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IEEE Conference on Global Communications (GLOBECOM)

作者： Asmaa Abdallah Abdulkadir Celik Ahmed Alkhateeb Ahmed M. Eltawil Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA Department of Electrical Computer and Energy Engineering Arizona State University Tempe USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper presents an end-to-end (E2E) autoencoder learning framework that relies on unsupervised deep learning for the joint design of millimeter wave (mmWave) probing beams and hybrid precoding matrices in multi-user communication systems. Our model utilizes prior channel observations to achieve two main objectives: designing a compact set of probing beams and predicting off-grid radio frequency (RF) beamforming vectors. The E2E learning framework optimizes probing beams in an unsupervised manner, concentrating sensing power on promising spatial directions based on the environment. To this aim, we develop a neural network architecture respecting RF chain constraints and model received signal strength (RSS) using complex-valued convolutional layers. The autoencoder is trained to directly produce RF beamforming vectors for hybrid architectures based on projected RSS indicators (RSSIs). Once RF beamforming vectors for multi-users are predicted, baseband digital precoders are designed by accounting for multi-user interference. The autoencoder neural network is trained E2E in an unsupervised manner with a customized loss function aimed at maximizing RSS. In a system with 64 antennas, 4 RF chains, and 4 users, our approach requires only 8 probing beams to design RF beamforming vectors, compared to the conventional predefined codebooks with 64 or 128 beams.

关键词： Radio frequency Array signal processing Precoding Autoencoders Neural networks Millimeter wave technology Interference Predictive models Vectors Millimeter wave communication

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Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals

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IFAC-PapersOnLine 2024年第24期58卷 579-583页

作者： Israel Jesus Santos Filho Muhammad Mahboob Ur Rahman Taous-Meriem Laleg-Kirati Tareq Al-Naffouri Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia The National Institute for Research in Digital Science and Technology Paris-Saclay France

We present for the first time a novel method that utilizes the chest movement-modulated radio signals for non-contact acquisition of the photoplethysmography (PPG) signal. Under the proposed method, a software-defined radio (SDR) exposes the chest of a subject sitting nearby to an orthogonal frequency division multiplexing signal with 64 sub-carriers at a center frequency 5.24 GHz, while another SDR in the close vicinity collects the modulated radio signal reflected of the chest. This way, we construct a custom dataset by collecting 160 minutes of labeled data (both raw radio data as well as the reference PPG signal) from 16 healthy young subjects. With this, we first utilize principal component analysis for dimensionality reduction of the radio data. Next, we denoise the radio signal and reference PPG signal using wavelet technique, followed by segmentation and Z-score normalization. We then synchronize the radio and PPG segments using cross-correlation method. Finally, we proceed to the waveform translation (regression) task, whereby we first convert the radio and PPG segments into frequency domain using discrete cosine transform (DCT), and then learn the non-linear regression between them. Eventually, we reconstruct the synthetic PPG signal by taking inverse DCT of the output of regression block, with a mean absolute error of 8.1294. The synthetic PPG waveform has a great clinical significance as it could be used for non-contact performance assessment of cardiovascular and respiratory systems of patients suffering from infectious diseases, e.g., covid19.

关键词： Non-contact methods RF-based methods software-defined radio PPG deep learning

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On the Optimal Expressive Power of ReLU DNNs and Its Application in Approximation with Kolmogorov Superposition Theorem

arXiv

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arXiv 2023年

作者： He, Juncai Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

This paper is devoted to studying the optimal expressive power of ReLU deep neural networks (DNNs) and its application in approximation via the Kolmogorov Superposition Theorem. We first constructively prove that any continuous piecewise linear functions on [0, 1], comprising O(N2L) segments, can be represented by ReLU DNNs with L hidden layers and N neurons per layer. Subsequently, we demonstrate that this construction is optimal regarding the parameter count of the DNNs, achieved through investigating the shattering capacity of ReLU DNNs. Moreover, by invoking the Kolmogorov Superposition Theorem, we achieve an enhanced approximation rate for ReLU DNNs of arbitrary width and depth when dealing with continuous functions in high-dimensional *** Codes 41A30, 68T07, 65D40 Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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An Explicit Discontinuous Galerkin Time-Domain Scheme to Simulate Kerr Nonlinearity

An Explicit Discontinuous Galerkin Time-Domain Scheme to Sim...

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Antennas and Propagation Society International Symposium

作者： Ruitao Sun Ming Dong Liang Chen Hakan Bagci Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

A discontinuous Galerkin time-domain (DGTD) method is formulated and implemented to analyze transient electromagnetic field interactions in a medium with Kerr nonlinearity. The (nonlinear) Maxwell equations in conservative form are discretized using a DG scheme that employs the Harten-Lax-van Leer numerical flux to facilitate the information exchange between discretization elements. A slope limiter is introduced into the DGTD method to prevent generation of non-physical oscillations in the solution and maintain the stability of the explicit time marching. Numerical examples, which demonstrate the applicability and the accuracy of the proposed method, are presented.

