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Optimizing the Deployment of an Aerial Base Station and the Phase-Shift of a Ground Reconfigurable Intelligent Surface for Wireless Communication systems Using Deep Reinforcement Learning

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Information (Switzerland) 2024年第7期15卷 386页

作者： Kabore, Wendenda Nathanael Juang, Rong-Terng Lin, Hsin-Piao Tesfaw, Belayneh Abebe Tarekegn, Getaneh Berie Department of Electronic Engineering National Taipei University of Technology Taipei10608 Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan

In wireless networks, drone base stations (DBSs) offer significant benefits in terms of Quality of Service (QoS) improvement due to their line-of-sight (LoS) transmission capabilities and adaptability. However, LoS links can suffer degradation in complex propagation environments, especially in urban areas with dense structures like buildings. As a promising technology to enhance the wireless communication networks, reconfigurable intelligent surfaces (RIS) have emerged in various Internet of Things (IoT) applications by adjusting the amplitude and phase of reflected signals, thereby improving signal strength and network efficiency. This study aims to propose a novel approach to enhance communication coverage and throughput for mobile ground users by intelligently leveraging signal reflection from DBSs using ground-based RIS. We employ Deep Reinforcement Learning (DRL) to optimize both the DBS location and RIS phase-shifts. Numerical results demonstrate significant improvements in system performance, including communication quality and network throughput, validating the effectiveness of the proposed approach. © 2024 by the authors.

关键词： Base stations

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From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild

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Plant Phenomics 2023年第2期5卷 195-208页

作者： Xinlu Wu Xijian Fan Peng Luo Sruti Das Choudhury Tardi Tjahjadi Chunhua Hu College of Information Science and Technology Nanjing Forestry UniversityNanjing 210037China Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijing 100091China Key Laboratory of Forestry Remote Sensing and Information System National Forestry and Grassland AdministrationBeijing 100091China Department of Computer Science and Engineering University of Nebraska-LincolnLincolnNE 68588USA School of Engineering University of WarwickCoventry CV47ALUK

Plant disease recognition is of vital importance to monitor plant development and predicting crop ***,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory *** environment,machine learning-based recognition models generated within a specific dataset(source domain)tend to lose their validity when generalized to a novel dataset(target domain).To this end,domain adaptation methods can be leveraged for the recognition by learning invariant representations across *** this paper,we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization,namely,Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification(MSUN).Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial ***,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty *** multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source *** effectively alleviates the problem of large interdomain *** adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass ***,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain *** was experimentally validated to achieve optimal results on the PlantDoc,Plant-Pathology,Corn-Leaf-Diseases,and Tomato-Leaf-Diseases datasets,with accuracies of 56.06%,72.31%,96.78%,and 50.58%,respectively,surpassing other state-of-the-art domain adaptation techniques considerably.

关键词： Plant breakthrough details

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinpeng Xu Chunna Zhao Jing Yang Yaqun Huang Yaoyuan Yang Por Lip Yee School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data; 2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model.

关键词： Accuracy Convolution Urban areas Time series analysis Spatial databases Spatiotemporal phenomena Sensors Speech processing Spatial resolution Signal resolution

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A novel medical image enhancement technique based on hybrid method

A novel medical image enhancement technique based on hybrid ...

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International Laser technology and Optics Symposium 2022, iLATOS 2022

作者： Khudhair, Kifah T. Najjar, Fallah H. Waheed, Safa Riyadh Al-Jawahry, Hassan M. Alwan, Haneen H. Al-Khaykan, Ameer Department of Information Technology Technical College of Management Al-Furat Al-Awsat Technical University Iraq Department of Computer System Techniques Technical Institute of Najaf Al-Furat Al-Awsat Technical University Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq Air Conditioning and Refrigeration Techniques Engineering Department Al-Mustaqbal University College Hillah Babylon51001 Iraq

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm's efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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Acute lymphoblastic leukemia image segmentation based on modified HSV model

Acute lymphoblastic leukemia image segmentation based on mod...

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International Laser technology and Optics Symposium 2022, iLATOS 2022

作者： Najjar, Fallah H. Khudhair, Kifah T. Khudhair, Zaid Nidhal Alwan, Haneen H. Al-Khaykan, Ameer Department of Computer System Techniques Technical Institute of Najaf Al-Furat Al-Awsat Technical University Iraq Department of Information Technology Technical College of Management Al-Furat Al-Awsat Technical University Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Air Conditioning and Refrigeration Techniques Engineering Department Al- Mustaqbal University College Hillah Babylon51001 Iraq

Image segmentation is a critical step in computer-aided diagnosis that could speed up Leukemia detection. Leukemia is a cancer of the blood that has a reputation for being particularly lethal. Based on the immunohistochemical method, the leukocytes can be manually counted in a stained peripheral blood smear image to detect Acute Lymphoblastic Leukemia (ALL). Regrettably, the manual diagnosis process takes about 3 to 24 hours to complete, which is insufficient. This paper introduced a new and straightforward ALL image segmentation approach based on color image transformation. First, Leukemia, ALL-IDB1, ALL-IDB2, and ALL image datasets were used in this paper. The Leukemia dataset includes 208 ALL-IDB1 and ALL-IDB2 images, while The ALL dataset has 3256 images. Next, we use the HSV model to transform ALL images. In addition, we modified the HSV model by pre-processing the saturation channel for better results. Then, the pre-processed images were segmented based on a fixed threshold. After that, various metrics are utilized to measure the output of the proposed method. Finally, the proposed methodology is compared to currently used benchmarks. The proposed method outperforms previous approaches regarding accuracy, specificity, sensitivity, and time. In addition, results show that the proposed technique improves performance measures significantly. © Published under licence by IOP Publishing Ltd.

