Teaching

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.

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.

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. PREREQUISITE: EECE 2201, EECE 2207 or BIOM 1720; MATH 2120 or MATH 3402.

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. PREREQUISITE: EECE 3201 and EECE 3203

Principles and applications of computer vision, advanced image processing techniques as applied to computer vision, shape analysis, and object recognition. The following topics will be covered: 1) image formation; 2) introduction to and review of deep learning methods for computer vision models; 3) models of multi-view geometry; 4) building a 3-D model from images; 5) dense motion estimation from optical flow; and 6) graphical models in computer vision.

Statistical methods for describing and analyzing random signals and noise; auto-correlation, cross-correlation, and spectral density functions; optimal linear filter theory. PREREQUISITE: EECE 6235 or permission of instructor.

Introduction to the foundations of non-physically based geometic models, various physically based models and deformable models along with brief introduction to graphics and openGL. Specific models governing particle interactions, and movement of hair, smoke, fire, and clothes will be covered. PREREQUISITE: graduate standing or permission of instructor.

Application of discrete transform theory to spectral analysis, digital filters, random signal analysis.

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

This course is structured to review mathematical preliminaries, provide necessary foundation in scientific computing techniques at the graduate level, and to prepare for advanced computing work in scientific, research and engineering problems. Early stage graduate students will gain experience in programming scientific computing techniques in MATLAB. The following topics will be covered: 1) Numerical linear algebra; 2) orthogonality and orthogonalization procedures; 3) eigenvalues and eigenvectors; 4) review of initial and boundary value problems involving differential equations; 5) finite difference approach to solving initial / boundary value problems, and Lax equivalence theorem; 6) integral equations and Green’s function; 7) numerical integration procedures; 8) basic iterative methods; 9) Krylov subspace methods; 10) preconditioning techniques for iterative methods; 11) solving nonlinear equations; 12) introduction to parallel programming and message passing interface; 13) multiresolution / multigrid techniques; 14) domain decomposition methods; 15) constrained and unconstrained optimization techniques; 16) introduction to wavelets; and 17) Fast Fourier Transform and Discrete Cosine Transform. 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 scientific computing.

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 od the finite element method; and 11) Student will complete a project work in their area of interest/research.

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.

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.