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Predicting entropy and heat capacity of hydrocarbons using machine learning

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Energy and AI 2021年第2期4卷 161-171页

作者： Mohammed N.Aldosari Kiran K.Yalamanchi Xin Gao S.Mani Sarathy Clean Combustion Research Center Physical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Computational Bioscience Research Center ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia

Chemical substances are essential in all aspects of human life,and understanding their properties is essential for developing chemical *** properties of chemical species can be accurately obtained by experiments or ab initio computational calculations;however,these are time-consuming and *** this work,machine learning models(ML)for estimating entropy,S,and constant pressure heat capacity,Cp,at 298.15 K,are developed for alkanes,alkenes,and *** training data for entropy and heat capacity are collected from the *** descriptors generated using alvaDesc software are used as input features for the ML *** vector regression(SVR),v-support vector regression(v-SVR),and random forest regression(RFR)algorithms were trained with K-fold cross-validation on two *** first level assessed the models’performance,and the second level generated the final *** the three ML models chosen,SVR shows better performance on the test *** SVR model was then compared against traditional Benson’s group additivity to illustrate the advantages of using the ML ***,a sensitivity analysis is performed to find the most critical descriptors in the property estimations.

关键词： Entropy Heat capacity Molecular descriptors Machine learning Supervised learning Hydrocarbons

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Multipath Detection and Mitigation from GNSS Observations Using Antenna Arrays

Multipath Detection and Mitigation from GNSS Observations Us...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Mohanad Ahmed Tarig Ballal Xing Liu Tareq Y. Al-Naffouri Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

We consider the problem of detecting and mitigating the effect of multipath on GNSS observations. In particular, we focus on carrier-phase observations that are collected at an array of GNSS antennas. We exploit a special antenna array geometry, synchronization among the GNSS receivers, and an attitude estimate provided by an attitude filter or an IMU, to develop a technique to identify the satellite observations that are contaminated with multipath. The proposed technique leverages the antenna geometry to rabidly estimate the attitude parameters for various satellite combinations. Next, a dedicated decision-making algorithm is used to identify the satellite observations affected by multipath. Our simulation results demonstrate the effectiveness of the proposed approach in detecting the occurrence of multipath with high success rates. By rejecting the multipath-affected observations, we show remarkable performance gains when attitude determination is considered as an example application.

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Extremely large water droplet impact onto a deep liquid pool

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Physical Review E 2024年第4期109卷 045107-045107页

作者： Sandip Dighe Dilip Kumar Maity Jeffrey N. Fonnesbeck Som Dutta Tadd Truscott Mechanical Engineering Program Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal 23955 Kingdom of Saudi Arabia Department of Mechanical Engineering Utah State University Logan Utah 84322 USA

Most studies of droplet impact on liquid pools focus on droplet diameters up to the capillary length (0.27 cm). We break from convention and study extremely large water droplets (1 to 6 cm diameter) falling into a pool of water. We demonstrate that the depth and width of the cavity formed by large droplet impact is greatly influenced by the deformed shape of the droplet at impact (i.e., prolate, spherical, and oblate), and larger droplets amplify this behavior by flattening before impact. In particular, the maximum cavity depth is a function of the Froude number and axis ratio of the droplet just before impact. Further, the cavity depth is more dependent on the droplet height than width, and the maximum cavity diameter is independent of the droplet height. In general, we observe that more oblate droplets result in decreasing cavity depths for a fixed liquid volume. This is because an increase in horizontal droplet diameter results in a reduced impact energy flux and therefore reduced cavity depth.

关键词： Capillary interactions Drop breakup Drops & bubbles Thin fluid films Wakes & jets Water

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An SIE-GSTC Solver for Simulation of Monoanisotropic Metasurfaces

An SIE-GSTC Solver for Simulation of Monoanisotropic Metasur...

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Antennas and Propagation Society International Symposium

作者： Sebastian Celis Sierra Ran Zhao Rui Chen Hakan Bagci Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

A surface integral equation (SIE) solver is proposed to simulate electromagnetic field interactions on three-dimensional arbitrarily-shaped monoanisotropic metasurfaces. The metasurface is modeled as an infinitely thin sheet on which generalized sheet transition conditions (GSTCs) are enforced. These conditions “connect” the equivalent electric and magnetic surface currents on two sides of the metasurface using susceptibility tensors that characterize the electromagnetic response of the metasurface. A coupled system of equations in unknown surface current densities is obtained by linearly combining electric and magnetic field integral equations on two sides of the metasurface and GSTCs.

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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

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computer Systems science & engineering 2023年第8期46卷 1533-1547页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Civil Engineering Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop *** agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental *** control has become one of the significant problems in the agricultural *** traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same *** more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed *** may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable *** study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision *** HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision *** image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise *** addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature *** detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection *** experimental results of the HHOGCN-WD technique are investigated under the benchmark *** results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

关键词： Weed detection precision agriculture graph convolutional network harris hawks optimizer hyperparameter tuning

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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