LOUIS University of Alabama in Huntsville
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Online Chebyshev-based pseudospectral methods for nonlinear system identification
Full text linkNonlinear system identification plays a crucial role in developing effective control for various systems, including autonomous vehicles, robotics, and industrial process control. Acquiring the system model with minimal sensing and computation can significantly reduce control costs in these applications. Traditional adaptive control techniques often rely on uniform or high-frequency sampling, which may become impractical or computationally expensive for rapidly changing or high-dimensional systems. While Chebyshev-based pseudospectral (PS) methods have proven highly effective in offline contexts, their direct online implementation faces several challenges. Successfully approximating system dynamics online requires prior information about the approximation intervals to compute Chebyshev nodes for data collection, as well as the basis polynomial order or node count to ensure the desired approximation accuracy. Furthermore, accurate measurement or estimation of state derivatives is critical for reliably computing Chebyshev coefficients. This dissertation presents a suite of online pseudospectral methods that utilize Chebyshev polynomial basis functions for nonlinear system identification. It employs non-uniform (aperiodic) sampling at Chebyshev nodes to take advantage of the minimax error property, ensuring the desired approximation accuracy. These methods aim to reduce sampling frequency while adhering to strict theoretical limits on parameter estimation errors in real-time scenarios. In the first approach, the nonlinear system dynamics are approximated using a state-based Chebyshev polynomial basis. To apply the PS method in real time, the time horizon is divided into intervals, allowing for the determination of sampling points based on Chebyshev nodes within a moving time window. A least-squares (LS) approach is employed to estimate the coefficients of the Chebyshev basis for accurate approximation. The state measurements used to construct the Chebyshev basis, in the first approach, are not the nodes of the polynomial. Therefore, the approximation does not guarantee an optimal error bound. To overcome this limitation and ensure the desired approximation accuracy, in the second approach, the Chebyshev polynomial basis is formulated as a function of time. The state and its derivative are measured and computed, respectively, at the Chebyshev time nodes, preserving the minimax property of the polynomials. Since this basis function results in a single-variable format, it significantly reduces computational overhead. The Chebyshev coefficients were estimated using the LS-based method. This approach still required state derivatives, as in the first approach, which necessitated additional samples near each node, increasing the sampling frequency. To address the need for state derivative computation, which is known to amplify noise, the proposed online PS approach is extended to first approximate the system dynamics in the third approach. The system dynamics are recovered analytically from the state approximation. To further improve computational efficiency in parameter estimation, the Fast Chebyshev Transform (FCT) was adapted to the moving window scheme. To guarantee the desired approximation accuracy in each proposed method, an adaptive node selection scheme based on the corresponding average approximation error was implemented. Rigorous analysis, based on Lyapunov arguments and bounded-error proofs, confirmed that both parameter error and function error remained finite over time. Each approach was validated through simulation studies, demonstrating a reduced sensing frequency while maintaining approximation accuracy. Overall, these online Chebyshev pseudospectral methods represent a novel approach for real-time, resource-constrained environments, offering a promising avenue for future research and industrial deployment where high accuracy and minimal sensing are equally essential
Efficient lossless compression of malaria infected image datasets using vision transformers and deep autoencoders
Full text linkIn the field of medical imaging, lossless compression techniques play a critical role in maintaining the fidelity of medical data, while improving the efficiency of data storage and transmission, especially in telemedicine applications. This thesis addresses the development of a novel machine learning-based method for lossless compression of images of erythrocytes infected with malaria. The proposed method has two stages. In the first stage, a Vision Transformer, a computer vision model that can learn long-range dependence, is employed to separate erythrocyte images into two categories: infected and uninfected. This classification stage is essential as it allows the subsequent compression approaches to be tailored for each category. In the second stage, the compression of these images is conducted using two dedicated deep autoencoders—one for each category. These autoencoders are specifically trained to significantly reduce the dimensionality of the input data. To achieve lossless compression, the residue, which is the difference between the original images and the images reconstructed by the autoencoder, undergoes further compression through Huffman coding. Simulation results demonstrate that the proposed compression method can achieve lower bit rates and greater compression ratios than conventional compression techniques like JPEG 2000, JPEG-LS, and CALIC. The thesis provides detailed descriptions and thorough analysis of each component of the proposed data compression system, while discussing the broader impacts of further development of the techniques for malaria health informatics
Katherine Parr: More Than Just the One Who Survived
Full text linkKatherine Parr is often remembered merely as the sixth and final wife of King Henry VIII, known for being the one to survive the infamous monarch. Yet, condensing her legacy to this one role overlooks her impact on our history. Parr was a devoted scholar, profound author, beloved stepmother, advocate for education, and avid Protestant reformer. Her contributions, rooted in her beliefs, make it clear that she is perhaps one of the most influential queens in history.https://louis.uah.edu/honors-399/1023/thumbnail.jp
Cholera in London\u27s Soho Neighborhood
Full text linkBy 1831, the neighborhood of Soho in London was plagued with Cholera. The real problem, though, was not how deadly this disease was; it was how much medical misinformation was spread. My project is to analyze the effect this misinformation had and why such drastic measures were necessary for John Snow to take.https://louis.uah.edu/honors-399/1020/thumbnail.jp
Enhancing Traffic Inicident Management with Large Language Models and Real-Time Data
No full texthttps://louis.uah.edu/rceu-hcr/1504/thumbnail.jp
Predicting ALD Film Thickness and Uniformity Using Machine Learning
No full texthttps://louis.uah.edu/rceu-hcr/1511/thumbnail.jp
Exploring Moral Distress in Air Medical Clinicians
No full texthttps://louis.uah.edu/rceu-hcr/1516/thumbnail.jp