are the main objectives. The combination of novel motion planning based on the orientation of the robot with graph search algorithms like DFS, A*, and Dijkstra's leads to effective maze discovery and traversal. Thorough testing shows that the system can successfully navigate unfamiliar maze configurations, demonstrating the dependability of ROS2 and modern robotics techniques. By completing this study, a foundation for future advancements in perception and planning to manage more complex settings is laid.

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Transi-Net: An Explainable Deep Learning Model Ensemble For Prostate's Transition Zone Segmentation 23

Transi-Net: An Explainable Deep Learning Model Ensemble For ...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos S. Kalantzopoulos, Charalampos Marias, Kostas Tsiknakis, Manolis Koutsouris, Dimitrios D. Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical & Computer Engineering Biomedical Research Institute National Technical University of Athens FORTH Athens Ioannina Greece Biomedical Research Institute FORTH Ioannina Greece Computational Biomedicine Laboratory FORTH Heraklion Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Biomedical Research Institute Unit of Medical Technology and Intelligent Information Systems FORTH University of Ioannina Ioannina Greece

ISBN: (纸本)9798350393118

The identification of the location of prostate cancer is of paramount importance for improved treatment. This process is strictly bonded with the accurate segmentation of the prostate gland and its zones, on MR images. In the present study, an ensemble of 3 deep learning models along with a Meta-learner module able to refine the outcomes of the models, is proposed (Transi-Net) to segment the prostate's transition zone. A method to quantify the model's uncertainty is introduced to measure the confidence of an architecture with respect to its final decision. The backbone of Transi-Net consist the original U-net, Dense2U-net and Bridged U-net models. The proposed model showcased significant improvement in comparison with its base components as well as an independent model, the USE-Net, while it was proven more confident about its decision. The proposed model resulted in an improvement of 5%, 3%, 3% and 4% for Sensitivity, Balanced Accuracy, Dice Score and Rand Error Index respectively, compared to the second best, USE-Net. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Quality-Aware Video Analytics in Edge Computing Environments 19

Quality-Aware Video Analytics in Edge Computing Environments

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Sadat, Mohammad Nazmus Vargas-Alfonso, Erwin Dai, Rui Bradley University Department of Computer Science and Information Systems PeoriaIL United States University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States

ISBN: (纸本)9781665431620

Edge computing has opened new doors for real-time video analytics applications due to its ability to offer significantly faster response times by processing videos near the source. However, the limited computing capabilities of edge devices can affect the quality of video analysis. While the existing literature has focused on minimizing latency, the quality aspect needs to be rigorously investigated to satisfy the high accuracy requirements of video analytics applications. This paper proposes a new quality-aware video analytics framework for edge networks. First, a real-time video analytics platform based on edge computing is designed and implemented. Then, the impacts of different factors on the quality and the overall latency of edge computing-based video analysis are investigated. Next, the trade-off between the video analysis quality and the latency, i.e., computation benefits at the edge vs. remote server, is studied. Finally, a quality-aware edge computing-based video analytics framework (QVAF) is designed to minimize the total latency while guaranteeing a required detection accuracy. Evaluation results show that QVAF could significantly improve response times across different detection accuracy requirements. © 2022 IEEE.

关键词： Edge computing

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English Offensive Text Detection using CNN based Bi-GRU model 2

English Offensive Text Detection using CNN based Bi-GRU mode...

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2nd International Conference on information and Communication Technology, ICICT 2024

作者： Roy, Tonmoy Islam, Md Robiul Miazee, Asif Ahammad Antara, Anika Al Amin Hossain, Sunjim Data Analytics & Information Systems Utah State University Utah United States Computer Science William & Mary Virginia United States Computer Science Maharishi International University Iowa United States Electrical and Electronics Engineering Brac University Dhaka Bangladesh Computer Science Uttara University Dhaka Bangladesh Computer Science Northern University Dhaka Bangladesh

ISBN: (纸本)9798331508227

Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing models. ©2024 IEEE.

关键词： Tweets

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Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

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computers, Materials & Continua 2023年第1期74卷 453-467页

作者： Mohamed Amine Ouamri Reem Alkanhel Cedric Gueguen Manal Abdullah Alohali Sherif S.M.Ghoneim University of Rennes 1/ADOPNET team at IRISA laboratory Rennes35000France Facultéde Technologie Laboratoire d’Informatique Médicale(LIMED)Universitéde BejaiaBejaia06000Algérie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electrical engineering department College of EngineeringTaif University P.O.Box 11099Taif21944Saudi Arabia

With the rapid development of emerging 5G and beyond(B5G),Unmanned Aerial Vehicles(UAVs)are increasingly important to improve the performance of dense cellular *** a conventional metric,coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous *** recent years,stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network *** this paper,an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been *** assumption was considered that all users are distributed according to Poisson Cluster Process(PCP)around base stations,in particular,Thomas Cluster Process(TCP).Using thismodel,the impact of beam alignment errors on the coverage probabilitywas ***,the ProbabilityDensity Function(PDF)of directional antenna gain between the user and its serving base station was ***,association probability with each tier was achieved.A tractable expression was derived for coverage probability in both Line-of-Sight(LoS)andNon-Line-of-Sight(NLoS)condition *** results demonstrated that at low UAVs altitude,beam alignment errors significantly degrade coverage ***,for a small cluster size,alignment errors do not necessarily affect the coverage performance.

