Research at the department ELEC

"ELEC" stands for "Fundamental Electricity and Instrumentation" (in dutch: "Algemene Elektriciteit en Instrumentatie") and the name corresponds with the educational and research tasks and objectives of the department. The main research activity of the department is the development of new measurement techniques using advanced signal processing methods, embedded in an identification framework. There are no formal research groups in the department ELEC. There are 3 research teams that cooperate intensively with each other. Team A: Data driven modeling (Rik Pintelon, J. Schoukens) Team B: Applied signal processing for engineering, telecommunications and Microwave systems (Gerd Vandersteen, Yves Rolain and Leo Van Biesen) Team C: Structured low-rank approximation (Ivan Markovsky)

Overview Research Topics

The long term objective of the department is the realization of the IMMI project (Interpretation by Measuring, Modelling and Identification), since 1989. Beside this main project, which covers a broad spectrum of several related activities, also some other independent research topics (national or European funding) are at stake.

User-friendly Identification of massive MIMO systems

Accurate mathematical models are essential prerequisites in a myriad of engineering disciplines. In many cases, future high-performance systems will likely exhibit significant resonant behavior while comprising more and more inputs and outputs.

Filter-based regularization for impulse response modeling

In the last years, the success of kernel-based regularization techniques in solving impulse response modeling tasks has revived the interest on linear system identification [1]. An alternative perspective on the same problem is discussed in [2], where a filter-based approach is proposed to...

Decoupling Multivariate Nonlinearities in System Identification

Nonlinear system identification often makes use of coupled multiple-input-multiple-output static nonlinear functions to represent nonlinearities [5, 6]. For the sake of model interpretability and to limit the rapid increase of parameters it is desirable to find a parsimonious description. A...

User-friendly estimation of nonlinear state-space models

State-space models are a natural choice for describing multiple input multiple output (MIMO) systems. Although linear state-space models are often used, all physical systems behave nonlinearly to some extent. If the nonlinear distortions are too large, linear state-space models are insufficient...

Battery modelling project “BATTLE” (IWT-SBO)

This project (BATTery modelling of Lithium chemistries based on an Eclectic approach) addresses the development of a powerful dynamic Li-ion based battery modelling unit for traction applications. This goal can be achieved by developing and combining dedicated models, using innovative numerical...

Kernel based regression to estimate Linear Time-Varying systems

An estimator has been developed for linear time-varying systems, described by differential equations with time-varying coefficients. These coefficients are estimated via kernel based regression (closely related to Gaussian process regression [3] and Least Squares Support Vector Machines [4])....

Low-rank approximations of tensors via structured matrix methods

We study a new connection between higher-order tensors and affinely structured matrices, in the context of low-rank approximation. In particular, we reformulate the tensor low multilinear rank approximation problem as a structured matrix low-rank approximation, the latter being an extensively...


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