My research at the University of Toronto involves designing and fabricating CMOS-compatible nano-plasmonic waveguide structures and devices for on-chip communication. By taking advantage of the absence of diffraction limits, strong modal confinement is made possible using plasmonic device architectures, laying the foundations for improved optical processes and photonic circuit integration. All-oxide structures have emerged as promising plasmonic materials that can serve as relatively low carrier density Drude metals and dielectric spacers by their electro-optic tunability and versatility. I demonstrated the facile integration of all-oxide heterointerfaces into metal–insulator–semiconductor (MIS) electro-optic structures. From there, I fabricated MIS devices that incorporated semiconductive oxide layers with oxide dielectric spacers on silicon-on-insulator (SOI) platforms, constituting graded-index coupled hybrid plasmonic waveguide (CHPW) optical modulators.

This joint workshop between KAUST and Fudan University covers a variety of topics related to optical communication and devices for visible light communication. Theoretical modeling, device engine/materials investigation and sub-system development will be discussed. These research are essential to eventually realize the goals of connecting all the unconnected, SMART systems and optical internet-of-things. The workshop will be running in hybrid mode, and presenters will be from both KAUST and Fudan University.

In this talk, I will be talking about β-Ga2O3 based monolithically integrated logic circuits and flash memory devices toward extreme environment operations. Moreover, the important challenges in material and device fabrication for realizing this technology will be discussed. Finally, I will introduce the promises of β-Ga2O3 at low-temperature (~ 4 K) operation for space electronics applications.

The growth of digital cameras and data communication has led to an exponential increase in video production and dissemination. As a result, automatic video analysis and understanding has become a crucial research topic in the computer vision community. However, the localization problem, which involves identifying a specific event in a large volume of data, particularly in long-form videos, remains a significant challenge.

This thesis focuses on Global Navigation Satellite Systems (GNSS)-based localization and attitude determination, essential for navigation and control systems in various platforms. Carrier-phase observations from GNSS signals are more accurate than pseudo-range, but resolving integer ambiguities in carrier-phase data is challenging. The thesis proposes three attitude determination methods based on an optimized GNSS attitude model with nonlinear constraints. Additionally, a joint solution for real-time kinematic positioning and attitude determination is proposed, leveraging the correlation between GNSS data in these two problems. Riemannian optimization is applied to improve the accuracy and ambiguity resolution in both localization and attitude determination.

Abstract

The inclusion of boron in III-Nitride semiconductors is an exc

The fact of power systems’ criticality to national security mandates continuous attention to the worst-case disruption scenarios. In a cyber world, grid modernization efforts have been hindered by cyber-related concerns. This webinar sheds some light on resiliency achievement by utilizing the modular cyber-physical nature of power systems, from an electron in a battery to a digital bit in the internet abyss.

This thesis investigates the potential of flat optics as a solution to the problem of bulky and expensive optical components in producing lightweight and wearable optoelectronic devices. The research addresses scalability challenges in structure fabrication, design of broadband functionalities, and increasing operational and transmission efficiency in the visible range. It focuses on the experimental part of the challenge. The study evaluates various design approaches, including inverse designs using optimization techniques as well as the use of machine learning algorithms. The thesis aims to explore a path toward high efficiency, wide bandwidth, functional response, and scalable fabrication in flat optics using semiconductor nanostructures. The results demonstrate the potential of using semiconductor nanostructures to engineer efficient, scalable, and broadband optical components for light processing via flat surfaces.

GNSS PPP-RTK is an integer ambiguity resolution enabled precise point positioning concept originally developed for use with the ultra-precise CDMA-based Global Navigation Satellite System carrier-phase signals. In this presentation, we first present the classical principles of PPP-RTK as they apply to the CDMA-based global and regional satellite navigation systems GPS, BeiDou, Galileo, QZSS and IRNSS.

