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Modeling blockage in high directional wireless systems

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arXiv 2024年

作者： Koutsonas, Evangelos Boulogeorgos, Alexandros-Apostolos A. Trevlakis, Stylianos E. Ali, Tanweer Tsiftis, Theodoros A. Department of Electrical and Computer Engineering University of Western Macedonia Kozani50100 Greece Research & Development Department InnoCube P.C. Thessaloniki55535 Greece Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo315100 China

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker’s shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage. Copyright © 2024, The Authors. All rights reserved.

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HCA-NET: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling

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HCA-NET: Hierarchical Context Attention Network for Interver...

IEEE International Symposium on Biomedical Imaging

作者： Afshin Bozorgpour Bobby Azad Reza Azad Yury Velichko Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Machine and Hybrid Intelligence Lab Northwestern University Chicago IL USA Fraunhofer Institute for Digital Medicine MEVIS Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model’s geometric dependence on the spine. This loss function is designed to constrain the model’s predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The code-base is accessible to the public on GitHub.

关键词： Adaptation models Accuracy Spinal cord Semantics Pose estimation Predictive models Skeleton

Classification of Soil Images using Convolution Neural Networks

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Classification of Soil Images using Convolution Neural Netwo...

2021 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2021

作者： Chatterjee, Kajal Obaidat, Mohammad S. Samanta, Debabrata Sadoun, Balqies Islam, Sk Hafizul Chatterjee, Rajdeep University Department of Computer Science Karnataka Bangalore India University of Sharjah College of Computing and Informatics Sharjah27272 United Arab Emirates Al-Balqa' Applied University College of Engineering Al Salt Jordan Indian Institute of Information Technology Kalyani Department of CSE West Bengal 741235 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9781665432085

Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. © 2021 IEEE.

关键词： Soils

Unified Source-Free Domain Adaptation

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arXiv 2024年

作者： Tang, Song Su, Wenxin Ye, Mao Zhang, Jianwei Zhu, Xiatian The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The TAMS Group Department of Informatics Universität Hamburg Hamburg Germany The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China The Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

In the pursuit of transferring a source model to a target domain without access to the source training data, Source-Free Domain Adaptation (SFDA) has been extensively explored across various scenarios, including closed-set, open-set, partial-set, and generalized settings. Existing methods, focusing on specific scenarios, not only address only a subset of challenges but also necessitate prior knowledge of the target domain, significantly limiting their practical utility and deployability. In light of these considerations, we introduce a more practical yet challenging problem, termed unified SFDA, which comprehensively incorporates all specific scenarios in a unified manner. To tackle this unified SFDA problem, we propose a novel approach called Latent Causal Factors Discovery (LCFD). In contrast to previous alternatives that emphasize learning the statistical description of reality, we formulate LCFD from a causality perspective. The objective is to uncover the causal relationships between latent variables and model decisions, enhancing the reliability and robustness of the learned model against domain shifts. To integrate extensive world knowledge, we leverage a pre-trained vision-language model such as CLIP. This aids in the formation and discovery of latent causal factors in the absence of supervision in the variation of distribution and semantics, coupled with a newly designed information bottleneck with theoretical guarantees. Extensive experiments demonstrate that LCFD can achieve new state-of-the-art results in distinct SFDA settings, as well as source-free out-of-distribution generalization. Our code and data are available at https://***/tntek/source-free-domain-adaptation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Synergizing Airborne Non-Terrestrial Networks and Reconfigurable Intelligent Surfaces-Aided 6G IoT

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TechRxiv

TechRxiv 2023年

作者： Jamshed, Muhammad Ali Shin, Wonjae Kaushik, Aryan Toka, Mesut Shakir, Muhammad Zeeshan Dash, Soumya Prakash Dardari, Davide The College of Science and Engineering University of Glasgow United Kingdom The School of Engineering & Informatics University of Sussex United Kingdom The Department of Electrical and Computer Engineering Ajou University Korea Republic of The School of Electrical Engineering Korea University Korea Republic of The Department of Electrical and Computer Engineering Princeton University United States The School of Computing Engineering and Physical Sciences University of the West of Scotland United Kingdom The School of Electrical Sciences Indian Institute of Technology Bhubaneswar India The Department of Electrical Electronic and Information Engineering University of Bologna Italy

On the one hand, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising solution to meet the demand for higher data rates, improved coverage, and efficient spectrum utilization. On the other hand, Non-Terrestrial Networks (NTNs) offer unprecedented possibilities for global connectivity. Moreover, the NTN can also support the upsurge in the number of Internet of Things (IoT) devices by providing reliable and ubiquitous connectivity. Although NTNs have shown promising results, there are several challenges associated with their usage, such as signal propagation delays, interference, security, etc. In this article, we have discussed the possibilities of integrating RIS with an NTN platform to overcome the issues associated with NTN. Furthermore, through experimental validation, we have demonstrated that the RIS-assisted NTN can play a pivotal role in improving the performance of the entire communication system. © 2023, CC BY.

