Abstract: Detection and estimation problems involve challenging tasks that often demand real-time, accurate results. Algorithms able to produce highly accurate results are often computationally expensive or inefficient. Naturally, we need to tailor algorithms to the specific needs of problems to optimally trade off between computation and accuracy. To explore this ever-present tradeoff, this dissertation describes three distinct problems in detection and estimation and our contribution to the decision-making process for choosing the best algorithms for solving these problems.
First, we look at tradeoffs involved in designing a low-cost, camera-based autonomous gait acquisition and analysis system for inspecting gait impairments in mice. Specifically, we give a detailed description of our detection and classification algorithms for gait-event detection and gait-parameter extraction. Using the videos acquired in a live-animal study, we validate the performance of our system for assessing recovery in a mouse model of Parkinson's disease.
Next, we analyze the tradeoffs involved in designing a modified data association algorithm for tracking multiple objects using measurements of uncertain origins, such as radar detection with false alarms and missed detection. Specifically, we explore the performance of the distance-weighting probabilistic data association approach in conjunction with the loopy-sum product algorithm and, using simulation data, we analyze its performance in terms of tracking accuracy and computation against other state-of-the-art data association methods for tracking multiple targets in clutter.
Finally, to address the ill-conditioning of linear minimum mean square error estimation, we develop four approximate Wiener filter formulas that do not directly involve the inverse of the observation covariance matrix. Using real data, we evaluate the performance-complexity tradeoff for our approximated filters.
The common underlying theme that connects our solutions to these district problems is that our decisions for selecting various parameters in each solution are based on the performance-computation tradeoff. Throughout this dissertation, we employ various methods to handle this tradeoff, such as receiver operating characteristics analysis and line-search procedure. Our analysis is beneficial for choosing the best algorithm to optimally trade off between performance and computation.

Adviser: Edwin Chong
Co-Adviser: N/A
Non-ECE Member: Ronald Tjalkens, ERHS
Member 3: Ali Pezeshki, ECE
Addional Members: Renzo Cavalieri, MATH

Publications:
[1] Hammond, Sean L., Katriana A. Popichak, Xi Li, Lindsay G. Hunt, Evan H. Richman, Pranav U. Damale, Edwin KP Chong, Donald S. Backos, Stephen Safe, and Ronald B. Tjalkens. "The Nurr1 ligand, 1, 1-bis (3′-indolyl)-1-(p-chlorophenyl) methane, modulates glial reactivity and is neuroprotective in MPTP-induced parkinsonism." Journal of Pharmacology and Experimental Therapeutics 365, no. 3 (2018): 636--651.

[2] Hammond, Sean L., Collin M. Bantle, Katriana A. Popichak, Katie A. Wright, Delaney Thompson, Catalina Forero, Kelly S. Kirkley, Pranav U. Damale, Edwin KP Chong, and Ronald B. Tjalkens. "NF-κB signaling in astrocytes modulates brain inflammation and neuronal injury following sequential exposure to manganese and MPTP during development and aging." Toxicological Sciences 177, no. 2 (2020): 506--520.

[3] Damale, Pranav U., Edwin KP Chong, and Tian J. Ma. "Performance study of distance-weighting approach with loopy sum-product algorithm for multi-object tracking in clutter." Sensors 21, no. 7 (2021): 2544.

[4] Damale, Pranav U., Edwin KP Chong, Sean L. Hammond, and Ronald B. Tjalkens. "A Low-Cost, Autonomous Gait Detection and Estimation System for Analyzing Gait Impairments in Mice." Journal of Healthcare Engineering 2021 (2021).

[5] Damale, Pranav U., Edwin KP Chong, Louis L. Scharf, "Wiener
filtering without covariance matrix inversion," Proceedings of the
IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP 2023), Rhodes Island, Greece, June 4--10, 2023.

[6] Damale, Pranav U., Edwin KP Chong, Louis L. Scharf, "Wiener filter
approximations without covariance matrix inversion," IEEE Open Journal
of Signal Processing, to appear.

Program of Study:
GSTR 600
ECE 652
ECE 752
MATH 501
PH 517
STAT 620
ECE 795
ECE 799