Introduces key concepts in biosensors and bioelectronics. The course starts from the basics of molecular and cellular biology, followed by discussions on important biomedical devices and system in electrical, optical, magnetic and mechanical domains. The class will focus on working principles, basic concepts, and important bio-EE applications. It has prerequisites on semiconductor devices and electronics.

This course introduces the students to the design of embedded systems with a focus in emerging cyber-physical systems and internet of things applications such as health care, connected vehicles, and augmented/virtual reality. This course presents the unique capabilities of embedded technologies, and takes a holistic approach to design end-to-end systems. These systems span various thrusts that cut across both horizontal and vertical architectural layers.

Optimization is ubiquitous in engineering and computer science. A recent application is the defeat of the world Go champion by the artificial intelligence algorithms executed in the Google?s AlphaGo system. Other application examples include deep learning network training, large data set clustering, and many engineering design problems.

This graduate-level course aims at familiarizing the students with some advanced concepts in applied probability and random process, with emphasis on their applications in science, engineering, and finance. The goal is to help graduate students to use stochastic tools in their research. The topics can be of interest to students in engineering/computer science, mathematics, and management. A previous course in probability is required.

With 5G networks being deployed worldwide, academia and industry are already shifting their attention towards 6G. This course focuses on the evolution, fundamentals, and challenges of 5G/6G wireless network design. Preliminary knowledge on networking and machine learning is encouraged.

This course will teach students basic experimental methodologies in electrical and computer engineering required by graduate students. The course will involve hands-on experimentation and lectures which train students in these areas. A student completing the course will be prepared to complete advanced graduate courses in electrical and computer engineering.

Quantum mechanical principles and formalism. Description of quantum systems and phenomena relevant to electrical and computer engineering. Common foundation for advanced study and work in semiconductor materials and devices, nanoelectronics, optical and quantum electronics, quantum information and computation, and other emerging applications. Prerequisite: ECE 244 or equivalent.

Electromagnetic fields in dielectric and lossy media, transmission lines, antennas and resonators treated with the concepts of duality, image theory, reciprocity, integral equations and other techniques. Boundary and initial value problems solved for several frequently encountered symmetries.

Elementary probability theory including random variables, p.d.f., c.d.f., generating functions, law of large numbers. Elementary stochastic process theory including covariance and power spectral density. Markov processes and applications.

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