UVa Class Schedule - Schedule for ECE 4501 - Fall 2020 (Unofficial, Lou's List)

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Schedule for ECE 4501 - Fall 2020

These data were not obtained from SIS in real time and may be slightly out of date. MouseOver the enrollment to see Last Update Time

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I continue to maintain this list of classes, now with UVA support! -- Lou Bloomfield, Professor Emeritus of Physics

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Electrical and Computer Engineering
ECE 4501	Special Topics in Electrical and Computer Engineering

	Digital Image Processing
Syllabus 11672	001	Lecture (3 Units)	Open	5 / 14 (5 / 14)	Miaomiao Zhang	MoWe 3:30pm - 4:45pm	Web-Based Course
	Electronic Instrumentation
Syllabus 12676	002	IND (3 Units)	Permission	0 / 30	Harry Powell	TBA	TBA
	Low Power Wireless Transceivers for IoT
Fall 2020 19197	003	Lecture (3 Units)	Open	14 / 18 (14 / 18)	Steven Bowers	TuTh 2:00pm - 3:15pm	Web-Based Course
	This course will explore both the hardware and communication standards for current low power wireless transceivers such as Bluetooth and Zigbee, as well as ongoing research into the low power transceivers of tomorrow that are being developed in research labs across the world today. A major emphisis of this course will be in learning how to efficiently find, read and critique academic research papers to enable you to become an independent learner.
	Advanced Embedded Computing Systems
	Real-time Embedded Systems
Website 19204	004	Lecture (3 Units)	Open	10 / 15 (10 / 15)	Homa Alemzadeh	TuTh 11:00am - 12:15pm	Web-Based Course
	This course provides the foundational knowledge and hands-on experience in design and validation of embedded computing systems, with a focus on embedded C programming and real-time operating systems for ARM® Cortex-M Microcontrollers. Topics include: embedded system architectures, hardware software interfacing, memory management, multitasking, interrupt handling, and real-time scheduling.
	Matrix Analysis in Engineering and Science
Syllabus 19321	005	Lecture (3 Units)	Open	3 / 10 (3 / 10)	Cong Shen	TuTh 9:30am - 10:45am	Web-Based Course
	The course provides an in-depth understanding of matrix analysis concepts, algorithms, and applications, including eigenvalues and eigenvectors, linear transformation, similarity transformations, commonly used factorizations, positive definite and semidefinite matrices, and positive and non-negative matrices. In particular, we will illustrate these concepts with specific applications in machine learning, control, signal processing, and optimization.