COURSES
Introduction to discrete
and continuous time signals and systems. Time-domain signal
representations, impulse response of linear time-invariant (LTI)
systems, and convolution. Frequency domain signal representations,
frequency response of LTI systems, and Fourier analysis. Filtering of
continuous and discrete time signals. Sampling and discrete time
processing of analog signals. Laplace-transform domain analysis of
continuous-time LTI systems. Exercises using MATLAB.
Credits: 4 Prerequisites: MATH.
107
Review of discrete-time
Fourier transform and sampling theory. Interpolation and decimation.
Sampling in the frequency domain. The discrete Fourier transform and
FFT, computation of FFT, Fourier analysis of signals using the FFT,
spectral estimation and windows. The Z-transform, digital filtering,
minimum-phase and generalized linear phase systems, structures for
digital filters, FIR filter design methods, IIR filter design methods.
Credits: 3 Prerequisites: ELEC. 201
Multi-dimensional
signals, transforms, and systems; multi-dimensional sampling theory and
sampling structure conversion. Fundamentals of color, human visual
system, digital video and 3D video including basic file formats, resolutions, and bit rates for various digital video applications.
Image filtering, edge and corner detection, enhancement, denoising, and
restoration. Motion analysis and estimation using 2D and 3D
models. Motion-compensated filtering methods for noise removal,
de-interlacing, and resolution enhancement. Digital image and video
compression methods and standards, including lossless compression,
JPEG/JPEG2000, MPEG-1/2, AVC and HEVC. Scalable video coding and
stereo/multi-view video coding extensions of AVC and HEVC.
Credits: 3 Prerequisites: ELEC. 303 or consent of the instructor
A capstone course to demonstrate
knowledge and skills attained by completing a
design project. All projects provide
the opportunity to incorporate concepts learned throughout the ELEC curriculum
into a real-life Project development.
Projects also provide an opportunity to organize, manage and complete a
product development in its entirety while considering ethics and
intellectual property issues; improve communication, teamwork and presentation
skills.
Credits: 4 Prerequisites: ELEC. 310, ELEC 311, and ELEC 316
Discrete and continuous probability spaces, conditional
probability. Random variables, probability mass function (pmf), probability
density function (pdf), characteristic function, independence of random variables,
conditional pmf/pdf, multiple/vector random variables, functions of multiple
random variables, Law of large numbers, Central Limit Theorem. Discrete-time
random processes, continuous-time random processes, auto and cross correlation
functions. Stochastic calculus. Stationary random processes, ergodicity, power
spectral density, spectral estimation; white noise processes, Linear minimum
mean square estimation. Markov processes, Markov chains, hidden Markov models.
Credits: 3 Prerequisites: ENGR. 200
College of Engineering Course Web Pages