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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Ltaief, Hatem Hong, Yuxi Wilson, Leighton Jacquelin, Mathias Ravasi, Matteo Keyes, David Elliot Extreme Computing Research Center Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Cerebras Systems Inc. SunnyvaleCA United States

ISBN: (纸本)9798400701092

We exploit the high memory bandwidth of AI-customized Cerebras CS-2 systems for seismic processing. By leveraging low-rank matrix approximation, we fit memory-hungry seismic applications onto memory-austere SRAM wafer-scale hardware, thus addressing a challenge arising in many wave-equation-based algorithms that rely on Multi-Dimensional Convolution (MDC) operators. Exploiting sparsity inherent in seismic data in the frequency domain, we implement embarrassingly parallel tile low-rank matrix-vector multiplications (TLR-MVM), which account for most of the elapsed time in MDC operations, to successfully solve the Multi-Dimensional Deconvolution (MDD) inverse problem. By reducing memory footprint along with arithmetic complexity, we fit a standard seismic benchmark dataset into the small local memories of Cerebras processing elements. Deploying TLR-MVM execution onto 48 CS-2 systems in support of MDD gives a sustained memory bandwidth of 92.58PB/s on 35, 784, 000 processing elements, a significant milestone that highlights the capabilities of AI-customized architectures to enable a new generation of seismic algorithms that will empower multiple technologies of our low-carbon future. © 2023 Owner/Author(s).

关键词： Energy efficiency

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A Study on Malicious Attacks for RIS-Aided Wireless Systems

A Study on Malicious Attacks for RIS-Aided Wireless Systems

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IEEE Middle East and North Africa Communications Conference (MENACOMM)

作者： Ammar El Falou Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9798331519957

ISBN: (纸本)9798331519964

Reconfigurable intelligent surfaces (RIS) aim to reconfigure the random channel between the source and the destination, thus directing the transmitted signals toward the destination. The potential gains of RIS-aided systems compared to legacy systems are an increase in rate, energy efficiency, and energy transfer. A RIS should be low-complex and quasi-passive, therefore, does not have a high computational capability. The source configures the phases of the RIS elements by sending a signal to its controller to maximize the required metrics, such as the received power, the rate, the signal-to-noise ratio (SNR), etc. However, the channel between the source and the RIS controller is vulnerable. In this paper, we discuss the security issues in RIS-aided wireless systems. We focus on the channel between the source and the RIS controller. We show that a severe decrease in the destination rate, to a value lower than the one of the system without RIS, can be made by controlling the phase shifts of the RIS. With a realistic 3GPP channel model and an RIS with 100 elements, the rate can be decreased from 4.55 bps/Hz to only 0.08 bps/Hz by selecting the RIS phase shifts to minimize the received power (power loss around 26 dB). Future directions and challenges are also discussed, such as energy deviation, multiple RISs, and multiple operators.

关键词： Wireless communication Measurement Energy exchange Prevention and mitigation Reconfigurable intelligent surfaces Minimization Security Jamming Gain Signal to noise ratio

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Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

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computers, Materials & Continua 2022年第5期71卷 2497-2513页

作者： Mohammad Alamgeer Fahd N.Al-Wesabi Huda G.Iskandar Imran Khan Nadhem Nemri Mohammad Medani Mohammed abdullah Al-Hagery Ali Mohammed Al-Sharafi Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication *** this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed *** researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English ***,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any *** has been implemented using PHP with VS code *** robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and *** SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

关键词： Watermarking soft computing text analysis hidden Markov model content authentication

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Privacy Preservation in Delay-Based Localization Systems: Artificial Noise or Artificial Multipath?

Privacy Preservation in Delay-Based Localization Systems: Ar...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yuchen Zhang Hui Chen Henk Wymeersch Electrical and Computer Engineering King Abdullah University of Sciences and Technology Saudi Arabia Department of Electrical Engineering Chalmers University of Technology Sweden

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Localization plays an increasingly pivotal role in 5G/6G systems, enabling various applications. This paper focuses on the privacy concerns associated with delay-based localization, where unauthorized base stations attempt to infer the location of the end user. We propose a method to disrupt localization at unauthorized nodes by injecting artificial components into the pilot signal, exploiting model mismatches inherent in these nodes. Specifically, we investigate the effectiveness of two techniques, namely artificial multipath (AM) and artificial noise (AN), in mitigating location leakage. By leveraging the misspecified Cramer-Rao bound framework, we evaluate the impact of these techniques on unauthorized localization performance. Our results demonstrate that pilot manipulation significantly degrades the accuracy of unauthorized localization while minimally affecting legitimate localization. Moreover, we find that the superiority of AM over AN varies depending on the specific scenario.

关键词： Location awareness Degradation Privacy Cramer-Rao bounds Noise Estimation Numerical models Delays Global communication Uplink

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Approximation of arbitrarily high-order PDEs by first-order hyperbolic relaxation

arXiv

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arXiv 2024年

作者： Ketcheson, David I. Biswas, Abhijit Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

We present a framework for constructing a first-order hyperbolic system whose solution approximates that of a desired higher-order evolution equation. Constructions of this kind have received increasing interest in recent years, and are potentially useful as either analytical or computational tools for understanding the corresponding higher-order equation. We perform a systematic analysis of a family of linear model equations and show that for each member of this family there is a stable hyperbolic approximation whose solution converges to that of the model equation in a certain limit. We then show through several examples that this approach can be applied successfully to a very wide range of nonlinear PDEs of practical interest. Copyright © 2024, The Authors. All rights reserved.

