Global Arc

Machine learning for predictive data analytics; information-based learning; similarity-based learning; probability-based learning; error-based learning; deep learning; evaluation.

Communication systems have become a ubiquitous part of modern life. This course introduces students to the fundamental of digital communication and wireless systems. Topics include concepts from information, compression, channel, modulation, radio propagation to principles of wireless cellular, and WiFi systems. At the end of the semester, students are expected to gain a deep understanding of the basis of wireless communication systems and the connection between theoretical concepts and real-world systems.

This course is oriented around 20 practical questions in the social, economic, and technological networks in our daily lives. How does Google sell ad spaces and rank webpages? How does Netflix recommend movies and Amazon rank products? How do I influence people on Facebook and Twitter? Why doesn't the Internet collapse under congestion, and does it have an Achilles heel? Why does each gigabyte of mobile data cost $10, but Skype is free? How come Wi-Fi is slower at hotspots than at home, and what is inside the cloud of iCloud? In formulating and addressing these questions, we introduce the fundamental concepts behind the networking industry.

Introduces distributed algorithms to optimize networked systems in electronic, mechanical, or biochemical substrates and other methodologies of optimization, both structures and numerical algorithms, for a variety of engineering applications. Applications will be selectively drawn from the following: computer networking, Internet protocols, communication systems, signal processing, circuit design, controlled dynamic systems, computational geometry, and financial engineering. Two 90-minute lectures. Prerequisite: MAT 202. No previous exposure to optimization theory, algorithms, or any specific application areas is required.

The technology underlying secure transactions and safe interactions in a public Internet and wireless world. Humans interact daily with each other, with information, and with services through cyberspace. Topics include policy, economic, and social issues related to cyber security needs such as confidentiality, data integrity, user authentication, trust, non-repudiation, availability, privacy and anonymity, case studies in electronic commerce, denial of service attacks, viruses and worms, digital rights management, surveillance, and cyber-terrorism. Two 90-minute lectures.

This interdisciplinary course addresses technological, regulatory, economic, and social issues arising in the rapidly developing field of wireless communications. The course introduces students to a major technological trend that will be a significant force in worldwide commercial and social development throughout the 21st century. Prerequisites: MAT 103 or permission of instructor. Two 90-minute lectures.

This course will introduce the matrix form of quantum mechanics and discuss the concepts underlying the theory of quantum information. Some of the important algorithms will be discussed, as well as physical systems which have been suggested for quantum computing. Prerequisite: Linear algebra at the level of MAT 202, 204, 217, or the equivalent.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

Start by analyzing biological systems to understand the origins of some of the signals that they present. Develop circuit models of these systems to determine what instrumentation circuits are required at the interface so that the signals can be reliably acquired. Study analog circuit topologies based on MOSFETs for low-noise instrumentation and processing of the signals. Study digital topologies based on MOSFETs for extensive computations on the biological signals. Analyze the trade-offs between the analog and digital topologies. Emphasis is on design and analysis using circuit simulators.