关键词： Unmanned aerial vehicles coverage analysis stochastic geometry millimeter wave imperfect beam alignment

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State of Health Estimation of Lithium Batteries through Vision Transformer Analysis of Electrochemical Impedance Spectroscopy 51

State of Health Estimation of Lithium Batteries through Visi...

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51st International Conference on computers and Industrial engineering, CIE 2024

作者： Selim, Alaa Mo, Huadong Pota, Hemanshu Li, Chaojie Liu, Xiaoyu Zhou, Jingxuan Dong, Daoyi School of Engineering and Technology University of New South Wales CanberraACT Australia School of Systems and Computing University of New South Wales CanberraACT Australia School of Electrical Engineering and Telecommunication UNSW SydneyNSW Australia Guanghua School of Management Peking university Beijing China School of Computer and Information Engineering Chuzhou university Chuzhou China CIICADA Lab School of Engineering Australian National University CanberraACT Australia

The rapid advancement and widespread deployment of energy storage systems, particularly lithium-ion batteries, underscore the necessity for precise and reliable state of health (SOH) estimation techniques. This paper develops a novel approach to battery SOH estimation by leveraging Vision Transformers (ViTs), a class of transformer models excelling in handling visual data, to analyze Electrochemical Impedance Spectroscopy (EIS) data. Unlike traditional methods that rely heavily on handcrafted features or classical impedance models, our methodology treats EIS Nyquist plots as images and utilizes ViTs for direct SOH classification. We use a comprehensive real dataset comprising thousands of Nyquist plots, labeled with their corresponding SOH and augmented through various colormap transformations to enhance ViT robustness. Our experiments demonstrate that ViTs can capture subtle, yet critical, impedance characteristics indicative of battery health, achieving superior classification accuracy compared to conventional machine learning techniques. The broader impact of this research lies in its potential to enhance the reliability and longevity of battery systems across various applications, from consumer electronics to electric vehicles and grid storage, ultimately contributing to more sustainable and efficient energy management. © 2024 computers and Industrial engineering. All rights reserved.

关键词： Diagnosis

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Mobile Network of Air Quality Monitoring Sensors 23

Mobile Network of Air Quality Monitoring Sensors

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and Informatics

作者： Vasileios Periklis Psychogyios Vassilis Kapsalis Masters Course in Information Systems Hellenic Open University Greece Department of Electrical and Computer Engineering University of Peloponnese Greece

ISBN: (纸本)9798400716263

This paper presents the development of a network of mobile sensors for the real-time monitoring of the atmospheric air in order to calculate the Air Quality Index (AQI). As a proof of concept, a prototype of the sensor, a web application and a mobile application were implemented in order to manage the mobile sensors and process/view the data collected by them. The sensor, which can be installed in vehicles, collects and transmits environmental measurements wirelessly to a web application using proper APIs. These measurements are stored in a database and based on them, the Air Quality Index (AQI) is calculated. Following a publish-subscribe model, real-time data are forwarded to authorized users for monitoring purposes in the form of charts and tables. Historical data are used for analysis and study of the air quality over time and also used as an input to the Random Forest predictive algorithm implemented to predict air quality in locations without availability of real-time measurements. Monitoring air quality through mobile sensors constitutes a valuable tool towards the study of air pollution in large areas overcoming the limitations of fixed sensors. Furthermore, the immediate notifications of high-precision AQIs help people with respiratory problems to take timely protection measures.

关键词： AQI Mobile air quality sensor mobile application monitoring notifications predictions

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Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

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computers, Materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.Mohamed Saud S.Alotaibi Majdy M.Eltahir Heba Mohsen Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD *** major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of *** performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

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Joint Optimizations for Double-IRS’ Cooperative Positioning and Beamforming Over Massive-MIMOAP Based 6G Secure Mobile Wireless Networks

Joint Optimizations for Double-IRS’ Cooperative Positioning...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiaojie Wang Fei Wang Xi Zhang Yuanyuan Yang College of Electronic and Information Engineering Southwest University China Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Intelligent reflecting surface (IRS) has been widely recognized as one of the key techniques to improve secure communications performances. However, most existing works mainly focus on the passive beamforming, i.e., phase-shifts, design of a single IRS, without taking into account the cooperations among multiple IRSs and the optimizations of their relative positions. To overcome these deficiencies, in this paper we propose the joint optimizations between the transmit beamforming of massive multiple-input multiple-output (massive-MIMO) access point (AP) and double-IRS’ positions and passive beamforming over 6G secure mobile networks. In our proposed networking architectures, AP transmits data to multiple mobile users (MUs) through reflections of two cooperative IRSs under the existence of one eavesdropper (Eve). First, we formulate a secrecy rate optimization problem to maximize the expectations of all MUs’ aggregate secrecy rates when Eve’s exact position is unknown. Second, leveraging the deep reinforcement learning (DRL), we develop two joint deploying and beamforming schemes to tackle the uncertainty of Eve’s exact position. Finally, we validate and evaluate our developed schemes by conducting the extensive simulations, showing the significant performances improvements of our developed schemes through jointly optimizing IRSs’ positions/orientations and transmit-beamforming of massive-MIMOAP as the function of the predicted Eve’s deploying areas.

关键词： 6G mobile communication Uncertainty Array signal processing Wireless networks Predictive models Deep reinforcement learning Reflection MIMO Global communication Optimization

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