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arXiv 2024年

作者： Liu, Xinliang Yang, Xia Zhang, Chen-Song Zhang, Lian Zhao, Li Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Xiangtan University Hunan China LSEC ICMSEC Academy of Mathematics and System Sciences Chinese Academy of Sciences University of Chinese Academy of Sciences China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

This research investigates the application of Multigrid Neural Operator (MgNO), a neural operator architecture inspired by multigrid methods, in the simulation for multiphase flow within porous media. The architecture is adjusted to manage a variety of crucial factors, such as permeability and porosity heterogeneity. The study extendes MgNO to time-dependent porous media flow problems and validate its accuracy in predicting essential aspects of multiphase flows. Furthermore, the research provides a detailed comparison between MgNO and Fourier Neural Opeartor (FNO), which is one of the most popular neural operator methods, on their performance regarding prediction error accumulation over time. This aspect provides valuable insights into the models' long-term predictive stability and reliability. The study demonstrates MgNO's capability to effectively simulate multiphase flow problems, offering considerable time savings compared to traditional simulation methods, marking an advancement in integrating data-driven methodologies in geoscience applications. © 2024, CC BY.

关键词： Porous materials

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Securing Internet of Vehicles IoV Communications: A Biometric and Hash-Key Derivation Function HKDF-Based Approach

Securing Internet of Vehicles IoV Communications: A Biometri...

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Information technology (ACIT)

作者： Hanay Almomani Ayoub Alsarhan Mahmoud AlJamal Mohammad Aljaidi Tamam Alsarhan Bashar Khassawneh Abdelrahman Alfaqih Ghassan Samara Manish Kumar Singla Department of Information Technology The Hashemite University Zarqa Jordan Department of Cybersecurity Irbid National University Irbid Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia King Abdullah II School of IT University of Jordan Amman Jordan Department of Computer Science Amman Arab University Amman Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Interdisciplinary Courses in Engineering Chitkara University Punjab India

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

In light of advancements in the Internet of Things (IoT), the Internet of Vehicles (IoV) has emerged as a crucial component in developing autonomous vehicles. However, the rapid evolution of this technology has introduced significant security concerns. This paper addresses the growing threat of cyberattacks on autonomous vehicles that utilize wireless communication networks. The proposed model introduces a novel solution to safeguard driver privacy and secure network connections during vehicle operation by employing the driver’s biometric information and Hash-Key Derivation Function (HKDF) (HMAC-based Key Derivation Function) to generate a unique session key. This approach fills a critical research gap by enhancing the security of automated vehicle communication systems vulnerable to various cyber threats. The results demonstrate the effectiveness of the proposed method in creating a robust and unpredictable network key, significantly increasing the difficulty for attackers in compromising the system.

关键词： Biometrics Wireless communication Privacy Biological system modeling Security Information technology computer crime Autonomous vehicles Internet of Vehicles

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SELF-REGULATED BIOLOGICAL TRANSPORTATION STRUCTURES WITH GENERAL ENTROPY DISSIPATIONS, PART I: THE 1D CASE

arXiv

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arXiv 2023年

作者： Astuto, Clarissa Haskovec, Jan Markowich, Peter Portaro, Simone Mathematical and Computer Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 Vienna1090 Austria

We study self-regulating processes modeling biological transportation networks as presented in [15]. In particular, we focus on the 1D setting for Dirichlet and Neumann boundary conditions. We prove an existence and uniqueness result under the assumption of positivity of the diffusivity D. We explore systematically various scenarios and gain insights into the behavior of D and its impact on the studied system. This involves analyzing the system with a signed measure distribution of sources and sinks. Finally, we perform several numerical tests in which the solution D touches zero, confirming the previous hints of local existence in particular *** Codes 35A01, 45J99, 65N06 © 2023, CC BY.

关键词： Boundary conditions

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LSTM-based Dynamic Routing with non-ISL LEO Satellite Constellations for Remote IoT Connectivity

LSTM-based Dynamic Routing with non-ISL LEO Satellite Conste...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Aymen Hamrouni Hakim Ghazzai Gianluca Setti Lokman Sboui CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwa Saudi Arabia Department of Electrical Engineering (ESAT) Katholieke Universiteit Leuven Leuven Belgium Systems Engineering Department École de Technologie Supérieure (ETS) University of Québec Montréal Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper presents a novel sequence-to-sequence LSTM-based routing algorithm, designed for delay-sensitive remote IoT communication via both terrestrial and Low Earth Orbit (LEO) satellite relays when no inter-satellite links are available. We introduce a matrix-based approach to compute the communication delay between pairs of IoT nodes and satellites while considering their orbital passes and temporary visibility. Then, we develop an encoder-decoder architecture based on LSTM networks augmented with a beam-search strategy that enables the efficient prediction of optimal routing paths. Through experimental evaluations, incorporating a K − 2 beam search optimization, the algorithm demonstrates superior performance in generating near-optimal and scalable routes. Comparative analyses indicate that this approach outperforms traditional routing methods, offering lower data delays with reduced computational overhead.

