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Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

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Reconfigurable Intelligent Computational Surfaces for MEC-As...

IEEE Conference on Wireless Communications and Networking

作者： Bo Yang Xueyao Zhang Zhiwen Yu Xuelin Cao Chongwen Huang George C. Alexandropoulos Yan Zhang Mérouane Debbah Chau Yuen School of Computer Science Northwestern Polytechnical University Xi'an Shaanxi China School of Cyber Engineering Xidian University Xi'an Shaanxi China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Informatics University of Oslo Oslo Norway Center for 6G Technology the Khalifa University of Science and Technology Abu Dhabi UAE School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

关键词： Simulation Vehicle-to-infrastructure Vehicular ad hoc networks Transforms Safety Servers Task analysis

Quantum-noise-limited optical neural networks using few photons per neuron activation

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Quantum-noise-limited optical neural networks using few phot...

Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Ma, Shi-Yuan Wang, Tianyu Laydevant, Jérémie Wright, Logan G. McMahon, Peter L. School of Applied and Engineering Physics Cornell University IthacaNY14853 United States USRA Research Institute for Advanced Computer Science Mountain ViewCA94035 United States NTT Physics and Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

ISBN: (纸本)9781957171296

We pushed the optical energy consumption of optical neural networks to a new regime. Despite dominant quantum noise, we experimentally achieved accurate image classification using 0.008 photons/MAC, demonstrating deterministic machine-learning tasks with ultra-low-power stochastic systems. © Optica Publishing Group 2023, © 2023 The Author(s)

关键词： Biomedical imaging Laser systems Neural networks Optical neural systems Quantum noise Stochastic processes

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

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arXiv 2023年

作者： Jamshed, Muhammad Ali Kaushik, Aryan Toka, Mesut Shin, Wonjae Shakir, Muhammad Zeeshan Dash, Soumya P. 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

Wideband Multi-User MIMO Communications with Frequency Selective RISs: Element Response Modeling and Sum-Rate Maximization

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arXiv 2022年

作者： Katsanos, Konstantinos D. Shlezinger, Nir Imani, Mohammadreza F. Alexandropoulos, George C. Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel School of Electrical Computer and Energy Engineering Arizona State University United States

Reconfigurable Intelligent Surfaces (RISs) are an emerging technology for future wireless communication systems, enabling improved coverage in an energy efficient manner. RISs are usually metasurfaces, constituting of two-dimensional arrangements of metamaterial elements, whose individual response is commonly modeled in the literature as an adjustable phase shifter. However, this model holds only for narrowband communications, and when wideband transmissions are utilized, one has to account for the frequency selectivity of metamaterials, whose response usually follows a Lorentzian-like profile. In this paper, we consider the uplink of a wideband RIS-empowered multi-user Multiple-Input Multiple-Output (MIMO) wireless system with Orthogonal Frequency Division Multiplexing (OFDM) signaling, while accounting for the frequency selectivity of RISs. In particular, we focus on designing the controllable parameters dictating the Lorentzian response of each RIS metamaterial element, in order to maximize the achievable sum rate. We devise a scheme combining block coordinate descent with penalty dual decomposition to tackle the resulting challenging optimization framework. Our simulation results reveal the achievable rates one can achieve using realistically frequency selective RISs in wideband settings, and quantify the performance loss that occurs when using state-of-the-art methods which assume that the RIS elements behave as frequency-flat phase shifters. © 2022, CC BY-NC-ND.

关键词： Broadband networks

Augmented Reality Display of Robot with Graphs of Property Response Using Its USD Model

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Augmented Reality Display of Robot with Graphs of Property R...

International Conference on Control, Automation and Systems ( ICCAS)

作者： Kazuki Tsukamoto Masanobu Koga Department of Creative Informatics Kyushu Institute of Technology Department of Intelligent and Control Systems Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology

This study proposes a method that can easily grasp the relationship between the actual machine and the graphs. In recent years, there has been a lot of research on augmented reality displays. The fields of research range from education to welfare. In the development of control systems, when evaluating the performance of a system by simulation or experiment, the results are often checked as graphs. Since the graphs are checked on a PC using CAD or other means, it is difficult to know which part of the actual machine each graph corresponds to. Therefore, we developed a tool that displays graphs in augmented reality around the actual machine through a camera on a mobile terminal. To display graphs in augmented reality, it is important to obtain the coordinates of the actual machine and display them in a location associated with the device. Therefore, a USD model with the same shape and size as the actual machine is used. This is achieved by displaying the USD model in augmented reality so that it is superimposed on the actual machine. The accuracy of the tool was also examined and its usefulness was evaluated.

