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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Lu, Haoliang Amara, Selma Moussi, Khalil Benmbarek, Sofiane Ainine, Ahmad Fariborzi, Hossein King Abdullah University of Science and Technology Computer Electrical and Mathematical Science and Engineering Division Thuwal Saudi Arabia King Abdullah University of Science and Technology Consultant Pediatrician KAUST Health Clinic Thuwal Saudi Arabia

ISBN: (纸本)9781665441544

This paper introduces an electromagnetic micropump for drug delivery applications. The diffuser-nozzle structure allows a valveless unidirectional flow. The performance of the electromagnetic actuator is investigated theoretically and evaluated experimentally. More importantly, we present a proper compromise between the device miniaturization and the electromagnetic pumping force for implantable drug delivery applications. For an input current and voltage of 160 mA and 4.6 V, respectively, the micropump produces a flow rate of 1.2174 ± 0.0041 μl/s. The low power consumption, small size, and precise flow rate make the proposed micropump suitable for implantable drug delivery applications. © 2022 IEEE.

关键词： Controlled drug delivery

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Hybrid Solution to Remediate Groundwater Contaminated by Petroleum-Hydrocarbons

Hybrid Solution to Remediate Groundwater Contaminated by Pet...

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2024 SPE Water Lifecycle Management Conference and Exhibition, WLMC 2024

作者： Alahmari, M.M. Humam, A.A. Zefzafy, I.M. Sanchez-Huerta, C. Hong, P.-Y. Zhang, S. Saudi Aramco's EXPEC Advanced Research Center Dhahran Saudi Arabia Environmental Science and Engineering Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9781959025320

Groundwater contamination by petroleum-hydrocarbons is a serious environmental problem. Crude oil is a complex mixture of hydrocarbons with serious environmental and health risks. Thus, remediation of groundwater from petroleum hydrocarbon contamination is an emerging priority. Current approaches to remediate hydrocarbon contamination include physical and chemical methods. However, most of these approaches have a limited application for in-situ groundwater remediation. This study aims to develop a sustainable hybrid solution for efficient restoration of groundwater polluted by crude oil, providing a source of high-quality groundwater stream. Hybrid solution compromises in-situ addition of biosurfactant followed by a flow through electrochemical reactor installed in the groundwater well. The proposed hybrid solution comprises a two-stage process evaluated through lab-scale experiments treating crude oil that was mixed with synthetic water, mimicking groundwater contamination by petroleum-hydrocarbons. For biosurfactant optimization, glass flasks containing synthetic groundwater and crude oil were supplemented with biosurfactant BS, and C added at the three surfactants: oil (S:O) ratios 1:5, 1:10, and 1:50. Two temperature (25-35 °C) conditions were analyzed to simulate groundwater environment. The change in the crude oil layer thickness total petroleum hydrocarbon concentration (TPH) was continuously monitored for 60 days. Electron Oxidation was carried out where Boron-dopped diamond (BDD) anode and titanium cathode plates, were fitted into a 1 L reactor containing groundwater, crude oil and biosurfactant. Kinetic analysis at three constant currents (20, 30 and 40 mA/cm2) was performed. Samples were collected at regular intervals along 120 min to determine changes in TPH, COD and pH. The performance of biosurfactant BS and C in reducing the thickness of crude oil layer was influenced by different parameters including temperature, and S:O ratio. The increase in temper

关键词： Groundwater

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REORDER++: Enhanced Randomized Real-Time Scheduling Strategy Against Side-Channel Attacks

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IEEE Transactions on Network science and engineering 2023年第6期10卷 3253-3266页

作者： Ren, Jiankang Wang, Zheng Lin, Chi Obaidat, Mohammad S. Xie, Hongrui Zhu, Haihui Liu, Chunxiao Wang, Kaiwen Tan, Guozhen School of Computer Science and Technology Dalian116024 China Dalian University of Technology School of Software Dalian116024 China University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University Honorary Distinguished Professor Noida201301 India

Embedded real-time systems are widely adopted in safety-critical domains such as aircrafts, automobiles and space vehicles. Unfortunately, with the sharp rise in the use of common-off-the-shelf components in systems and the drive towards remote communication through untrusted networks, such as WiFi, radio or cellular, the security is increasingly becoming the key consideration in real-time system design. In particular, the real-time system is vulnerable to side-channel attacks from the external networks, which attempt to infer the timing of task execution by exploiting the system deterministic execution patterns. In this article, we present an enhanced online randomized scheduling strategy (named REORDER++), which breaks the deterministic task execution pattern of systems by random priority inversions at run-time to counteract the timing side-channel attacks in dynamic-priority real-time systems. In order to realize the feasible priority inversions under real-time constraint, we propose an online priority inversion test to increase the opportunity of tasks' priority inversions by judging the feasibility of tasks' priority inversions at run-time. Owing to such online priority inversion test, REORDER++ can generate highly randomized schedule of real-time tasks to mitigate the side-channel attack vulnerability. Experiments with synthesized task sets show that REORDER++ significantly outperforms the existing approaches in terms of schedule randomness. © 2013 IEEE.

