Teaching


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    1. EECE 4279: Professional Development

      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.

    2. EECE 4280: Electricl/Computer Engr Design

      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.

    3. EECE 3203: Signals and Systems I

      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.

    4. EECE 3204: Signals and Systems II

      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

    5. EECE 7216 and EECE 8216: Computer Vision

      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.

    6. EECE 7251 and EECE 8251: Random Signals and Noise

      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.

    7. EECE 7224 and EECE 8224: Physically Based Animation

      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.

    8. EECE 7215 and EECE 8215: Digital Signal Processing

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

    9. EECE 7220 and EECE 8220: Scientific Computing

      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.

    10. Special Topic: Computational Science and Engineering--Part 0: Scientific Computing

      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.

    11. Special Topic: Computational Science and Engineering--Part 1

      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.

    12. Special Topic: Computational Science and Engineering--Part 2: Fluid Flow

      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.

    13. EECE 7903 and EECE 8903: Computational Science and Engineering--Part 3: Fluid-Structure Interaction

      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.



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    Semester Course # Course Title # Survey Participants SETE Average
    Spring 2023 EECE 4279 Professional Development 4/10 4.55/5.0
      EECE 4280 Senior Design 22/28 4.71/5.0
    Fall 2022 EECE 3203 Signals & Systems I 15/25 4.34/5.0
      EECE 3204 Signals & Systems II 13/16 4.70/5.0
    Spring 2022 EECE 3203 Signals & Systems I 13/24 4.42/5.0
      EECE 3204 Signals & Systems II 16/26 4.58/5.0
    Fall 2021 EECE 3203 Signals & Systems I 22/29 4.55/5.0
      EECE 3204 Signals & Systems II 7/15 4.51/5.0
    Spring 2021 EECE 3204 Signals & Systems II 16/18 3.95/5.0
      EECE 7/8216 Computer Vision 12/16 4.72/5.0
    Fall 2020 EECE 3204 Signals & Systems II 16/17 4.65/5.0
      EECE 7/8251 Random Signals & Noise 14/16 4.6/5.0
    Spring 2020 EECE 3204 Signals & Systems II 17/18 4.66/5.0
      EECE 7/8224 Physically based animation 12/15 4.52/5.0
    Fall 2019 EECE 3204 Signals & Systems II 22/24 4.65/5.0
      EECE 7/8251 Random Signals & Noise 12/15 4.71/5.0
    Spring 2019 EECE 3204 Signals & Systems II 16/19 4.92/5.0
      EECE 7/8215 Digital Signal Processing 11/11 4.57/5.0
    Fall 2018 EECE 3204 Signals & Systems II 11/12 3.84/5.0
      EECE 7/8220 Scientific Computing 13/18 4.20/5.0
    Spring 2018 EECE 3204 Signals & Systems II 12/17 3.75/5.0
    Fall 2017 EECE 7/8251 Random Signals & Noise 6/11 4.46/5.0
    Spring 2017 EECE 7/8216 Computer Vision 7/10 4.62/5.0
    Spring 2016 EECE 7/8903 Physically based animation 14/15 4.73/5.0
    Fall 2015 EECE 7/8902 Computational Science & Engineering Part 0: Scientific Computing 13/13 4.78/5.0
    Spring 2015 EECE 7/8905 Computational Science & Engineering Part 3: Fluid-Structure Interaction 1/5 5.0/5.0
    Fall 2014 EECE 7/8903 Computational Science & Engineering Part 2: Fluid Flow 9/9 4.57/5.0
    Spring 2014 EECE 7/8907 Computational Science & Engineering Part 1 9/11 4.89/5.0