关键词： Satellite constellations Satellites Transfer learning Low earth orbit satellites computer architecture Routing Prediction algorithms Orbits Delays Relays

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Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs

arXiv

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arXiv 2022年

作者： Chen, Jiayu Chen, Jingdi Lan, Tian Aggarwal, Vaneet The School of Industrial Engineering Purdue University West LafayetteIN47907 United States Computer Science King Abdullah University Of Science And Technology Thuwal23955 Saudi Arabia The Department of Electrical and Computer Engineering George Washington University WashingtonDC20052 United States

Covering skill (a.k.a., option) discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios, where only sparse reward signals are available. It aims to connect the most distant states identified through the Fiedler vector of the state transition graph. However, the approach cannot be directly extended to multi-agent scenarios, since the joint state space grows exponentially with the number of agents thus prohibiting efficient option computation. Existing research adopting options in multi-agent scenarios still relies on single-agent algorithms and fails to directly discover joint options that can improve the connectivity of the joint state space. In this paper, we propose a new algorithm to directly compute multi-agent options with collaborative exploratory behaviors while still enjoying the ease of decomposition. Our key idea is to approximate the joint state space as the Kronecker product of individual agents’ state spaces, based on which we can directly estimate the Fiedler vector of the joint state space using the Laplacian spectrum of individual agents’ transition graphs. This decomposition enables us to efficiently construct multi-agent joint options by encouraging agents to connect the sub-goal joint states which are corresponding to the minimum or maximum of the estimated joint Fiedler vector. Evaluation on multi-agent collaborative tasks shows that our algorithm can successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options, in terms of both faster exploration and higher cumulative rewards. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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RSGPT: A Remote Sensing Vision Language Model and Benchmark

arXiv

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arXiv 2023年

作者： Hu, Yuan Yuan, Jianlong Wen, Congcong Lu, Xiaonan Li, Xiang The DAMO Academy Alibaba Group Beijing China The Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates The University of Chinese Academy of Sciences Beijing China The King Abdullah University of Science and Technology Jeddah Saudi Arabia

The emergence of large-scale Large Language Models (LLMs), with GPT-4 as a prominent example, has significantly propelled the rapid advancement of Artificial General Intelligence (AGI) and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing, there is a growing interest in developing large vision language models (VLMs) specifically tailored for data analysis in this domain. However, current research predominantly revolves around visual recognition tasks, lacking comprehensive, large-scale image-text datasets that are aligned and suitable for training large VLMs, which poses significant challenges to effectively training such models for remote sensing applications. In computer vision, recent research has demonstrated that fine-tuning large vision language models on small-scale, high-quality datasets can yield impressive performance in visual and language understanding. These results are comparable to state-of-the-art VLMs trained from scratch on massive amounts of data, such as GPT-4. Inspired by this captivating idea, in this work, we build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the remote sensing field. Unlike previous remote sensing datasets that either employ model-generated captions or short descriptions, RSICap comprises 2,585 human-annotated captions with rich and high-quality information. This dataset offers detailed descriptions for each image, encompassing scene descriptions (e.g., residential area, airport, or farmland) as well as object information (e.g., color, shape, quantity, absolute position, etc). To facilitate the evaluation of VLMs in the field of remote sensing, we also provide a benchmark evaluation dataset called RSIEval. This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of remote sensing. We are actively engaged in expanding the scale of thes

关键词： Remote sensing

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Emerging Neuroimaging Approach of Hybrid EEG-fNIRS Recordings: Data Collection and Analysis Challenges

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Data science in science 2024年第1期3卷

作者： Pinto-Orellana, Marco Antonio Khan, Haroon Ombao, Hernando Mirtaheri, Peyman Biomedical Engineering Department University of California Irvine Irvine CA United States Department of Mechanical Electronics and Chemical Engineering Oslomet–Oslo Metropolitan University Oslo Norway Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

The hybrid EEG-fNIRS (electroencephalogram - functional near-infrared spectroscopy) modality provides a comprehensive understanding of brain activity by simultaneously capturing electrical and hemodynamic responses. It takes advantage of the temporal resolution in EEG with the good spatial resolution of fNIRS. This hybrid system is non-invasive, portable, and relatively affordable compared to functional magnetic resonance imaging (fMRI). Despite its inherent limitations, EEG-fNIRS systems can be a valuable tool in neuroimaging, suitable for cognitive and clinical research that requires temporal and spatial information about brain function. They are well-suited in naturalistic experimental settings, brain-computer interface applications, and disease diagnosis. This paper also explores electro-metabolic interactions in the motor cortex during left- and right-hand grip tasks. We analyzed cross-frequency coupling (CFC) between delta rhythms (0–4Hz) and oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations calculated from fNIRS. CFC was estimated as the correlation between HbO, HbR, and the instantaneous amplitude of delta signals. CFC cannot be studied with non-synchronous measurements and is not directly related to activation amplitudes. Based on previous fMRI-EEG studies, some categories of oscillations denote a negligible correlation with fNIRS signals. However, our results showed a contra-lateral coupling between delta-EEG and HbO with significant pattern differences in the left motor cortex. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： cross-frequency coupling data pre-processing electrical-hemodynamic coupling Hybrid EEG-fNIRS statistical modeling synchronization

