Signals and Systems (ENGIN 321) The concepts of signals and systems arise in all
areas of technology. This course provides an introduction to the analysis of
linear systems in the time and frequency domain (e.g., what is the output of a
system if we know the input and the impulse response function or the transfer
function of the system, how to characterize a system by stimulating it and
measuring the output signals, etc.). Students will learn about the input/output
differential or difference equation, the convolution theorem and its applications,
the continuous-time and discrete-time Fourier and Laplace transforms, and how
to use Matlab in solving problems. Semesters Taught: Fall 2017, Fall 2018 Probability and Random Processes (ENGIN 322) An introduction to probabilistic description via
the probability density function or distribution function and statistical
description via the ensemble average, variance, etc. of random signals as
applied to the analysis of linear systems. Key topics and concepts also
include: conditional probability, statistical independence, correlation,
sampling theory, confidence intervals, hypothesis testing, stationary and
ergodic processes, autocorrelation and cross-correlation functions, spectral
density, and their interconnections. Semesters Taught: Spring 2018, Spring 2019 Computer Architecture (ENGIN 446) An introduction to
computer architectures; analysis and design of computer subsystems including
central processing units, memories and input/output subsystems; important
concepts include datapaths, computer arithmetic, instruction cycles,
pipelining, virtual and cache memories, direct memory access and controller
design. Semesters Taught: Fall 2018 Computer Communication Networks (EC541) Graduate-level course on performance analysis of communication networks. The objective of the course is to introduce popular mathematical models of computer communications and analytic techniques to quantify critical performance issues. Topics covered include fundamental concepts in computer networking, the Poisson process, Queuing and delay models in communication networks, and Markov chains. Semesters Taught: Spring 2017 (Boston University) |
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