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IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Zia Ullah Ayman S. Abdel-Khalek Shehab Ahmed Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal KSA Department of Electrical Engineering Alexandria University Alexandria Egypt

Integrated onboard battery charging (IOBC) is an emerging technology that utilizes the same circuitry used for propulsion to charge the battery packs of an electric vehicle (EV). Multiphase machines are the better choice for IOBC owing to their high current, fault-tolerant capability, and reduced modification requirements to achieve zero average torque. In this digest, a six-phase interior permanent magnet synchronous machine (IPMSM) was specially designed for the propulsion and IOBC of an e-Golf. Two different winding configurations, i.e., dual three-phase and asymmetrical, were tested for propulsion and IOBC. In both cases, zero average torque in charging mode was ensured. A predictive current control algorithm was used to charge the battery using constant current. Furthermore, the vehicle-to-grid (V2G) mode was also tested using both winding configurations, and the results were compared. The simulation was carried out using finite element analysis and MATLAB Simulink. The simulation results are verified using a 15 kW IPMSM hardware-in-loop-based control system and power grid emulator.

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A 19 fJ/op, Low-Offset StrongARM Latch Comparator for Low-Power High-Speed Applications

A 19 fJ/op, Low-Offset StrongARM Latch Comparator for Low-Po...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： abdullah Alshehri Khaled Salama Hossein Fariborzi Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Computer Electrical and Mathematical Science & Engineering Division Electronics and Communication Engineering Umm Al-Qura University Al-Leith Saudi Arabia

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

In this paper, a new StrongARM latch comparator design has been proposed for low-power high-speed applications. The proposed design improves the energy consumption and propagation delay when compared to the previous designs in the literature. The proposed design is post-layout simulated in TSMC 65 nm technology node and it achieves a low energy consumption of 19.31 fJ per operation and a low propagation delay of 211 ps. Moreover, the proposed design shows a highly favorable input offset voltage of 0.56 mV and achieves a maximum frequency of 8 GHz. Furthermore, the proposed design reduced the transistor stack that allows it to be used in the low-voltage supply application.

关键词： Energy consumption Low voltage Latches Circuits and systems Voltage Transistors Propagation delay

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Learning the References of Online Model Predictive Control for Urban Self-Driving

arXiv

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arXiv 2023年

作者： Wang, Yubin Peng, Zengqi Xie, Yusen Li, Yulin Ghazzai, Hakim Ma, Jun Guangzhou China The Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

In this work, we propose a novel learning-based model predictive control (MPC) framework for motion planning and control of urban self-driving. In this framework, instantaneous references and cost functions of online MPC are learned from raw sensor data without relying on any oracle or predicted states of traffic. Moreover, driving safety conditions are latently encoded via the introduction of a learnable instantaneous reference vector. In particular, we implement a deep reinforcement learning (DRL) framework for policy search, where practical and lightweight raw observations are processed to reason about the traffic and provide the online MPC with instantaneous references. The proposed approach is validated in a high-fidelity simulator, where our development manifests remarkable adaptiveness to complex and dynamic traffic. Furthermore, sim-to-real deployments are also conducted to evaluate the generalizability of the proposed framework in various real-world applications. Also, we provide the open-source code and video demonstrations at the project website: https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Bacteria cells estimation in wastewater treatment plants using data-driven models ⁎

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IFAC-PapersOnLine 2024年第14期58卷 718-723页

作者： Fahad Aljehani Ibrahima N’Doye Pei-Ying Hong Mohammad K. Monjed Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Biology Faculty of Science Umm Al-Qura University Makkah 21961 Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Saclay France