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Profit aware Resource Allocation in Fog Computing: A Stackelberg Game Approach

Profit aware Resource Allocation in Fog Computing: A Stackel...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Akshita Doad Nitin Gupta Mohammad S. Obaidat Kuldeep Singh Jadon Piyush Rawat Kuie–Fang Hsiao Department of Computer Science and Engineering National Institute of Technology Hamirpur HP India Distinguished Professor at the King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China The Amity University Noida UP India University of Texas-Permain Basin Odessa Tx USA

Recently, Internet of Things (IoT) based smart systems have led to more latency-sensitive and bandwidth hungry IoT applications. Fog computing as an extension of Cloud computing fulfills those requirements more effectively. However, a key challenge for the fog computing is multiple resources allocation between heterogeneous Data Service Operators (DSOs) and End Users (EUs). The DSOs want to increase their revenues, however, due to lack of efficient resource allocation mechanisms, they often end up compromising Quality of Service (QoS) and violating Service Level Agreements (SLAs). It is very important for them to make a balance among user's expectations, expenses of providers and penalties for not providing services within the deadline. Hence, a profit-aware resource allocation policy using Stackelberg game is proposed in the work where DSO acts as leaders and EU as followers. The DSOs being the leaders, set the prices first, send it to the followers, and then the followers can compute their best demand for resources accordingly. Next, Stackelberg equilibrium for each type of resource is obtained to find the best resource demand strategy for an EU. The simulation results demonstrate the superiority of the proposed algorithm.

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Accelerating adoption of species-agnostic plant sensors for precision farming

Nature Reviews Electrical Engineering

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Nature Reviews electrical engineering 2025年 58-70页

作者： abdullah Bukhamsin Khaled Nabil Salama Jürgen Kosel Ikram Blilou Bioengineering Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Center of Excellence — Sustainable Food Security (SFS) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Sensor Systems Division Silicon Austria Labs Villach Austria Plant Science Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Precision farming is an optimized management farming scheme that seeks to link the real-time needs of crops with the nutrients to be administered. Sensing platforms that can monitor the physiological status of crops in situ are key to enabling timely and localized interventions. However, the underdevelopment of plant sensing strategies limits the potential of precision farming. In this Review, we discuss the challenges and advancements in phyto-monitoring, focusing on strategies that are applicable to a wide range of plant species and suitable for field deployment. We explore species-agnostic sensors, including optical and electrochemical sensors, whose operation is based on principles that are widely applicable to all plant species. These platforms enable real-time monitoring of the physiological state of crops by assessing key biomarkers, such as plant hormones, and metabolites such as salicylic acid and reactive oxygen species. Evaluating these systems, we conclude that an integrative sensing approach is necessary to compensate for the limitations of the individual methods and can provide a holistic view of crop health. Cost-effective species-agnostic sensors are thus needed to provide information that can be used to minimize the resource footprint of farming and meet the growing global demand.

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Feasibility analysis of 2-dimensional permutation vector optical code division multiple access passive optical network

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Optik 2023年 294卷

作者： Ullah, Rahat Ullah, Sibghat Imtiaz, Waqas Ahmad Alatawi, abdullah A. Alwageed, Hathal S. Ahmad, Ibrar Jianxin, Ren Institute of Optics and Electronics Nanjing University of Information Science & Technology Nanjing210044 China National Research Center for Optical Sensors/Communications Integrated Networks School of Electronic Science and Engineering Southeast University Nanjing210096 China Electrical Engineering Department University of Engineering and Technology Peshawar Pakistan King Abdulaziz City for Science and Technology Saudi Arabia College of Computer and Information Sciences Jouf University Saudi Arabia Computer Science Department University of Peshawar Pakistan

This paper analyzes the feasibility of a 2-dimensional (2D) permutation vector (PV) optical code division multiple access (OCDMA) passive optical network (PON) in terms of transmission capacity, network reliability, power budget, and the number of subscribers. The proposed PON is built with a novel 2D PV-OCDMA code having ideal auto- and cross-correlation properties. Furthermore, a ring-based topology at the feeder level and a tree-based topology at the distribution level are utilized to support ubiquitous computing. The proposed architecture also employs multiple remote nodes at the feeder level to facilitate adequate power budget and cardinality for high-capacity applications. Simulation results indicate that the proposed PON elevates system transmission capacity by eliminating phase-induced intensity noise and multiple access interference. Furthermore, reliability analysis using mathematical formalism demonstrates that the proposed architecture can provide a connection availability of ≈0.099993 (5 nines), which is significantly higher than the conventional tree-based topology. Power budget analysis also determines that the proposed PON can support well above 320 subscribers at adequate receiver sensitivity and optical network terminals (ONTs) per remote node. © 2023 Elsevier GmbH

关键词： Passive optical networks

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