Signal processing is a mathematically rigorous speciality of electrical engineering that involves methods that transform signals into information. Examples of signals include speech, underwater acoustics, inertial sensors, biomedical signals, and antenna arrays. Signal processing can occur in analog hardware, digital hardware, microprocessors, and in media that affects signals before they are transduced such as optical filters. Successful graduates have a common core of knowledge that includes linear systems, transforms, and filter design. Those wishing to gain deeper knowledge may gain expertise in adaptive filters, state space methods, statistical signal processing, and array processing. Closely related and supporting areas include communications and control systems.
A graduate degree is usually required in order to obtain an engineering position that involves the application and development of signal processing methods. Our graduate track is designed to provide this knowledge and prepare students for successful careers for both those seeking a job in industry upon graduation and those planning to continue on to graduate programs and research careers.
New approved requirements
- EE 520 Random Processes
- EE 521 Discrete-Time Signal Processing I
- EE 522 Discrete- Time Signal Processing II
- EE 510 (529) Project
Depth and Breadth Courses
- EE 517 Instrumentation & Sensing
- EE 523 Estimation & Detection I
- EE 524 Estimation & Detection II
- EE 525 SSP I: Spectral Estimation
- EE 526 SSP II: Linear Estimation & Adaptive Filters
- EE 510 (527) Sensor Array Processing
- EE 510 (528) State Space Tracking
The PSU ECE Department has many faculty who apply or develop methods of signal processing as part of their teaching and research. A brief background on each faculty member is below.
James McNames is the Director of the Signal Processing Track and founder of the Biomedical Signal Processing Laboratory. He is interested in applying statistical signal processing methods to biomedical problems, and recently has focused on the analysis of inertial measurement units and other sensors that can be used to quantify human movement.
Eric Wan is the Director of the Biomedical Signal Processing Lab, and is doing research on statistical and adaptive signal processing, with a focus on nonlinear Kalman filtering and probabilistic inference. Recent projects involve developing technologies that can be used to track and monitor the elderly in their homes or in assistive living environments. Previously, he was an Associate Professor in the Biomedical Engineering Department at Oregon Health & Science University.
YC Jenq is a Professor and his expertise includes (1) Modeling and dynamic characterization of A/D and D/A converters, (2) Theory and applications of non-uniform sampling signals and systems, and (3) High speed signal processing with massively parallel processors. He was elected IEEE Fellow in 1993 for "contributions to dynamic testing of analog-to-digital converters and to digital spectral analysis of non-uniform sampling signals".
Lisa Zurk is a Professor of Electrical and Computer Engineering and the Director and founder of the Northwest Electromagnetics and Acoustics Research Laboratory (NEAR-Lab). Her research is in physics-based processing applied to sensing applications, particularly in the domain of terahertz imaging and spectroscopy, and underwater acoustical signal processing.
Dan Rouseff is interested in signal processing applied to acoustic signals measured underwater. He has been Chief Scientist for two at-sea experiments testing concepts in underwater acoustic communications and beamforming. Previously, he was a Senior Faculty Fellow in the Acoustic Signal Processing Branch of the Naval Research Laboratory in Washington DC.
Rick Campbell teaches analog and RFIC Design, and has been Principal Designer at TriQuint Semiconductor and Cascade Microtech. He is primarily interested in the analog signal processing front-end of scientific and measurement instrumentation from DC to Daylight.
Martin Siderius is the Director of the NEAR-Lab, and is interested in acoustic signal processing for single sensors and sensor arrays. These techniques are primarily used for sonar applications such as remote sensing the ocean environment, sound localization and tracking and acoustic communications.