ESE Featured Courses Spring 2017

ESE 310: Electric and Magnetic Fields I

Dr. Nader Engheta
Mondays, 4:30 – 7:30 PM


Prerequisite(s): PHYS 151 and MATH 114

This course examines concepts of electromagnetism, vector analysis, electrostatic fields, Coulomb's Law, Gauss's Law, magnetostatic fields, Biot-Savart Law, Ampere's Law, electromagnetic induction, Faraday's Law, transformers, Maxwell equations and time-varying fields, wave equations, wave propagation, dipole antenna, polarization, energy flow, and applications.



ESE 501: Networking – Theory & Fundamentals

Dr. Saswati Sarkar
Mondays and Wednesdays, 4:30 – 6:00 PM

Prerequisite(s): ESE 530 or equivalent

Networks constitute an important component of modern technology and society. Networks have traditionally dominated communication technology in form of communication networks, distribution of energy in form of power grid networks, and have more recently emerged as a tool for social connectivity in form of social networks. In this course, we will study mathematical techniques that are key to the design and analysis of different kinds of networks. First, we will investigate techniques for modeling evolution of networks. Specifically, we will consider random graphs (all or none connectivity, size of components, diameters under random connectivity), small world problem, network formation and the role of topology in the evolution of networks. Next, we will investigate different kinds of stochastic processes that model the flow of information in networks. Specifically, we will develop the theory of markov processes, renewal processes, and basic queueing, diffusion models, epidemics and rumor spreading in networks.

ESE 526: Photovoltaic Systems Engineering

Dr. Jorge Santiago
Wednesdays, 10:00 – 11:30 AM

Prerequisite(s): Permission of the Instructor. Please contact Dr. Santiago at santiago@seas.upenn.edu.

This course will present the engineering basis for photovoltaic (PV) system design. The overall aim is for engineering students to understand the what, why, and how associated with the electrical, mechanical, economic, and aesthetic aspects of PV system. The course will introduce additional practical design  considerations, added to the theoretical background, associated with pertinent electro-mechanical design.

 

ESE 676: Coding Theory

Dr. Richard Blahut
Tuesdays & Thursdays, 12:00 – 1:30 PM

Prerequisite(s): Math 240, Phys 150, ESE 224, Mathematical Aptitude

A course on coding theory for telecommunications. This course emphasizes the algebraic theory of cyclic codes using finite field arithmetic, decoding of BCH and Reed-Solomon codes, finite field Fourier ransform and algebraic geometry codes, convolutional codes and trellis decoding algorithms, graph based codes, Berrou codes and Gallager codes, turbo decoding, and iterative decoding belief propagation.

Dr. Richard Blahut is internationally recognized as a pioneer in the field of error-control codes.  In his 30 years in the Federal Systems Division at IBM, his work contributed to such projects as message transmission to the Tomahawk missile and data protection sent over the public broadcasting network. He has since received the prestigious IEEE Alexander Graham Bell medal "For contributions to error-control coding, particularly by combining algebraic coding theory and digital transform techniques."


ESE 680-002: Combinatorial Optimization

Instructor: Rakesh Vohra
Mondays & Wednesdays, 12:00 – 1:30 PM

Prerequisite(s): Permission of the instructor. Please contact Dr. Vohra at rvohra@seas.upenn.edu

This is a PhD course in Combinatorial Optimization. It assumes knowledge of linear algebra, linear programming and basic convexity. The course is targeted to PhD students but Masters and motivated Undergraduate students comfortable with the theorem/proof style of class are welcome. This course will cover: Integral Polyhedra – Sufficient conditions for a polytope to have integral extreme points; Network Flow and Matching Problems; Matroids and Polymatroids – An abstraction of the notion of linear independence; Extended Formulations – The use of auxiliary variables to obtain the convex hull of integer solutions; discrete convexity.