关键词： Real time systems

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Neural Bayes estimators for censored inference with peaks-over-threshold models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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Low-Cost Hardware Platform for Testing ML-Based Edge Power Grid Oscillation Detectors 10

Low-Cost Hardware Platform for Testing ML-Based Edge Power G...

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10th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems, MSCPES 2022

作者： Dorado-Rojas, Sergio A. Xu, Shunyao Vanfretti, Luigi Olvera, Galilea Ayachi, M. Ilies I. Ahmed, Shehab Rensselaer Polytechnic Institute Electrical Computer and Systems Engineering Troy United States King Abdullah University of Science and Technology Electrical and Computer Engineering Thuwal Saudi Arabia

ISBN: (纸本)9781665468657

This paper introduces a low-cost hardware testing platform designed to investigate the performance of a Machine Learning (ML)-based edge application developed to detect forced oscillations in power grids. The core of the ML application lies in a Convolutional Neural Network (CNN) model deployed on an ML edge device (NVIDIA Jetson TX2). The proposed platform consists of a method for real-time signal emulation using the WaveForms Software Development Kit (SDK) that defines low-voltage signals generated by Digilent's Analog Discovery Board. The output of the signal generator is read by the Jetson board using an Analog-to-Digital Converter (ADC). Our experiments compare the performance of different ADCs when performing inference with the same CNN model. Additionally, we give an overview of the communication scheme that allows experiment automation, which is particularly useful when experiment design is time-consuming and laborious. © 2022 IEEE.

关键词： Data acquisition

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Sensitivity Analysis in Self-Assembly Self-Repair System

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IFAC-PapersOnLine 2023年第2期56卷 9366-9373页

作者： Renzo Caballero Eric Feron Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology (KAUST) K.S.A

We study a case of robot interaction where a robot arm partially repairs a 3D printer by adjusting one of the support columns in the structure of the 3D printer. The collaboration stability is challenged when we assume that the robot misplaced the column on the 3D printer, leading to a non-ideal 3D printer and compromising the quality of the printing process. Experiments are conducted to find and analyze the limits in which the system can maintain self-repair and self-assemble. Based on observations, we constructed and validated mathematical models to explain the results of the experiment and analyze the stability of the self-repair process.

关键词： Modelling Robotics Non-Linear Control Systems Robust Control Mechatronic Systems

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A Discontinuous Galerkin Time-Domain Scheme to Simulate Semiconductor Lasers

A Discontinuous Galerkin Time-Domain Scheme to Simulate Semi...

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European Conference on Antennas and Propagation, EuCAP

作者： Ming Dong Liang Chen Ran Zhao Hakan Bagci Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

A computational framework, which relies on the discontinuous Galerkin time-domain scheme, is proposed to simulate transient lasing generated by interactions of light with an active medium. The proposed scheme solves a coupled system of the Maxwell and the rate equations. The active medium inside the laser region is described quantum-mechanically by the rate equations to account for the atomic transitions of a multi-level system, while electromagnetic field interactions are described classically by the Maxwell equations. Numerical examples are provided to demonstrate the accuracy and the applicability of the proposed framework.

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Wi-Fi RTT Range Offset and Multi-Band Analysis: Challenges and Opportunities for Indoor Localization

Wi-Fi RTT Range Offset and Multi-Band Analysis: Challenges a...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Sultan Al Sulami Tarig Ballal Muhammad Moinuddin Tareq Al-Naffouri Electrical and Computer Engineering Dept King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering Dept King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798331523176

ISBN: (纸本)9798331523183

Indoor localization has emerged as a critical research area, driven by the growing need for accurate positioning in environments where Global Navigation Satellite Systems (GNSS) are unavailable. Wi-Fi Round-Trip Time (RTT) has shown promise for precise indoor positioning, but the presence of systematic offsets across frequency bands poses significant challenges to its accuracy and reliability. These offsets, arise with factors such as device heterogeneity, multipath propagation, and environmental interference, not only degrade RTT performance but also complicate its integration with RTT multi-band and other measurement sources . This study presents a comprehensive analysis of RTT range offset across the 2.4 GHz, 5 GHz, and 6 GHz frequency bands, identifying it as limiting factor in achieving high-precision indoor positioning. Furthermore, we investigate the potential of multi-band fusion to mitigate these offsets and explore the associated challenges in realizing accurate localization systems. Our findings offer insights into integration of RTT-multi-band to overcome the practical limitations of RTT technology.

关键词： Location awareness Global navigation satellite system Time-frequency analysis Accuracy Systematics Limiting Correlation Calibration Frequency measurement Wireless fidelity

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the university of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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