关键词： Solid modeling Automation Shape Robot kinematics Robot vision systems Education Control systems

KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition

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arXiv 2022年

作者： Oinar, Chingis Binh, M.L.E. Woo, Simon S. Mercari Tokyo106-6118 Japan Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon16419 Korea Republic of

Feature learning is a widely used method for large-scale face recognition tasks. Recently, large-margin softmax loss methods have demonstrated significant improvements in deep face recognition. However, these methods typically propose fixed positive margins to enforce intra-class compactness and inter-class diversity, without considering imbalanced learning issues that arise due to different learning difficulties or the number of training samples available in each class. This overlook not only compromises the efficiency of the learning process but, more critically, the generalization capability of the resultant models. To address this problem, we introduce a novel adaptive strategy called KappaFace, which modulates the relative importance of each class based on its learning difficulty and imbalance. Drawing inspiration from the von Mises-Fisher distribution, KappaFace increases the margin values for the challenging or underrepresented classes and decreases that of more well-represented classes. Comprehensive experiments across eight cutting-edge baselines and nine well-established facial benchmark datasets strongly confirm the advantage of our method. Notably, we observed an enhancement of up to 0.5% on the verification task when evaluated on the IJB-B/C datasets. In conclusion, KappaFace offers a novel solution that effectively tackles imbalanced learning in deep face recognition tasks and establishes a new baseline. Copyright © 2022, The Authors. All rights reserved.

关键词： Face recognition

Arabic Sentiment Analysis of Food Delivery Services Reviews

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Arabic Sentiment Analysis of Food Delivery Services Reviews

International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Safaa M. Khabour Ahmed S. Shatnawi Eyad Taqieddin Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Software Engineering Jordan University of Science & Technology Irbid Jordan Dept. of Cyber Security School of Computing and Informatics Al-Hussein Technical University Amman Jordan

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.

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Active Disturbance Rejection Control for 3D Crane Systems 11

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Active Disturbance Rejection Control for 3D Crane Systems

11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Roman, Raul-Cristian Precup, Radu-Emil Petriu, Emil M. Politehnica University of Timisoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 Timisoara300223 Romania Center for Fundamental and Advanced Tech. Res. Romanian Academy - Timisoara Br. Bd. Mihai Viteazu 24 Timisoara300223 Romania University of Ottawa School of Electrical Engineering and Computer Science 800 King Eduard OttawaONK1N 6N5 Canada

Active Disturbance Rejection Control (ADRC) is a data-driven algorithm that offers a promising approach for robust control design. This paper investigates the effectiveness of first-order and second-order ADRC for 3D cranes. To compare the results fairly and reduce the heuristics in parameter tuning, the ADRC parameters were optimally computed via the African Vultures Optimization Algorithm. Both controllers based on ADRC are validated using experiments on a 3D crane laboratory system and their design and performance evaluation are done by minimizing the sum of squared errors. The results demonstrate the robustness of the ADRCs. This comparative analysis highlights the advantages and limitations of both control strategies, providing valuable insights for selecting the appropriate ADRC order for specific applications. © 2024 The Authors.

关键词： Disturbance rejection

INFERRING PROBABILISTIC BOOLEAN NETWORKS FROM STEADY-STATE GENE DATA SAMPLES

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arXiv 2022年

作者： Šliogeris, Vytenis Maglaras, Leandros Moschoyiannis, Sotiris The School of Computer Science & Electronic Engineering University of Surrey GU2 7XH Singapore The School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Probabilistic Boolean Networks have been proposed for estimating the behaviour of dynamical systems as they combine rule-based modelling with uncertainty principles. Inferring PBNs directly from gene data is challenging however, especially when data is costly to collect and/or noisy, e.g., in the case of gene expression profile data. In this paper, we present a reproducible method for inferring PBNs directly from real gene expression data measurements taken when the system was at a steady state. The steady-state dynamics of PBNs is of special interest in the analysis of biological machinery. The proposed approach does not rely on reconstructing the state evolution of the network, which is computationally intractable for larger networks. We demonstrate the method on samples of real gene expression profiling data from a well-known study on metastatic melanoma. The pipeline is implemented using Python and we make it publicly available. © 2022, CC BY.

关键词： Complex networks