Hardware impairments (HWIs) impose a huge challenge on modern wireless communication systems owing to the characteristics like compactness, least complexity, cost effectiveness and high energy efficiency. Numerous techniques are implemented to minimize the detrimental effects of these HWIs ,however, the residual HWIs may still appear as an additive distortion, multiplicative interference, or an aggregate of both. Numerous studies have commenced efforts to model one or the other forms of hardware impairments in the radio frequency (RF) transceivers. Many presented the widely linear model for in-phase and quadrature imbalance (IQI) but failed to recognize the impropriety induced in the system because of the self-interfering signals. Therefore, we have presented not only a rigorous aggregate impairment model along with its complete impropriety statistical characterization but also the appropriate performance analysis to quantify their degradation effects. Latest advances have endorsed the superiority of incorporating more generalized impropriety phenomenon as opposed to conventional propriety.

Signal processing and communication communities have witnessed the rise of many exciting communication technologies in recent years. Notable examples include alternative waveforms, massive multiple-input multiple-output (MIMO) signaling, non-orthogonal multiple access (NOMA), joint communications and sensing, sparse vector coding, index modulation, and so on.

This talk will provide an overview of the III-nitride-based visible light-emitting diodes (LEDs). Especially, the InGaN-based blue LEDs are very contributed to energy-saving for light sources all over the world.

The rapid penetration of renewable energy sources (RESs) into power systems has introduced many challenges in securing a stable network operation and has forced power systems to operate in low inertia conditions. Low short-circuit power and intrinsic inertial response from converter-interfaced RES may cause poor dynamic performance of systems and render the system frequency more vulnerable than conventional grids. The talk proposes and analyzes different strategies using synchronous condenser (SC), synthetic inertia (SI) of wind power plant, and their combination to enhance the frequency stability of low inertia systems under various scenarios and wind conditions.

Simulation tools are often enablers of cutting-edge research in many fields of science and engineering. This also applies to the field of electromagnetics: Design and characterization of many electromagnetic systems and devices, which drive technological advances in areas such as communications, computing, biomedicine, and solar energy, would not be possible without simulation tools. Having said that, developing numerical methods for electromagnetic analysis of such complex systems is not a trivial task. Dimensions of these systems are large (longer than several wavelengths), their frequency of operation has a wide range, and their geometry has sub-wavelength features. When it comes to simulating these systems by solving the Maxwell equations (in differential or integral form), these characteristics translate into multiscale discretizations, large and often ill-conditioned matrix systems with millions of unknowns to be solved for, and long execution times. My research group at KAUST has been developing efficient, accurate, and robust electromagnetic solvers to address these challenges. In this talk, I will provide an overview of my group’s recent research activities. Technical part of my presentation will focus on two examples, where I talk about solvers we have developed to efficiently and accurately simulate photoconductive antennas (used in terahertz source generation) and plasmonic structures (with a wide range of applications from sensing to solar energy). I will provide results that demonstrate the benefits of these solvers over existing methods. I will conclude my talk with a brief description of my future research plans and a few slides about my research supervision, teaching activities, and international visibility of my research group.

Nowadays, the development of portable and wearable electronics becomes unprecedentedly significant because they allow for real-time communication, remote controlling, and monitoring of various potential environmental hazards regardless of location and time.

Biological data sets, such as gene expressions, copy number alteration, and pharmacogenomics, are often high-dimensional and thus difficult to analyze and interpret. This talk presents two data analysis methodologies that we recently developed based on network analysis via Wasserstein optimal transport combined with unsupervised classification techniques.

In a nutshell, Resilient Computing is a new paradigm based on modelling, architecting and designing computer systems so that: they have built-in baseline defences; such defences cope with virtually any quality of threat, be it accidental faults, design errors, cyber-attacks, or unexpected operating conditions; provide incremental protection of, and automatically adapt to, a dynamic range of threat severity; provide sustainable operation. Resilient Computing will be a game changer in the craft of designing computer systems of today and future.

Nowadays acoustic sensors collect huge amount of data on a regular basis to monitor various activities. This necessitates automated audio event recognition using acoustic data. In this talk, we will present effective methods for detecting and classifying different audio events. Interesting ideas and results will be shown for the proposed methods related to biological/bioacoustic signals.

