Combinational logic simplification and design, MSI and PLD components, synchronous and asynchronous sequential design, algorithmic state machines, registers, counters, memory units, introduction to hardware design languages. Digital circuit and system design and analysis laboratory implementation. Prerequisite: CS 1210.
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Covers the techniques for the design, analysis and layout of digital CMOS circuits and systems. Major topics include MOSFET basics (structure and behavior of a MOSFET, CMOS fabrication, and design rules), detailed analysis of the CMOS circuits and systems (static behavior, ratioed vs. ratioless design), noise margins, computing rise and fall times, delay models, resistance. Prerequisite: Electrical Engineering Graduate student, Computer Science Graduate student, or Instructor permission. Cross-listed with: EE 5410.
Offers a rigorous introduction to Machine Learning and its application in the engineering sciences. Topics include supervised, unsupervised, and reinforcement learning. Introduces data-driven modeling techniques used in engineering and physical sciences. Applications include networks, power systems, mechanical dynamics, fluids, and device sensors. Provides introduction to computer vision for autonomy and robotics applications. Knowledge of calculus, linear algebra, probability is assumed. Credit not awarded for both CS 5540 and CS 5611. Cross-listed with: CS 5611.
Fundamentals of digital communications including PCM, source and channel coding, pulse shaping and modulation; wireless communications, modulation, antennas and link budgets; application of probability; related laboratory experience. Prerequisite: Graduate student or Instructor permission.