400

Introduction to analog and digital communication systems with emphasis on modulation, demodulation, encoding, decoding, and synchronization techniques used in wireless systems. Python is used to simulate communication systems and to write a software defined receiver (SDR) for a real RF signal.

Modeling and control of continuous-time control systems. Topics include feedback, transfer functions, responses in the time and frequency domains, stability, and compensation. Applications include manufacturing and robotics.

Introduction to Verilog-based design process. Hierarchical modeling methodology. Basic Verilog language structures for modeling digital hardware functions. Modules and ports. Gate-level modeling. Data flow modeling. Behavioral modeling. Tasks and functions. Useful modeling techniques in digital system design. Component timing and delay modeling. Logic synthesis with Verilog HDL.

Introduction to topics in testing of digital systems. Physical circuit failures and fault modeling. Fault simulation and fault coverage. Algorithms for automatic test pattern generation. Introduction to Built-in self test. Testing of sequential circuits. Test application and response processing techniques. Design for testability. Includes an advanced testing project.

This course covers techniques used to process digital signals in applications such as audio filtering and speech recognition. Topics include analog-to-digital and digital-to-analog conversions, aliasing, quantization, discrete-time signals and systems, discrete-time Fourier transform, Z-transform, and digital filter design. MATLAB is used to demonstrate concepts and to process real signals.

Introduction to the hardware and software used in real-time digital signal processing (DSP) systems. Topics include analog-to-digital and digital-to-analog converters, DSP chip architecture, and special software techniques such as frame-based processing, circular buffering, digital filters, and the Fast Fourier Transform. Students will implement real-time DSP systems using C language and will run them on a DSP board.

Applications of electrical engineering in recording and modifying neural activity of the brain. Topicsinclude basics of brain imaging techniques such as electroencephalography (EEG), magneticresonance imaging (MRI), and functional magnetic resonance imaging (fMRI). Introduction to treatmentmethods utilizing electric and magnetic fields to alter brain activity such as repetitive transcranialmagnetic stimulation (rTMS).

A major design experience based on the knowledge and skills acquired in earlier course work and incorporating appropriate standards and multiple realistic constraints. Projects have some combination of the following characteristics: realism, communication, exposure, teamwork, learning, and related opportunities. 

Continuation of a major design experience based on the knowledge and skills acquired in earlier course work and incorporating appropriate standards and multiple realistic constraints. Projects have some combination of the following characteristics: realism, communication, exposure, teamwork, learning, and related opportunities. 

Selected study or project in electrical engineering for upper-division students. Must be arranged between the student and an individual faculty member and subsequently approved by the dean of engineering. No more than three hours of directed study taken at the University may be used for elective credits to satisfy degree requirements.

Credit Arranged.

Credit Arranged.

Faculty-directed student research. Before enrolling, a student must consult with a faculty member to define the project. May be repeated for credit. Course is graded A-F.