Estimating or predicting the concentrations of bacteria cells is crucial for achieving better control of the operation of wastewater treatment plants. However, measuring the bacterial concentration along the influent wastewater stage to the treated effluent process is challenging as it involves lab access and trained personnel. Additionally, wastewater plants are generally nonlinear systems involving time-varying physical and biological characteristics, increasing the difficulty in estimating the bacterial concentration from a model-based approach. This paper proposes data-driven models based on four machine-learning models to estimate the bacterial cell density with a limited dataset in a wastewater treatment plant. The performance results demonstrate that the machine-learning models (i.e., K-Nearest Neighbour (kNN), Random Forest (RF), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGB)) have the potential to estimate accurately the bacterial concentration. RF displays better bacteria estimation in the influent by 10.7% compared to GBR and 7.4% compared to XGB and kNN. Whereas for the effluent, XGB improved the estimation by 12.8%, 2.4%, 14.6% compared to GBR, RF, and kNN, respectively. Also, results show that conductivity as a single feature is the most significant parameter affecting the bacterial cell estimation in the influent stage for the four machine learning algorithms. Similarly, the chemical oxygen demand (COD) and turbidity have pronounced effects in the effluent stage. These results reveal potential signs of designing a universal data-driven model-based approach applicable for bacteria estimation at influent and effluent based on the minimum feature combinations (conductivity, COD, and turbidity).

关键词： Wastewater treatment plant data-driven models bacterial estimation models machine learning algorithms

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Unbiased Parameter Estimation for Partially Observed Diffusions

arXiv

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arXiv 2023年

作者： Awadelkarim, Elsiddig Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this article we consider the estimation of static parameters for partially observed diffusion process with discrete-time observations over a fixed time interval. In particular, we assume that one must time-discretize the partially observed diffusion process and work with the model with bias and consider maximizing the resulting log-likelihood. Using a novel double randomization scheme, based upon Markovian stochastic approximation we develop a new method to unbiasedly estimate the static parameters, that is, to obtain the maximum likelihood estimator with no time discretization bias. Under assumptions we prove that our estimator is unbiased and investigate the method in several numerical examples, showing that it can empirically out-perform existing unbiased *** Codes 60J22, 62M05, 65C40, 62M20 © 2023, CC BY.

关键词： Parameter estimation

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How to Control the State Transformation from Short-Term Potentiation to Long-Term Potentiation of Charge Trapping Synapse?

How to Control the State Transformation from Short-Term Pote...

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International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)

作者： Md. Hasan Raza Ansari Nazek El-Atab SAMA Labs Division of Computer Electrical and Mathematical Science and Engineering (CEMSE) Division Electrical and Computer Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

In this work, the charge trapping-based Gate All Around Transistor (GAA) technology is utilized for the artificial synapse due to its superior performance in terms of low power consumption and high speed. The controlling of state transformation of electronic synapses from short-term potentiation (STP) to long-term potentiation (LTP) is crucial. We have investigated the effect of metal gate workfunction and channel doping on state transformation by using a feedback mechanism (impact ionization). This mechanism regulates channel potentiation and the level of charge trapping in the nitride layer. Our simulation results indicate that this approach can effectively control the STP to LTP transition, while also achieving long STP retention and better dynamic range. This study presents a promising direction for developing advanced electronic devices based on charge-trapping-based GAA transistors.

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3D micro-printed ultra-compact single-fiber Optical tweezers

3D micro-printed ultra-compact single-fiber Optical tweezers

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Optical Manipulation and Its Applications, OMA 2021 - Part of Biophotonics Congress: Optics in the Life sciences 2021

作者： Reddy, Innem V.A.K Bertoncini, Andrea Liberale, Carlo Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Electrical Engineering University at Buffalo NY United States Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

We present a fully 3D printed, on-fiber hybrid photonic structure to create customizable single-fiber optical tweezers. The structure contains wave guiding, reflecting, and refracting micro-optical elements stacked to... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical tweezers

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Exploring High Tower Base Stations with Multi-User Massive MIMO for Rural Connectivity

Exploring High Tower Base Stations with Multi-User Massive M...