Embedded systems are currently an indispensable part of our lives because of their pervasive deployment in a wide range of critical applications (e.g., automotive, encryption, and healthcare, etc.) as well as non-critical applications (e.g., image/video processing, etc.). These embedded system form the fundamental components of today’s Cyber Physical Systems (CPS) and Internet-of-Things (IoT), which are subjected to stringent constraints in terms of reliability, security, and power-/energy especially in case of battery-driven scenarios. Due to the shrinking transistor dimensions, embedded computing hardware are increasingly susceptible to a wide range of reliability threats e.g., transient faults (such as soft errors due to high-energy particle strikes) and permanent faults due to design-time process variations and run-time aging effects. These threats may lead to functional and timing errors that may jeopardize the correct application’s executions. Furthermore, security has also become a crucial aspect in today’s systems because of several security threats, e.g., confidentiality threat to steal the IP or private information via side channel attacks. These reliability and security threats may lead to a catastrophic impact on the robustness of embedded systems. Another key design constraint of a battery-driven embedded platform is power-/energy-wise efficiency, e.g., a device under a low power/battery mode during the critical application execution may miss a critical event that may lead to a catastrophic outcome. In order to address the above-mentioned challenges, it is crucial to investigate different techniques at the hardware and software layers. In this talk, I will first highlight the key robustness and energy efficiency challenges in embedded computing systems, and will present techniques to address these challenges.

The recognition of human behavior has driven most advances in video analysis over the past decade.

Being renowned for operating with visible light pulses and electrical signals, optoelectronic smart non-volatile memory devices have excellent potential for neuromorphic computing systems and artificial visual information processing.

Deep Neural Networks (DNNs) have shown huge success over the years to solve many 2D computer vision tasks driven by massive labeled 2D datasets and advancements in 2D vision models, but less success is witnessed on 3D vision tasks. This dissertation proposes innovative approaches to enhance the robustness of DNNs for 3D understanding and in 3D settings. The research focuses on two main areas: adversarial robustness on 3D data and setups, and the robustness of DNNs to realistic 3D scenarios. Two paradigms for 3D understanding are discussed: representing 3D as a set of 3D points and performing 2D processing of multiple images of the 3D data.

Many real-world events do not have outcome labels. For example, the fraudulence of a transaction remains unknown until it is discovered.

Delivering healthcare to patients in their homes is shaping the future of healthcare, as it offers better access to healthcare for people who live far from hospitals. It also facilitates the early detection of diseases and the prevention of complications that might otherwise require hospitalization, while simultaneously reducing costs for patients and healthcare systems. Nevertheless, the utilization of machine learning and health sensors for clinical purposes within the home environment requires addressing certain challenges, such as simplifying compliance procedures, ensuring that the digital representations of patients are comparable to the established medical gold standard, and preserving data privacy. In this talk, I will discuss how to develop new algorithms and models with health sensors to capture rich, continuous representations for in-home healthcare applications that address all these challenges.

We study the use of a nonlinear projection operator in the development of a novel function space approach for the optimization of trajectory functionals.

Complex systems and software engineering, and associated challenges become increasingly important for the well-being and safety of our society of humans.

Si photonics has been developed to enable the next generation tele- and data-communications for its high performance, making use of the mature silicon CMOS technologies.

Although technical challenges are still daunting, the clinical utility of neuroprosthetics has increased dramatically over the past few years. This has been accomplished through the convergence of numerous disciplines, which have individually added fundamental understanding/capabilities to systems that interface with the human body to restore senses and movement, or treat prevalent diseases that have currently no foreseeable cure. Among these, predictive multiscale computational modeling methods have greatly aided in the design of neuroprosthetics by embracing the complexity of the nervous system, which span multiple spatial scales, temporal scales, and disciplines. In this talk, we will cover some of the recent advances in bioelectromagnetic systems for healthcare, with a particular focus on visual and hippocampal prosthesis, peripheral neuroprosthetics, and sensors.

Gene therapy, particularly, genome editing therapy can potentially be used to treat many genetic diseases including some currently without a cure.

Large-scale MIMO communication (at sub-6GHz, mmWave, and sub-THz bands) is a key enabler for 5G, 6G, and beyond. Scaling MIMO systems, however, is subject to critical challenges, such as the large channel acquisition and beam training overhead and the sensitivity to channel estimation errors (especially at lower frequencies) and blockages (at higher frequencies). These challenges make it difficult for MIMO systems to support applications that have high mobility and strict reliability constraints. In this talk, I will first motivate the use of machine learning and sensory data to address these challenges. Then, I will present a few key machine-learning roles, enabling datasets, and recent hardware proof-of-concept prototypes that demonstrate the machine-learning gains in real-world environments.

The pressure to achieve carbon-free grids in the near future is driving the rapid integration of renewable energy sources into the grid.

Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms.

Visible VCSELs have been attracting researchers' interest for potential applications as light sources for optical storage, laser printers, projectors, displays, solid-state lighting, optical communications, biosensors, and so on.

In this dissertation, three different coupled systems of volume integral and hydrodynamic equations are formulated to analyze electromagnetic scattering from composite plasmonic structures consisting of dielectrics, metals, or semiconductors.