关键词： Internet of things

Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

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Strategic Planning and Knowledge Management Approaches as in...

8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

Fast Network Recovery from Large-Scale Disasters: A Resilient and Self-Organizing RAN Framework

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arXiv 2024年

作者： Yagan, M. Yaser Kayraklik, Sefa Kesir, Samed Sumen, Gizem Hokelek, Ibrahim Basaran, Mehmet Alexandropoulos, George C. Basar, Ertugrul Cavdar, Cicek Arslan, Huseyin Gorcin, Ali Lab TÜBİTAK-BİLGEM Kocaeli Turkey The Department of Electronics and Telecommunications Engineering Istanbul Technical University Istanbul Turkey The 6GEN Laboratory Istanbul34854 Turkey The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States Department of Electrical and Electronics Engineering Koç University Istanbul Sariyer Turkey The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The Department of Electrical and Electronics Engineering Istanbul Medipol University Istanbul34810 Turkey

Extreme natural phenomena are occurring more frequently everyday in the world, challenging, among others, the infrastructure of communication networks. For instance, the devastating earthquakes in Turkiye in early 2023 showcased that, although communications became an imminent priority, existing mobile communication systems fell short with the operational requirements of harsh disaster environments. In this article, we present a novel framework for robust, resilient, adaptive, and open source sixth generation (6G) radio access networks (Open6GRAN) that can provide uninterrupted communication services in the face of natural disasters and other disruptions. Advanced 6G technologies, such as reconfigurable intelligent surfaces (RISs), cell-free multiple-input-multiple-output, and joint communications and sensing with increasingly heterogeneous deployment, consisting of terrestrial and non-terrestrial nodes, are robustly integrated. We advocate that a key enabler to develop service and management orchestration with fast recovery capabilities will rely on an artificial-intelligence-based radio access network (RAN) controller. To support the emergency use case spanning a larger area, the integration of aerial and space segments with the terrestrial network promises a rapid and reliable response in the case of any disaster. A proof-of-concept that rapidly reconfigures an RIS for performance enhancement under an emergency scenario is presented and discussed. © 2024, CC BY-NC-ND.

关键词： Radio access networks

Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection

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arXiv 2023年

作者： Zhou, Wei Yue, Guanghui Zhang, Ruizeng Qin, Yipeng Liu, Hantao Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada School of Biomedical Engineering Shenzhen University Shenzhen518060 China Beijing Institute of Technology Beijing100081 China School of Computer Science and Informatics Cardiff University CardiffCF24 4AX United Kingdom

Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in this letter, we propose a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP). Specifically, we make the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation. Through this process, we tackle the issue of transmitting large-volume original point clouds to user-ends for quality assessment. Then, motivated by the characteristics of the human visual system (HVS), the objective quality scores of distorted point clouds are produced by combining content-oriented similarity and statistical correlation measurements. Finally, extensive experiments are conducted on SJTU-PCQA and WPC databases. The experimental results demonstrate that our proposed algorithm outperforms existing reduced-reference and no-reference quality metrics, and significantly reduces the performance gap between state-of-the-art full-reference quality assessment methods. In addition, we show the performance variation of each proposed technical component by ablation tests. © 2023, CC BY.

关键词： Luminance

LMI-Based Non-Iterative Design Strategies for Guaranteed Cost Static Output Feedback Control for Linear Systems

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LMI-Based Non-Iterative Design Strategies for Guaranteed Cos...

International Conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： Hidetoshi Oya Takuya Nakagawa Shunya Nagai Kazushi Nakano Department of Computer Science Tokyo City University Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

This paper proposes a non-iterative LMI-based design strategy for guaranteed cost static output feedback controllers for linear systems. The proposed design approach is based on the well-known necessary and sufficient condition for the existence of the static output feedback stabilizing control for linear systems. In this paper, for linear systems we show the non-iterative LMI-based design procedure for static output feedback controllers which minimize the upper bound of a given quadratic cost function, and the effectiveness of the proposed static output feedback controller with guaranteed cost is presented through a simple numerical example.

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