## 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.

### Estimation of respiratory impedance at low frequencies using the forced oscillation technique

The forced oscillation technique (FOT) is a non-invasive measurement technique to characterize the impedance of a respiratory system (Zrs). Zrs is defined as a frequency dependent ratio of pressure at the airway opening (pao) to air flow (qe) induced by Zrs.

### 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...

### Identification of Nonparametric Models for Nonlinear Systems

This project aims at the development of efficient methods for the identification of nonparametric Volterra models, which exploit prior information about the system dynamics to be modeled.

### Data-driven modeling of nonlinear systems in the presence of dominant model errors

State of the art data-driven modeling considers data to be perturbed by a noise model. The use of noise models is well studied in the field of linear time-invariant (LTI) system identification [1,2], and also most nonlinear system identification methods consider a noise model. However, in most...

### Brain Tissue Differentiation and Characterization using Piezoelectric Actuators driven by multisine excitation

This project is related to the estimation of the mechanical parameters of biological tissues (in particular brain tissue) using a piezoelectric tactile sensor (shown in Figure 1). The main purpose is to provide the tactile sensor a reliable measurement procedure for the differentiation of two...

### 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...

### Using a polynomial decoupling algorithm for state-space identification of a Bouc-Wen system

The polynomial nonlinear state space (PNLSS) approach [1] is a powerful tool for modeling nonlinear systems. A PNLSS model consists of a discrete-time linear state space model, extended with polynomials in the state and the output equation:

x(t+1)=Ax(t)+Bu(t)+Eζ...

### Frequency-domain Identification of Time-Varying Systems for Analysis and Prediction of Aeroelastic Flutter

In this project a different approach to wind tunnel flutter testing is presented. This procedure can now be performed as one continuous test, resulting in a major time saving. Both analysis of the current behaviour of the structure, and prediction towards higher velocities, are important for...

### 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])....

### 2D Regularization for LTV systems using a single measurement: transient elimination and coping with large datasets

This abstract presents a methodology to obtain a nonparametric two-dimensional impulse response function (IRF) estimate of a linear time varying (LTV) system using 2D regularization

### 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...

### Macro models for the Design of Microwave Filters

The design of microwave narrow-band bandpass filters often depends on brute-force electromagnetic (EM) optimization. These optimization procedures are numerically expensive, which makes the design process very time-consuming. Moreover the EM optimizer does not provide any physical insight in the...