Imaging systems have long been designed in separated steps: the experience-driven optical design followed by sophisticated image processing. Such a general-propose approach achieves success in the past but left the question open for specific tasks and the best compromise between optics and post-processing, as well as minimizing costs. Driven from this, a series of works are proposed to bring the imaging system design into end-to-end fashion step by step, from joint optics design, PSF optimization, phase map optimization to a general end-to-end complex lens camera.

In this talk, I will introduce a simulator specifically formulated and implemented for photoconductive terahertz devices enhanced with nanostructures. The fundamental challenge in formulating this simulator is the rigorous modeling of the coupling mentioned above. My talk will focus on this aspect as well as the general concerns in building mathematical models for various physical processes and their numerical discretization. To this end, I will introduce a discontinuous Galerkin framework that can efficiently discretize the multiscale mathematical models formulated for coupled semiconductor physics and electromagnetics. Furthermore, I will present numerical results which demonstrate the applicability of this framework in characterizations of photoconductive terahertz antennas and photomixers.

This talk will be focused on developing low-cost sensors, which can increase oil production efficiency through real-time monitoring of oil wells. The mechanism behind the development of low-cost & printed microwave sensors will be introduced followed by a number of applications where these sensors can be utilized. A specific application to measure the composition of production fluid will be described in detail. Unique benefits of Microwave DMOR technology, utilized in the sensors, will be explained in comparison with existing technologies.

After a quick overview of the ECE Graduate Seminar logistics, I will share a quick introduction to the wellbore construction process. This will help build the case for maintaining wellbore integrity in order to protect assets, people, the environment and production. The synergistic integration of electromagnetics, electronics and machine learning to create a novel mechatronic solution to address wellbore integrity needs is then discussed. The solution utilizes a full maxwell equations solver deployed on KAUST’s super computing platforms to enable next generation physics informed wellbore integrity solutions based on non-contact EM field propagation circuits. While downhole camera technologies are used today, they require illumination and an optically clear environment. Our electromagnetic ‘vision’ system overcomes these limitations and provides additional capability to ‘see through’ nested wellbore tubulars.

In this talk, I will describe three use cases that highlight present challenges and opportunities for the development of machine learning methodology for applications in healthcare. First, I will describe the development of simple word embedding approaches for bag of-documents classification and its applications to diagnosis of peripheral artery disease from clinical narratives. Second, I will present an approach for volumetric image classification that leverages attention mechanisms, contrastive learning and feature-encoding sharing for geographic atrophy prognosis from optical coherence tomography images. Third, I will discuss machine learning approaches for multi-modal and multi-dataset integration for biomarker discovery from molecular (omics) data. To conclude, I will summarize the contributions and insights in each of these different directions in which relatively low sample sizes are the common denominator.

Magnetic random access memory (MRAM) devices have been widely studied since the 1960s. During this time, the size of spintronic devices has continued to decrease. Consequently, there is now an urgent need for new low-dimensional magnetic materials to mimic the traditional structures of spintronics at the nanoscale. We also require new effective mechanisms to conduct the main functions of memory devices, which are: reading, writing, and storing data.

This event has been postponed from 20th July to 27th July. Stochastic optimization refers to the minimization/maximization of an objective function in the presence of randomness. The randomness may appear in objective functions, constraints, or optimization methods. It has the advantage of dealing with uncertainties that deterministic optimizers cannot solve or cannot solve efficiently. In this work, we discuss the implementation of stochastic optimization methods in solving target positioning problems and tackling key issues in location-based applications.

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This talk explains how agents over a graph can learn from dispersed information and solve inference tasks of varying degrees of complexity through localized processing. The presentation also shows how information or misinformation is diffused over graphs, how beliefs are formed, and how the graph topology helps resist or enable manipulation. Examples will be considered in the context of social learning, teamwork, distributed optimization, and adversarial behavior.

#RobotoKAUST21.

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Wide bandgap (WBG) semiconductors including GaN have demonstrated great success in lighting, display, electrification, and 5G communication due to superior properties and decades of R&D. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) semiconductors with bandgap larger than GaN have attracted increasing attentions. They are regarded as the 4th wave of the inorganic semiconductors after the consequential Si, III-V, and WBG semiconductors. Because the UWBG along with other properties could enable electronics and photonics to operate with significantly greater power and frequency capability and at much shorter far−deep UV wavelengths, crucial for sustainability and health of the human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. This seminar would cover the latest research by the Advanced Semiconductor Lab. It includes multi-disciplinary studies of growth, materials, physics, and devices of the UWBG semiconductors.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.