关键词： Image segmentation

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Digital Twins in the IIoT:Current Practices and Future Directions Toward Industry 5.0

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computers, Materials & Continua 2025年第6期83卷 3675-3712页

作者： Bisni Fahad Mon Mohammad Hayajneh Najah Abu Ali Farman Ullah Hikmat Ullah Shayma Alkobaisi Computer&Network Engineering United Arab Emirates UniversityAl Ain15551United Arab Emirates Big Data Analytics Centre United Arab Emirates UniversityAl Ain15551United Arab Emirates Department of Business Information Technology Liwa CollegeAbu Dhabi41009United Arab Emirates Information System&Security United Arab Emirates UniversityAl Ain15551United Arab Emirates

In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry *** article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real *** discusses the emergence of DT,contextualizing its evolution within the framework of *** study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the *** paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and *** emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing *** provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.

关键词： Digital twin in industry integration of DT in IIoT artificial intelligence AI-Driven DT applications

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Accurate Identification of Closing State of Horizontal Rotary Disconnectors in a Substation Based on Mask R-CNN 7

Accurate Identification of Closing State of Horizontal Rotar...

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7th International Conference on Computational Intelligence and Applications, ICCIA 2022

作者： Cao, Renwei Li, Wenliang Huang, Yijun Lu, Deling Lin, Qiuying Guangzhou Power Supply Co. Ltd. System Operation Department Guangzhou China South China University of Technology Department of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9781665495844

Disconnectors are extremely important equipments in a substation. Long-term use may lead to abnormal structure, resulting in inadequate closing, which means a serious accident. Therefore, the judgment of disconnector closing in place has become a very important problem. Judging whether the closing is in place requires very accurate state identification, which is difficult to judge by the current method. Firstly, this paper used mask R-CNN network for target recognition, and obtained a model with high recognition accuracy. Then, this paper used HED method to extract the edge, and proposed an algorithm to detect the closing state. Experiments show that this method can effectively identify the closing state of a horizontal rotary disconnector. © 2022 IEEE.

关键词： Image recognition

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Retraction Note: Enhancing low latency and high performance in clustered-based wireless sensor network routing using genetic algorithm for 5G networks

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Banu, Firdouse Udhayamoorthi, M. Ramana Gupta Nallagattla, Venkata Phaneendra Maguluri, Lakshmana Mahesh Kumar, N. B. Jayachitra, S. Department of Applied Information System Applied College King Khalid University Khamis Mushaiyt Saudi Arabia Department of Computer Science and Design Karpagam College of Engineering (Autonomous) Coimbatore India Department of CSE Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

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Deep Learning Network for Vision-Based UAV Recognition

Deep Learning Network for Vision-Based UAV Recognition

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Prabhakar, Telagarapu Almutairi, Maryam Almutairi, Hissah Vasamsetti, Subrahmanyam Rao, M. V. Raghavendra Kunda, Aruna GMR Institute of Technology Vizianagaram District Andhra Pradesh Rajam532127 India College of Sciences and Humanities in Hawtat Bani Tamim Department of Information System Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia College of Computer and Information Technology Department of IS and IT Jazan University Jazan Saudi Arabia Godavari Institute of Engineering & Technology Department of Automobile Engineering Rajahmundry India

ISBN: (纸本)9781665462631

Unmanned aerial vehicles (UAVs) could be used carelessly or intentionally, which poses a serious risk to aviation safety. This makes automated UAV detection and tracking a crucial component of aerial security systems. Radar, radio frequency, and visible- and thermal-infrared imaging are often used technologies for UAV detection. Deep neural networks (DNNs) for the image-based object recognition have recently made advancements that make it possible to leverage For this detecting and tracking challenge, visual data. Through the encoder-decoder structure network, the suggested technique first estimates the distribution line area. The interference is eliminated using sampling clustering and image processing operations. Finally, based on the discovered distribution line, the UAV direction of tracking of the power line is determined. The approach can achieve a frame inspection speed of over 78 ms, a heading error of deviation range of (-1.53°, 1.36°), and a tracking rate of almost 99%. The results of the network and inspection technique dataset test demonstrate that the approach presented in this research may be used to check UAV distribution lines efficiently, swiftly, and accurately. © 2023 IEEE.

关键词： Aircraft detection

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Q-learning Based Meta-Heuristics for Scheduling Bi-Objective Surgery Problems with Setup Time

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Complex system Modeling and Simulation 2024年第4期4卷 321-338页

作者： Ruixue Zhang Hui Yu Adam Slowik Kaizhou Gao School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of System Engineering Macao University of Science and TechnologyMacao 999078China the Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

Since the increasing demand for surgeries in hospitals,the surgery scheduling problems have attracted extensive *** study focuses on solving a surgery scheduling problem with setup *** a mathematical model is created to minimize the maximum completion time(makespan)of all surgeres and patient waiting time,*** time by the fatigue effect is included in the surgery time,which is caused by doctors’long working ***,four mate-heuristics are optimized to address the relevant *** novel strategies are designed to improve the quality of the initial *** improve the convergence of the algorithms,seven local search operators are proposed based on the characteristics of the surgery scheduling ***,Q-learning is used to dynamically choose the optimal local search operator for the current state in each ***,by comparing the experimental results of 30 instances,the *** based local search strategy's effectiveness is *** all the compared algorithms,the improved artificial bee colony(ABC)with Q-leaming based local search has the best competiiveness.

关键词： surgery scheduling meta-heuristics Q-earning makespan

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