In this talk, we address fundamental questions that should be asked when contemplating future smart and connected systems, namely, How, Where and What? How can we design computing and communication nodes that best utilize resources in a way that is cognizant of both the abilities of the platform, as well as the requirements of the network? Where are the nodes deployed? By understanding the context of deployment, one can architect unique solutions that are currently unimaginable. With the transformation to diverse applications such as body area networking, critical infrastructure monitoring, precision agriculture, autonomous driving etc., the need for innovative solutions becomes even more amplified. What benefit can be inferred from the data gathered by nodes in the capacity of computing, communication and sensing? The talk will discuss how recent advances in wireless computing and communication nodes can be harnessed to serve the multitude of deployment scenarios required to empower communities of the future with smart and connected systems.

The mini-course is an introduction to intrinsic scaling, a powerful method in the analysis of degenerate and singular parabolic PDEs. The local Hölder continuity of bounded weak solutions will be derived from scratch for the model case of the degenerate p-Laplace equation. Our approach is entirely self-contained and focused on the essence of the method, leaving aside technical refinements needed to deal with more general equations.

Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.

The mini-course is an introduction to intrinsic scaling, a powerful method in the analysis of degenerate and singular parabolic PDEs. The local Hölder continuity of bounded weak solutions will be derived from scratch for the model case of the degenerate p-Laplace equation. Our approach is entirely self-contained and focused on the essence of the method, leaving aside technical refinements needed to deal with more general equations.

This webinar will explore the potentiality of massive MIMO arrays in a scenario where radar and communication tasks coexist. The first part of the talk will be devoted to the discussion of a scenario where a wireless network is to co-exist with an external radar system sharing the same frequency band. The second part of the talk will be instead focused on a scenario where a base station equipped with a large scale antenna array simultaneously perform the task of radar surveillance of the surrounding environment and of data communication with mobile users.

Environmental statistics plays an important role in many related applications, such as weather-related risk assessment for urban design and crop growth. However, modeling the spatio-temporal dynamics of environmental data is challenging due to their inherent high variability and nonstationarity. This dissertation focuses on the modeling, simulation, and prediction of spatio-temporal processes using statistical techniques and machine learning algorithms, especially for nonstationary processes.

I’ll cover thee different RPC policies implemented on top of R2P2. Specifically, we’ll see how R2P2 enables efficient in-network RPC load balancing based on a novel  join-bounded-shortest-queue (JBSQ) policy. JBSQ lowers tail latency by centralizing pending RPCs in the middle box and ensures that requests are only routed to servers with a bounded number of outstanding requests. Then, I’ll talk about SVEN, an SLO-aware RPC admission control mechanism implemented as an R2P2 policy on P4 programmable switches. Finally, I’ll describe HovercRaft, a new approach to building fault-tolerant generic RPC services by integrating state-machine replication in the transport layer.

Nanophotonic research has a huge influence on various aspects of modern society, such as communication, commercial products, biochemistry, and information processing. A central topic in the research of nanophotonics is the geometrical optimization of the nanostructures since the geometries are deeply related to the Mie resonances and the localized surface plasmon resonances in dielectric and metallic nanomaterials. When many nanostructures are assembled to form a metamaterial, the tuning of the geometrical parameters can bring even more profound effects, such as bound states in the continuum (BIC) with infinite quality factors (Q factors). Moreover, with the development of nanofabrication technologies, there is a trend of integrating nanostructures in the vertical direction, which provides more degrees of freedom for controlling the device performance and functionality. In this dissertation, we explore how the geometrical changes of the nanostructures influence the nanophotonic devices, furthermore, we utilize machine learning techniques to actively control the topologies of the devices to enhance their performances.

COVID-19 is a wake-up call for public health and safety. Since the pandemic began, the ultraviolet (UV) technologies have been widely employed in numerous places to contain and eliminate the viruses. Although UV light is nonchemical and does not contaminate the environment, however, most of the UV light today comes from the toxic mercury lamps. In our lab and many institutions in the world, researchers are developing semiconductor-based UV technologies that are compact, reliable, and nontoxic. This seminar shows the research background and briefly discusses the research progress made by our team in a few key areas including material growth, physics, and device fabrication for UV lasers, LEDs, and photodetectors.

Eigenvalue problems associated with partial differential equations are key ingredients for the modeling and simulation of a variety of real world applications, ranging from fluid-dynamics, structural mechanics, acoustics, to electromagnetism and medical problems. We review some properties related to the approximation of eigenvalue problems. Starting from matrix algebraic problems, we present a series of examples and counterexamples showing that even extremely simple situations can produce unexpected results.

First Speaker: Megumi Kaneko, National Institute of Informatics, Japan. Talk Title: Resource Allocation in NOMA-Based Fog Radio Access Networks. In this talk, I will first describe the potential benefits offered by the integration of NOMA in an FRAN architecture for achieving the specific objectives of use cases envisioned in B5G, in terms of throughput, latency, reliability and energy efficiency. Second Speaker: Bruno Clerckx, Imperial College London, United Kingdom. Talk Title: Rate-Splitting Multiple Access and its Applications to Cloud-Enabled Platforms. This talk argues that to efficiently cope with the high throughput, reliability, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future multi-antenna wireless networks, multiple access and multiuser communication system design need to depart from the two extreme interference management strategies, namely fully treat interference as noise (as commonly used in 5G, MU-MIMO, CoMP, Massive MIMO, millimetre wave MIMO) and fully decode interference (as in NOMA).

