prof.dr.ir. Geert LeusProfessor
Expertise: Signal processing for communications, with applications to underwater communications, cognitive radio, and multiple-input multiple-output (MIMO) systems. Signal processing for (compressive) sensing with applications to ultrasound imaging and radar. Distributed signal processing. Graph signal processing.Themes: Sensor network communication
|Group:||Circuits and Systems (CAS)|
|Department of Microelectronics|
Geert Leus was born in Leuven, Belgium, in 1973. He received the M.Sc. and Ph.D. degree in Electrical Engineering from the KU Leuven, Belgium, in June 1996 and May 2000, respectively. He was a Research Assistant and a Postdoctoral Fellow at the KU Leuven 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.B. 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 sensing. Recently, he has been working on distributed signal processing as well as graph signal processing.
Geert Leus received numerous awards including the 2021 EURASIP Individual Technical Achievement Award, a 2005 IEEE Signal Processing Society Best Paper Award, and a 2002 IEEE Signal Processing Society Young Author Best Paper Award. He was also a Distinguished Lecturer of the IEEE Signal Processing Society.
Geert Leus is a Fellow of the IEEE and a Fellow of EURASIP. He was a Member-at-Large of the Board of Governors of the IEEE Signal Processing Society, the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee, a Member of the IEEE Sensor Array and Multichannel Technical Committee, a Member of the IEEE Big Data Special Interest Group, a member of the EURASIP Signal Processing for Communications and Networking Special Area Team, and the Editor in Chief of the EURASIP Journal on Advances in Signal Processing. He was also on the Editorial Boards of the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, the IEEE Signal Processing Letters, and the EURASIP Journal on Advances in Signal Processing. Currently, he is the Chair of the EURASIP Signal Processing for Multisensor Systems Technical Area Committee, a Member of the IEEE Signal Processing Theory and Methods Technical Committee, an Associate Editor of Foundations and Trends in Signal Processing, and the Editor in Chief of EURASIP Signal Processing.
EE-BD Biomedical Devices (BD) profile
A profile in our MSc-EE program dedicated to biomedical devices. Biomedical devices are used for medical diagnosis, monitoring and treatment. They can be fixed, portable, wearable, implantable and injectable. They are active and thus embed electronics, computing and software
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.
EE4C03 Statistical digital signal processing
A second course on digital signal processing: random signals, covariances, linear prediction, 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.
Closed loop adaptive radar resource allocation
Design of a radar system to operate in a congested and contested environment
Three-dimensional Ultrasound Imaging Through Compressive Spatial Coding
Develop smart compressive coding masks to make 3D ultrasound imaging cheap and widely applicable.
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?
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: 16 Aug 2021