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Electrical Engineering

Welcome to the UNL Department of Electrical Engineering.

The EE Department has more than 25 faculty involved in research related to electronic materials, nano-technology, optical systems, communications, biomedical applications, signal processing, microelectronics design, energy systems, and electromagnetics.

Undergraduate students are encouraged to participate in research activities, and have opportunities to travel and present their research results. Graduate students are also strongly supported by the department concerning travel and participation in technical conferences and workshops.

The B.S. Degree in Electrical Engineering is accredited by the Engineering Accreditation Commission of ABET, For enrollment/graduation data, Click Here.



Graduate Student Funding

Nebraska Engineering wordmarkUNL recognized with 3 IEEE awards

The UNL IEEE PES/PELS/IAS Joint Student Branch Chapter won in three categories of IEEE's Industrial Applications Society Chapter and Membership Development awards and contests. Dingguo Lu, who leads the IEEE PES/PELS/IAS Joint Student Branch Chapter at UNL, thanked chapter members and the faculty who helped guide the group’s success, with honors including:

* Outstanding Student Branch Joint Chapter (2012 activity)
* Top honors in the 2013 Chapter Website Contest (view site at; webmaster Daihyun Ha is a UNL EE graduate student
* First prize, Graduate Student Thesis Contest, to Taesic Kim for “A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects.” Kim's thesis adviser Wei Qiao (Harold and Esther Edgerton Associate Professor with UNL Electrical Engineering) also advises the IEEE PES/PELS/IAS joint student branch chapter at UNL.

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Qiao earns NSF funding for optimizing wind energy systems

Wei Qiao, UNL’s Harold and Esther Edgerton Assistant Professor of Electrical Engineering, received a three-year, $359,852 grant from the National Science Foundation for research on Cognitive Prediction-Enabled Online Intelligent Fault Diagnosis and Prognosis for Wind Energy Systems. His team’s work will focus on the use of time and frequency domain data mining methods to effectively extract the features of faults in a wind turbine from the signals acquired from the wind turbine condition monitoring system; and study the use of artificial neural networks and machine learning for intelligently diagnosing and predicting the faults and lifetime, while quantitatively evaluating the physical condition, of the wind turbine using the extracted fault features.