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International Black Sea Conference on Communications and Networking (BlackSeaCom)

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

The digital divide is a key issue worldwide. Almost 3 billion people, mainly in rural areas, are still not connected. In this paper, we explore the capability of high towers base station (HTBS) with massive multiple input multiple output (mMIMO) in offering low-cost rural connectivity. We previously showed the benefits of HTBS in the downlink. We focus in this work on the uplink (UL) where we compute the UL data rate per user for different values of transmit effective isotropic radiated power (EIRP). Our results show that the HTBS solution is viable as relatively good user UL rates are achieved with reasonable EIRPs. This is of high interest for covering rural areas, characterized by low population densities and a low number of active users, as the coverage is their main constraint, rather than the capacity as in urban areas. Techno-economical aspects such as the recommended frequency for HTBS, the number of covered persons, the average population density of covered rural areas, and potential low-cost locations for HTBS are provided. Non-technological challenges for the HTBS solution are also discussed.

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Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching

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IEEE Transactions on Communications 2025年

作者： Tharakan, Krishnendu S. Dahrouj, Hayssam Kouzayha, Nour Elsawy, Hesham Al-Naffouri, Tareq Y. Queen's University School of Computing Kingston Canada University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955-6900 Saudi Arabia

Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of view (FoV) aware caching for mobile edge computing (MEC)-enabled wireless VR network. In particular, the FoV of each VR user is cached/prefetched at the base stations (BSs) based on the caching strategies tailored to each BS. Specifically, decentralized and personalized federated learning (DP-FL) based caching strategies with guarantees are presented. Considering VR systems composed of multiple VR devices and BSs, a DP-FL caching algorithm is implemented at each BS to personalize content delivery for VR users. The utilized DP-FL algorithm guarantees a probably approximately correct (PAC) bound on the conditional average cache hit. Further, to reduce the cost of communicating gradients, one-bit quantization of the stochastic gradient descent (OBSGD) is proposed, and a convergence guarantee of O(1/√T) is obtained for the proposed algorithm, where T is the number of iterations. Additionally, to better account for the wireless channel dynamics, the FoVs are grouped into multicast or unicast groups based on the number of requesting VR users. The performance of the proposed DP-FL algorithm is validated through realistic VR head-tracking dataset, and the proposed algorithm is shown to have better performance in terms of average delay and cache hit as compared to baseline algorithms. © 1972-2012 IEEE.

关键词： Federated learning

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A Case Study of an Adaptive Delta-Stepping Algorithm in OpenMP 4

A Case Study of an Adaptive Delta-Stepping Algorithm in Open...

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4th Research, Invention, and Innovation Congress: Innovative electricals and Electronics, RI2C 2023

作者： Khadka, Mandeep Ausavarungnirun, Rachata Terboven, Christian Electrical and Computer Engineering TGGS King Mongkut’s University of Technology North Bangkok Bangkok Thailand Chair for Computer Science 12 - High Performance Computing RWTH Aachen University Aachen Germany

ISBN: (纸本)9798350330618

The ∆-stepping algorithm is one of the most popular algorithms implemented in modern graph processing systems for solving the single-source shortest path problem. The algorithm executes in a sequence of steps, processing a subset of vertices in parallel in each step. A user-defined parameter called "delta" determines the number of vertices processed in a single step. Thus, the performance of the ∆-stepping algorithm is highly sensitive to the value of the delta parameter. The value of delta that performs well on one graph input may give worse results on another input. As a result, the delta parameter requires exhaustive tuning which is typically a part of pre-processing phase. In this work, we study a case for an adaptive ∆-stepping algorithm wherein the delta value is dynamically adjusted during the program runtime. We implement this adaptive policy for delta on top of an existing reference implementation and apply heuristics for optimal parameter calculation. We evaluate our implementation on a diverse set of graph inputs. The results show that, compared to the baseline reference implementation, adjusting delta during the program execution can reduce the performance sensitivity to ∆ in terms of parallel running time from orders of magnitude of 10 to approximately 2× the best-performing delta value on all the inputs. In addition, the changes made to the ∆-stepping algorithm do not impact the program scalability and incur less than 2% overhead on the overall performance. ©2023 IEEE.

关键词： Application programming interfaces (API)

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