Mobile devices for the personalized detection of health and environmental hazards are becoming the basis for futuristic sensing technologies.

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.  This is potentially interesting for brain scientists because functional brain networks are associated with cognitive function and mental and neurological diseases.

A variational model in the context of the gradient theory for fluid-fluid phase transitions with small scale heterogeneities is studied. In the case where the scale of the small heterogeneities is of the same order of the scale governing the phase transition, the interaction between homogenization and the phase transitions process leads to an anisotropic interfacial energy. Bounds on the homogenized surface tension are established. In addition, a characterization of the large-scale limiting behavior of viscosity solutions to non-degenerate and periodic Eikonal equations in half-spaces is given. This is joint work with Riccardo Cristoferi (Radboud University, The Netherlands), Adrian Hagerty (USA), Cristina Popovici (USA), Rustum Choksi (McGill, Canada), Jessica Lin (McGill, Canada), and Raghavendra Venkatraman (NYU, USA).

The exact mechanisms controlling plant growth are a topic of ongoing botanical research. This makes the realistic computer simulation of plants a challenging task. We present a method for generating realistic models of trees and shrubs. This method is based on the biological hypothesis that the form of a developing tree emerges from a self-organizing process dominated by the competition of buds and branches for light or space, and regulated by internal signaling mechanisms. Simulations of this process robustly generate a wide range of realistic trees and bushes.

Together with the algorithm suitability to exploit current petascale and next-generation exascale supercomputers, robust, accurate, and structure-preserving discretizations are necessary for developing predictive computational tools. The research carried out in the Advanced Numerical Algorithms and Numerical Simulations Laboratory (AANSLab) leverages a multidisciplinary platform that integrates numerical analysis, physics, and high-performance computing. In particular, we focus on the analysis and development of novel numerical methods for ordinary and partial differential equations with provable properties such as nonlinear stability and conservation, and structure-preserving techniques. These properties are critical for designing reliable, efficient, and self-adaptive solvers for complex geometries – an essential cornerstone for next-generation computational frameworks. Current classes of partial differential equations that we are working on are the compressible Navier–Stokes equations, the Eulerian model for compressible heat-conducting flows, and the diffusion-reaction and convection-diffusion-reaction equations for molecular communication. We also use deep learning to complement and speed up the process of solving efficiently large-scale PDE-based problems. In this talk, I will summarize the progress we made in the last five years in the following areas: - Numerical analysis and algorithm development for robust, smart compressible flow solvers. - Development from the ground up of a new scalable hp-adaptive computational fluid dynamics (CFD) framework that places KAUST a few years ahead of the NASA CFD 2030 vision: o Applications and impact in the automotive and aerospace industry. o Improving knowledge of flow physics: Examples in detonation and aeroacoustics. - Advection-reaction-diffusion algorithms for molecular communication. Finally, I'll discuss our translational work to solve industrially relevant flow problems in partnership with Boeing, NASA Langley Research Center, and McLaren F1 Racing Team, and my future research plans.

In modern societies, there is an accelerated convergence of information technology (IT) like the internet-cloud-web complex, with operational technology (OT) like cyber-physical systems (CPS) control, complemented by Internet-of-Things fabrics (IoT).

Mobile devices for the personalized detection of health and environmental hazards are becoming the basis for futuristic sensing technologies. In recent decades, air and environmental pollution levels have risen globally. Therefore, environmental protection must be strengthened by developing sensors that detect pollutants. The monitoring of these pollutants with high spatial coverage requires inexpensive electronic gas sensors and self-sustainable sensing systems that can be deployed everywhere. This dissertation reports on technological developments to provide solutions for inexpensive, compact, power-efficient, and easily deployable toxic gas sensors and integrated systems using semiconducting metal-oxide thin-film transistors (TFTs).

Abstract

In many research fields such as meteorology, eco

Over 4000 exoplanets - planets beyond the Solar System - have been discovered since the first Nobel prize-winning exoplanet detection around a Sun-like star in 1995. The majority of these exoplanets have been detected by indirect methods, inferring the presence of the exoplanet by observing the star.

Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in proper way if one want to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.

Extreme climate events affect the lives of millions people around the world each year and are anticipated to increase in frequency and magnitude due to climate change. Improving our understanding of historical and expected climate variability and change is paramount to mitigating the impacts of these extreme events. In this seminar, I will talk about my research on the fusion of information from weather models, satellites, and ground stations to gain a better understanding of climate variability in the past, present, and future.

Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in proper way if one want to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.

The qualitative study of PDEs often relies on integral identities and inequalities. For example, for time-dependent PDEs, conserved integral quantities or quantities that are dissipated play an important role. In particular, if these integral quantities have a definite sign, they are of great interest as they may provide control on the solutions to establish well-posedness.

Antennas are integral part of wireless communication devices and traditionally have remained off the Integrated Circuits (ICs which are also commonly known as chips) resulting in large sized modules.

Robotic systems are well-known to be highly nonlinear processes due to their complex kinematics. The identification of the corresponding system dynamics plays an important role in obtaining an adequate model which is especially hard under changing parametric circumstances. Furthermore, precise knowledge of the state is crucial for a large variety of control tasks. Sparse sensor setups make these problems more challenging due to significant noise impact. For designing an efficient and robust algorithm, an integral transform approach is proposed exploiting the robotic system structure. Specifically, the Modulating Function Method is introduced in the context of multi-body systems for fixed-time parameter and state estimation. An adaptive observer structure is presented in order to give an impression of the general methodology and the related research questions.

Backward induction, the cornerstone of dynamic game theory, is the classical algorithm applied to solve finite dynamic games with perfect and complete information. While theoretically sound and beautiful in its simplicity, backward induction does not perform so well when it comes to predicting human behavior. The objective of this seminar is twofold. First, we will understand what backward induction is and how to apply it on game-theoretic trees. Second, we will answer the question of whether backward induction is a good model of how people make choices in dynamic interactions.

In this talk an inversion-based control approach is presented for the generation and stabilization of a periodic orbit in a multi-link triple pendulum on a cart. To this end, a nominal trajectory is generated by formulating the posture transition problem as a two-point Boundary Value Problem (BVP) in an input-output representation. For solvability of the BVP, a setup function is introduced such that additional parameters are provided in the differential equation of the internal dynamics. Based on the linearized dynamics about the nominal trajectory, a linear-quadratic-Gaussian controller is implemented to compensate for measurement noise, model uncertainties, and external disturbances. This way we force a triple pendulum to move along a non-trivial periodic orbit and render it attractive. The high performance and accuracy of our approach is illustrated on an experimental setup.

Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in a proper way if one wants to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.

We are excited to announce the KAUST Research Conference on Robotics and Autonomy 2022, which will be held on Feb 28 - March 2, 2022 (#RobotoKAUST). The conference will address the most recent trends of robotics application in a range of disciplines. The conference will give space for presentations and discussions of systems, items, and methods related to robotics and autonomy. As part of this event, we are accepting poster abstract submissions.

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones.

After a quick overview of the ECE Graduate Seminar logistics, I will share the progression of our team’s work in the field of compact integrated on-board EV charging.

Highly integrated and customizable systems have been a principal focus of development for parenteral and oral drug administration. Extensive work has been done to optimize drug efficacy via localized delivery and dosage control providing new ways for accomplishing targeted therapeutic effects. However, many challenges and opportunities for advancement remain. One promising research path is introducing novel microfabrication methods or engineering discoveries in concept realization, making devices more versatile and effective.

This thesis studies novel and efficient computational sampling methods for applications in three types of stochastic inversion problems: seismic waveform inversion, filtering problems, and static parameter estimation.

Optical Wireless Communication (OWC) offers many benefits over established radio frequency-based communication links. In particular, high beam directivity generates efficient power usage and high-speed data services. Moreover, due to its ease of deployment, high transmission security, license-free operation, and insensitivity to interference, the OWC link is becoming an attractive solution for the next generation of communication systems.

High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.