(Spring 2014, Fall 2012, Fall 2010)
This course covers fundamentals of pattern recognition and machine learning, including linear models for regression; linear models for classification, kernel methods, clustering, mixture models, Expectation Maximization (EM), Gaussian process, dimension reduction, Principal Component Analysis (PCA), PCA extensions, manifold learning
(Fall 2002-2003, Spring 2006, 2008, 2010, 2012, 2013, 2015, 2018)
This course covers advanced topics on the development of machine vision and image understanding techniques, including camera models, camera calibration, texture analysis, image segmentation, statistical clustering, Principle Component Analysis (PCA), manifold learning, hidden Markov models (HMMs) for gesture recognition.
(Spring 2002-2005, 2007, 2009, 2011, 2013, 2016, 2019)
This course introduces basic concepts and methodologies for digital image processing, including image formation and acquisition, image enhancement, image restoration, color image processing, image compression, and morphological processing.
This course is intended to build on the introductory DSP course and discusses more advanced topics. The content includes Discrete linear time-invariant (LTI) systems; Z-transform, design of FIR/IIR filters, discrete/fast Fourier transform (DFT/FFT); sampling theory, down-sampling and up-sampling, transform analysis of LTI system, structures of LTI systems, signal analysis using DFT.
(Spring 2006-2007, Fall 2012-2019)
The course is designed to prepare new Ph.D. students for research and study. ECE faculty will share their experiences and knowledge on succeeding in graduate school. This course will help new students to become engaged in the Ph.D. program and also make students aware of the expectations and opportunities for Ph.D. graduates.
(Spring 2007-2013, Fall 2007-2015)
This is a fundamental core course for all EE students. This course introduces continuous-time (CT) and discrete-time (DT) signals and systems, CT/DT linear time-invariant (LTI) systems, CT convolution integral, DT convolution sum, periodic signals, Fourier series, continuous-time Fourier transform (CTFT), sampling theorem, modulation/demodulation, multiplexing/demultiplexing.
(Fall 2013-2016, 2018, 2019)
Introduction to probability theory; Random variables, distribution and density functions; Operations on Single and multiple random variables; Pairs of random variables; Random process; Analysis of electrical systems using elementary concepts of probability, Statistical properties of electrical noise. Analysis and design of optimum linear systems.
ECEN4763 Introduction to Digital Signal Processing
This course introduces discrete linear time-invariant (LTI) systems, linear convolution, Linear constant coefficient difference equation (LCCDE), Z-transform, sampling theorem, digital filter design, discrete Fourier transform, fast Fourier transform, DSP applications.
This course provides the fundamental
theory of the basic building blocks that exist in all communication
systems, including amplitude modulation and demodulation, angle
modulation and demodulation, sampling and analogy-to-digital conversion,
performance analysis of communication
This course presents an introduction to the study and practice of engineering. The covered materials include the skills for students in the College of Engineering, Architecture and Technology (CEAT), expected engineering student behavior, tools needed by CEAT students, and the role of engineers in society. It also provides an introduction to engineering ethics, and the relationship of engineering to social, global, and contemporary issues.