EECE 4280: Electrical/Computer Engineering Design (Syllabus)

Course Description: Implementation of team design project as part of the culminating major design experience that requires application of electrical engineering and/or computer engineering concepts. Oral and written presentations required

EECE 7907/8907: Computational Science & Engineering (Syllabus)

Course Description: Scientific computing is a powerful approach to study and solve engineering, scientific, and interdisciplinary biomedical problems involving complex geometrical structure and function. The following topics are covered in this class to learn the theoretical foundation, to program and to use the finite element method to solve linear boundary value problems in 1-D and 2-D: 1) Review of tools and methods from ordinary differential equations, partial differential equations, and calculus of variation for solving boundary value problems; 2) Review of Hilbert and Banach spaces; 3) Overview of finite difference and finite element methods for solving boundary value problems; 4) Deriving strong and weak formulation, Galerkin approximation and matrix formulation; 5) Finite element formulation; 6) Conjugate gradient method and other numerical techniques for solving the finite element formulation; 7) Finite element formulation for solving 2-D boundary value problems; 8) Mesh generation; 9) Programming a finite element; 10) Convergence, exactness and error analysis of the finite element method; and 11) Student will complete a project work in their area of interest/research

A Computational Framework for the Structural Change Analysis of 3D Volumes of Microscopic Specimens

Glaucoma, commonly observed with an elevation in the intraocular pressure level (IOP), is one of the leading causes of blindness. The lamina cribrosa is a mesh-like structure that provides axonal support for the optic nerves leaving the eye. The changes in the laminar structure under IOP elevations may result in the deaths of retinal ganglion cells, leading to vision degradation and loss. We have developed a comprehensive computational framework that can assist the study of structural changes in microscopic structures such as lamina cribrosa. The optical sectioning property of a confocal microscope facilitates imaging thick microscopic specimen at various depths without physical sectioning. The confocal microscope images are referred to as optical sections. The computational framework developed includes: 1) a multi-threaded system architecture for tracking a volume-of-interest within a microscopic specimen in a parallel computation environment using a reliable-multicast for collective-communication operations 2) a Karhunen-Loève (KL) expansion based adaptive noise prefilter for the restoration of the optical sections using an inverse restoration method 3) a morphological operator based ringing metric to quantify the ringing artifacts introduced during iterative restoration of optical sections 4) a l2 norm based error metric to evaluate the performance of optical flow algorithms without a priori knowledge of the true motion field and 5) a Compute-and-Propagate (CNP) framework for iterative optical flow algorithms. The realtime tracking architecture can convert a 2D-confocal microscope into a 4D-confocal microscope with tracking. The adaptive KL filter is suitable for realtime restoration of optical sections. The CNP framework significantly improves the speed and convergence of the iterative optical flow algorithms. Also, the CNP framework can reduce the errors in the motion field estimates due to the aperture problem. The performance of the proposed framework is demonstrated on real-life image sequences and on z-Stack datasets of random cotton fibers and lamina cribrosa of a cow retina with an experimentally induced glaucoma. The proposed framework can be used for routine laboratory and clinical investigation of microstructures such as cells and tissues, for the evaluation of complex structures such as cornea and has potential use as a surgical guidance tool

EECE 7903/8903: Computational Science & Engineering II: Fluid Flow (Syllabus)

Course Description: Modeling of transport phenomena from governing physical principles (e.g. fluid flow) and numerical analysis of the underlying differential equations have a broad spectrum of applications ranging from biomedicine, engineering design and simulation, aerodynamics, weather forecasting to animated motion pictures. Topics covered will emphasize on a collective learning of physical phenomena and theoretical models that govern fluid flow, mathematical formulation, numerical analysis and visualization of fluid flow. Particular emphasis will be on mathematical development of finite-element methods for incompressible Navier-Stokes equations governing fluid flow in a non-moving domain. Class projects will be structured to aid learning key theoretical concepts and techniques such as implementation of numerical techniques and simulation of fluid flow, smoke and fire or a similar transport problem related to students’ research interests. Specific topics that will be covered are: 1) A general form of differential equation governing transport phenomena; 2) strong form of the incompressible Navier-Stokes equations governing fluid flow; 3) stability and oscillatory-solution issues with Galerkin finite element; 4) streamline-upwind/Petrov-Galerkin (SUPG) formulation; 5) residual-based variational multiscale formulation (RBVMS); 6) modeling laminar and turbulent flows; 7) finite element formulation of water, fire, smoke and viscous fluids; 8) error analysis; and 9) students will complete a project work in their area of interest / research. PhD students registering at the 8000 level will exhibit deeper understanding by submitting / presenting a research paper based on their projects or on more advanced topics in modeling transport phenomena

EECE 4279: Professional Development (Syllabus)

Course Description: Design, ethics, standards, participation in professional organizations; preparation for licensing; preparation for senior design project; contemporary issues and the impact of engineering solutions in a global, economic, environmental, and societal context

EECE 7220/8220: Scientific Computing (Syllabus)

Course Description: Review of scientific computing mathematical preliminaries. Topics include numerical linear algebra, orthogonality, eigenvalues, boundary value problems, integral equations and Green\u27s functions, numerical integration, basic iterative methods, preconditioning, parallel programming, and advanced topics

EECE 7905/8905: Computational Science & Engineering III: Fluid-Structure Interaction (Syllabus)

Course Description: Multiphysics simulations are useful for modeling the behavior of coupled systems governed by two or more physical laws and their interactions. Examples are modeling of blood flow in arteries and veins, pulmonary gas exchange and transport, hydrodynamics and aerodynamics during power generation and electro-thermal-structural interface during drug delivery. Emphasis of this course is on computational modeling of fluid-structure interaction in a moving domain. Topics covered will emphasize on deriving theoretical models from physical laws and constitutive equations governing fluid-structure interaction, and developing finite element procedures for modeling fluid-structure interaction in a moving domain. PhD students registering at the 8000 level will exhibit deeper understanding by submitting / presenting a research paper based on their projects or on more advanced topics in multiphysics

EECE 3203: Signals & Systems I (Syllabus)

Course Description: Introduction to continuous-time signals and systems in time and frequency domains; system analysis of linear, time-invariant systems using Laplace and Fourier transforms and Fourier series

EECE 3204: Signals & Systems II (Syllabus)

Course Description: Introduction to discrete-time signals and systems in time and frequency domains; frequency representation of signals using discrete Fourier series, discrete Fourier transforms and Z transforms

EECE 7251/8521: Random Signals & Noise (Syllabus)

Course Description: Statistical methods for describing and analyzing random signals and noise; auto-correlation, cross-correlation, and spectral density functions; optimal linear filter theory