- Advanced Engineering Research Laboratory is fully equipped to conduct research in digital control,communications, and power electronics applications.
Center for Advanced Control Technologies equipped to conduct joint research projects with industry, giving students the opportunity to apply state-of-the-art technology in real-world problems.
Digital Communications Research Laboratory is equipped with electronics and communications instruments, high-speed workstations, computer simulation packages to conduct research projects in digital modulation, error-control coding, satellite and wireless communications, and spread spectrum communications.
Embedded Control Systems Research Laboratory focuses on the theoretical development and real-time implementation of control and signal processing algorithms. Theoretical directions that are of particular interest include optimal control, Kalman filtering, H-infinity control and estimation, neural networks, and fuzzy logic.
Mobile Computing Research Laboratory (MCRL) has the goal of investigating scalability issues, energy awareness, spectral efficiency and interoperability issues in mobile networks. Power reduction techniques in mobile embedded systems have been another important research area in MCRL.
- Power Electronics and Electric Machines Laboratory has recently been funded by National Science Foundation, Nasa Glen research Center and Fenn College of Engineering. It consists of seven state of the art test benches such as: Modular Lab - Volt Power Electronics and Electric Machines Training System, DSPACE controller boards, PWM converters, transducers, sensors, induction, synchronous and DC machines as well as instrumentation. It is fully equipped to conduct research in the power area.
- Secure and Dependable Systems Laboratory The mission of this laboratory is to advance the state of the art of fault– and intrusion– tolerance techniques for the next generation secure and dependable computer systems.
- Dependable Systems and Networks Research Lab engages in research that investigates ways of building complex software systems that scale well with code size, and more importantly, with distribution. At the same time, our work ensures that the size and complexity of the software does not preclude the ability to understand such systems.