prof.dr.ir. Geert LeusProfessor
Expertise: Signal processing for communication and networking, with applications to underwater communication, cognitive radio and sensor networks.Themes: Wireless communication
|Group:||Circuits and Systems (CAS)|
|Department of Microelectronics|
Geert Leus was born in Leuven, Belgium, in 1973. He received the MSc degree in Electrical Engineering and the PhD degree in Applied Sciences from the Katholieke Universiteit Leuven (KUL), Belgium, in June 1996 and May 2000, respectively. He was a Research Assistant and a Postdoctoral Fellow at the KUL from October 1996 till September 2003. During the summer of 1998, he visited Stanford University (with Prof A. Paulraj), and from March 2001 till May 2002, he was a Visiting Researcher (with Prof. G. Giannakis) and Lecturer at the University of Minnesota. Currently, he is a Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology (TU Delft), The Netherlands.
His research interests lie in the broad area of signal processing for communications and networking. Recently, he has been working on distributed signal processing for self organizing wireless networks, with applications in cognitive radio and sensor networks. The topics he addresses include energy efficiency, spectrum sensing, spectrum utilization and information processing.
Geert Leus received numerous awards including a 2002 IEEE Signal Processing Society Young Author Best Paper Award and a 2005 IEEE Signal Processing Society Best Paper Award.
He is an IEEE Fellow (for contributions in Signal Processing for Communications) and is Editor-in-Chief for the the EURASIP Journal on Advances in Signal Processing. In the past, he served as the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee (SPCOM TC). He has also served on the Editorial Board of the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, and the IEEE Signal Processing Letters.
EE4530 Applied convex optimization
Applied convex optimization: role of convexity in optimization, convex sets and functions, Canonical convex problems (SDP, LP, QP), second-order methods, first-order methods for large-scale problems, ADMM.
EE4C03 Statistical digital signal processing
A second course on digital signal processing: random signals, covariances, linear prediction, Levinson and Schur algorithm, spectrum estimation, optimal filtering, Wiener and Kalman filters, LMS and RLS algorithm
ET4147 Signal processing for communications
Signal separation and parameter estimation using arrays of sensors.
EE2521 Digital signal processing
(not running) First course in digital signal processing: sampling, filter design, filter structures, DFT, multirate filters
Task-cognizant sparse sensing for inference
Low-cost sparse sensing designed for specific tasks
Data reduction and image formation for future radio telescopes
The future SKA telescope will produce large amounts of correlation data that cannot be stored and needs to be processed quasi real-time. Image formation is the main bottleneck--can compressive sampling and advanced algebraic techniques help?
Quality of Service-driven Channel Selection for Cognitive Radio Networks
Improving the reliability of disaster relief networks using cognitive radio with strict QoS requirements.
Sensing Heterogeneous Information Network Environment
How can heterogeneous resources (people, mobile sensors, fixed sensors, social media, information systems, etc.) self-organize for answering information needs?
Autonomous, self-learning, optimal and complete underwater systems
Can we develop robust, cooperative and cognitive communication for Autonomous Underwater Vehicles?
Dependable Distributed Sensing Systems
The D2S2 project aims at developing an algorithmic framework for operating large-scale distributed sensor systems.
Signal Processing for Self-Organizing Wireless Networks
Mathematical foundations to develop large self-organizing networks based on cognitive radio devices that are capable of sensing the radio spectrum and adapt accordingly.
Smart moving Process Environment Actuators and Sensors
Can an RF sensor network be developed for an underwater environment (chemical reaction tank)? Main issues are localization and UWB communication. This is a difficult environment for RF.
Last updated: 23 Dec 2016