In this thesis, I firstly provide a comprehensive assessment of wind energy resources and associated spatio-temporal patterns over Saudi Arabia in both current and future climate conditions, based on a Regional Climate Model output. A high wind energy potential exists and is likely to persist at least until 2050 over a vast area of Western Saudi Arabia, particularly in the region between Medina and the Red Sea coast and during Summer months. Since an accurate assessment of wind extremes is crucial for risk management purposes, I then present the first high-resolution risk assessment of wind extremes over Saudi Arabia.

Network communication is a major bottleneck in large-scale distributed deep learning. To minimize the problem, many compressed communication schemes, in the form of quantization or sparsification, have been proposed. We investigate them from the Computer Systems perspective, under real-life deployments. We identify discrepancies between the theoretical proposals and the actual implementations, and analyze the impact on convergence.

This talk will give an overview of the research of III-nitride based visible light-emitting diodes (LEDs) from the KAUST Energy Conversion Devices and Materials (ECO Devices) Laboratory (https://ecodevices.kaust.edu.sa/Pages/Overview.aspx). III-nitride based visible LEDs have achieved tremendous success in the field of solid-state lighting and dominated the lighting markets in the past decades. This technique becomes mature, and the luminous efficacy of LED devices approaches the theoretical maximum limit.

Our suggested criteria are more useful for the determination of tuning parameters for sophisticated approximation methods of spatial model fitting. To illustrate this, we investigate the trade-off between the execution time, estimation accuracy, and prediction efficiency for the TLR method with intensive simulation studies and suggest proper settings of the TLR tuning parameters.

Aerospace Engineering is the source of much dreaming and worrying in most people's imagination. As such, it constitutes a marvelous support for the development and application of numerous disciplines, chief among them are aerodynamics, structures, flight and orbital mechanics. With time however, information technologies are progressively becoming the costliest activity within the aerospace engineering systems design phase. Indeed, they stand as the core elements of avionics systems, airline operations, air traffic control, spacecraft engineering, and planetary exploration, among others. Such subjects have been bringing much joy and teaching many lessons to your speaker for the past 30 years and will keep doing so, hopefully, for the next three decades.

In this webinar we will give an overview of several key technologies related to our special issues in Frontiers of Communications and Networks; (1) visible light communications (VLC) and (2) applications of machine learning in optical and wireless communication systems.

This talk will introduce two novel models we developed for automatic HG generation in two different settings, positive-negative learning and positive-unlabeled learning. We demonstrate the efficacy of the proposed model on three real-world datasets constructed from biomedical publications.

The mini-course is an introduction to the analysis of infinity-harmonic functions. We detail the proof of the equivalence between enjoying comparison with cones and solving the infinity-Laplace equation in the viscosity sense, thus making a seamless connection with the previous mini-course. Further material includes the existence of infinity-harmonic functions in the case of an unbounded domain and an easy and self-contained proof, due to Armstrong and Smart, of the celebrated uniqueness theorem of Jensen.

Compartmental epidemiological models are one of the simplest models for the spread of a disease.  They are based on statistical models of interactions in large populations and can be effective in the appropriate circumstances.  Their application historically and in the present pandemic has sometimes been successful and sometimes spectacularly wrong.  In this talk I will review some of these models and their application.  I will also discuss the behavior of the corresponding dynamical systems, and discuss how the theory of optimal control can be applied to them.  I will describe some of the challenges in using such a theory to make decisions about public policy.

Digital health solutions improve healthcare services and help achieve sustainable and higher standards of health and well-being. These solutions are mainly based on Digital Signal Processing (DSP) to record, interpret, and diagnose bio-signals such as Electrocardiogram (ECG) or Magnetoencephalography (MEG). In my thesis, a novel signal/image post-processing algorithm is proposed based on the Semi-Classical Signal Analysis method (SCSA) to enhance biomedical data quality. In addition, new feature extraction algorithms are proposed, based on the SCSA and the new Quantization-based Position Weight Matrix (QuPWM), which opens new tracks toward smart biomedical diagnosis and decision-making assistance in different fields such as predicting true Poly(A) regions in a DNA sequence, multiple hand gesture prediction.

Billions of IoT devices will need to communicate with each other in a wireless fashion in the future. Thus, new antenna designs, which perform irrespective of their orientation and position, and can be mass manufactured at lower costs are required. This work presents the theory and design of antennas with near isotropic radiation performance which can be additively manufactured on the packaging of the circuits.

This dissertation presents our efforts to build an operational ensemble forecasting system for the Red Sea, based on the Data Research Testbed (DART) package for ensemble data assimilation and the Massachusetts Institute of Technology general circulation ocean model (MITgcm) for forecasting. The Red Sea DART-MITgcm system efficiently integrates all the ensemble members in parallel, while accommodating different ensemble assimilation schemes. The promising ensemble adjustment Kalman filter (EAKF), designed to avoid manipulating the gigantic covariance matrices involved in the ensemble assimilation process, possesses relevant features required for an operational setting. We developed new schemes aiming at lowering the computational burden while preserving reliable assimilation results. The ensemble data assimilation system is implemented and tested on Shaheen, our world-class supercomputer, and will form the basis of the first ever operational Red Sea forecasting system that is currently being implemented to support Saudi Aramco operations in this basin.

In this talk, we review the main backhauling techniques, and present the main motivations/standardization agreements on IAB. Moreover, we present comparisons between the IAB networks and the cases where all or part of the small access points are fiber-connected.

The mini-course is an introduction to the analysis of infinity-harmonic functions. We detail the proof of the equivalence between enjoying comparison with cones and solving the infinity-Laplace equation in the viscosity sense, thus making a seamless connection with the previous mini-course. Further material includes the existence of infinity-harmonic functions in the case of an unbounded domain and an easy and self-contained proof, due to Armstrong and Smart, of the celebrated uniqueness theorem of Jensen.