Saudi Cultural Missions Theses & Dissertations
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Item Restricted Highly Resonant Nonlinear Wireless Power Transfer for Dynamic Charging(Saudi Digital Library, 2026) Alothman, Abdullah; Mortazawi, AmirThis dissertation investigates the use of nonlinear resonance to enhance the performance and robustness of near-field resonant wireless power transfer (WPT) systems. In general, WPT systems are sensitive to transmitter and receiver misalignment and distance variation, limitations that are particularly critical for applications such as implantable medical devices or electric vehicle charging, where stable power delivery is essential. The approach investigated in this research allows WPT systems to inherently achieve high efficiency and stable power delivery under dynamic operating conditions. A single-nonlinearity resonant WPT topology is developed for the first time for powering a left ventricular assist device (LVAD), introducing a novel methodology for synthesizing nonlinear characteristics to achieve a distance-robust operation. The proposed methodology is then applied to a dual-nonlinear resonator architecture, enabling the simultaneous stabilization of the output power and input impedance while suppressing frequency bifurcation without requiring frequency tracking. Furthermore, a complete characterization of the WPT system in terms of DC-DC performance was conducted. Overall, the proposed framework offers a scalable, passive, and self-adaptive solution for WPT in dynamic and mobile environments.12 0Item Restricted Leveraging Latent Models of Neural Population Dynamics for Efficient and Accurate Brain State Estimation(University of California, 2023-09-15) Alothman, Abdullah; Gilja, VikashIt has been demonstrated that the neural population activity is often dominated by a few prominent latent neural modes in low-dimensional space that capture a significant fraction of the population covariance. Previous studies showed that those latent modes can be used as basic building blocks for brain-computer interfaces (BCI). In fact, it was demonstrated that through modeling those latent modes into variables, one can drive an online BCI with a performance surpassing traditional neural-observation-driven BCI. Additionally, it has been proposed that these neural modes are stable over long periods of time with respect to the intended behavior. To that end, we propose in this thesis methods and techniques that aim to enhance the performance of these latent-variable-driven BCIs in a multitude of ways. In the first chapter, we propose an unsupervised compression technique for neural interfaces that aims to compress input neural features by minimizing the loss of information between observations and modeled latent modes. Our results suggest the potential of design neural interfaces with sublinear power scaling with number of input electrodes which could enable power-efficient large-scale neural recordings. In the second chapter, we utilize latent modeling in a multimodal study featuring Electrocorticography (ECoG) and calcium imaging to show that you can predict the activity of modality using the other. We predict the latent modes of the calcium response using spectral features of ECoG recording then project decoded modes into the observation space to reconstruct single-cell activity of calcium response. In the last chapter, we extend this work in multimodal analysis to propose a generalized framework that unifies multiple modalities. One can leverage the unique strength of each modality by projecting them into a common latent space, which can be utilized to drive a brain-computer interface more accurately.32 0