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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A Multi-Armed Bandit Approach for User-Target Pairing in NOMA-Aided ISAC

A Multi-Armed Bandit Approach for User-Target Pairing in NOM...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Ahmed Nasser Abdulkadir Celik Ahmed M. Eltawil Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division at King Abdullah University of Science and Technology (KAUST) Thuwal KSA

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

In this paper, we propose a robust interference management approach for the integrated sensing and communication (ISAC) system that employs non-orthogonal multiple access (NOMA) for multiplexing. Our proposed approach effectively addresses interference challenges by optimizing the pairing of communication users (CUs) and radar targets (RTs) while simultaneously designing receiving beamformers. These optimizations aim to maximize the combined utility of communication rates and the radar estimation information rate (REIR), inherently constituting a challenging non-convex combinatorial problem. To tackle this intricate problem, we employ the upper confidence bound (UCB) algorithm, a powerful online learning technique rooted in multi-armed bandit (MAB) theory. Along with UCB, we harness zeroforcing beamforming to optimize the receiving beamformer. The numerical results underscore the importance of CU-RT pairing, with a $65 \%$ average performance improvement over traditional NOMA-ISAC and OMA-ISAC, close to the exhaustive search performance by only $2 \%$. It also substantially reduces complexity, with about $90 \%$ less computational complexity than exhaustive search.

关键词： Multiplexing NOMA Simulation Interference Radar Integrated sensing and communication Copper Optimization Land mobile radio Information rates

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Near-Field Channel Estimation for Ultra-Massive MIMO Antenna Array with Hybrid Architecture

Near-Field Channel Estimation for Ultra-Massive MIMO Antenna...

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IEEE Conference on Wireless Communications and Networking

作者： Ahmed Hussain Asmaa Abdallah Ahmed M. Eltawil Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Ultra massive multiple-input-multiple-output (UM-MIMO) has emerged as a prospective capacity-enhancing technology for next generation (NG) networks. To harness its full potential, accurate channel estimation with low pilot overhead becomes paramount. The combination of large antenna arrays and high frequency bands transitions the wireless communication from the far-field to the near-field realm. This transition presents a unique challenge, as channel sparsity in the angular domain becomes unattainable in the presence of near-field spherical wavefronts. Nonetheless, polar-domain sparsity is achievable, allowing existing near-field channel estimation methods to use polar codebooks over the classical angle-based codebooks. However, a major challenge in utilizing polar domain sparsity is the size of the polar codebook, demanding sampling of both distance and angle points across the entire near-field. In this work, we investigate near-field channel response in the beam space domain to redefine the boundaries of polar domain sparsity. We propose a novel polar codebook leveraging our results of sparsity analysis and beamfocusing property of near-field. Unlike existing work, the proposed polar codebook design is agnostic to user range information and has considerably reduced dimensions. Exploiting the new polar codebook, we present the beam focused simultaneous orthogonal matching pursuit (BF-SOMP) algorithm for efficient near-field channel estimation. Simulation results demonstrate that the proposed algorithm surpasses contemporary methods in terms of normalized mean square error (NMSE) and reduced computational complexity.

关键词： Wireless communication Simulation Precoding Channel estimation Matching pursuit algorithms Mean square error methods Sensors

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Online DRL-based Beam Selection for RIS-Aided Physical Layer Security: An Experimental Study

Online DRL-based Beam Selection for RIS-Aided Physical Layer...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ahmed Nasser Abdulkadir Celik Asmaa Abdallah David Lago Cachón Ruiqi Wang Yiming Yang Atif Shamim Ahmed M. Eltawil Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The integration of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) significantly enhances physical layer security (PLS) in wireless networks, provided that RIS’s phase shifts are precisely optimized to prevent security vulnerabilities. This paper introduces a reinforcement learning (RL)based algorithm designed to optimize the phase shifts in RIS-partitioning-aided PLS systems operating in the millimeter wave (mm-Wave), without requiring channel state information (CSI) for any users. The RL algorithm optimizes the phase shifts by efficiently selecting the best beam from a predefined codebook for different partitions, which simultaneously enhances the intended signal for legitimate users and increases the effectiveness of AN on eavesdroppers, thereby maximizing the system’s secrecy capacity (SC) and addressing the inherent non-convex challenges. Additionally, the paper details the development of an experimental testbed that provides essential data to refine the algorithm. The numerical results from the testbed highlight the significant impact of RIS partitioning in PLS, which can enhance the SC by an average of 55% over the full RIS scenario, and confirm the effectiveness of the RL-based algorithm in reducing computational complexity by approximately 80% compared to the exhaustive search algorithm.

关键词： Wireless networks Search methods Reinforcement learning Reconfigurable intelligent surfaces Physical layer security Approximation algorithms Partitioning algorithms Security Optimization Signal to noise ratio

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