## 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 are at stake.

### Realization and identification of autonomous Wiener systems via low-rank approximation

Wiener systems are nonlinear dynamical systems, consisting of a linear dynamical system and a static nonlinear system in a series connection. Existing results for analysis and identification of Wiener systems assume zero initial conditions. In this paper, we consider the response of a Wiener...

### Compressed ultrasound signal reconstruction using a low-rank and joint-sparse representation model

With the introduction of very dense sensor arrays in ultrasound imaging, data transfer rate and data storage can become a bottle neck in ultrasound system design. To reduce the amount of sampled channel data, we proposed a new approach based on the low-rank and joint-sparse model that allows to...

### Learning Kalman filtering with Lego mindstorms

Research shows that, in learning science and engineering, guided project work leads to deeper understanding of theoretical concepts (as well as acquisition of hands-on skills) than the classical approach of textbook reading and attending lectures. In an approach to education based on project...

### Sum-of-exponentials modeling via Hankel low-rank approximation with palindromic kernel structure

Estimation of a sum-of-damped-exponentials signal from noisy samples of the signal is a classic signal processing problem. It can be solved by maximum likelihood as well as suboptimal subspace methods. In this paper, we consider the related problem of sum-of-exponentials modeling, in which the...

### Using structured low-rank approximation for sparse signal recovery

Structured low-rank approximation is used in model reduction, system identification, and signal processing to find low-complexity models from data. The rank constraint imposes the condition that the approximation has bounded complexity and the optimization criterion aims to find the best match...

### Applications of polynomial common factor computation in signal processing and systems theory

We consider the problem of computing the greatest common divisor of a set of univariate polynomials and present applications of this problem in system theory and signal processing. One application is blind system identification: given the responses of a system to unknown inputs, find the system...

### Uncertainty analysis of a subspace-based input estimation method for dynamic measurements

A measurement is an experimental procedure to determine the value of a physical magnitude. The true value of the to-be-measured quantity is unknown and the measurement result is an estimation of the true value. The difference between the true value and its estimate cannot be absolutely...

### Identifying Reflections in High Frequency Structures

The goal of this work is to improve the modeling of Lumped Distributed Structures (LDSs) by obtaining a sensible aaccuracy with a reasonably low number of model parameters while identifying the reflections present.

### Developing wave-based calibration for vector network analyzers

### Current state-of-the-art in RF calibration

Calibration is an essential part of any RF, microwave and millimeter wave measurement process. The accuracy of a bare-bones high-frequency measurement instrument is jeopardized by the influences of non-idealities, cabling and instrument drift....

### Data-driven signal processing using the nuclear norm heuristic

Applications in signal processing and control theory are typically model-based and proceed in two steps. In the 'modeling' step, a mathematical model is built from the measured noisy data. In the 'design' step, the model is used to solve a specific application problem. In this project, we will...

### FRF measurements subject to missing data: quantification of noise, nonlinear distortion, and time-varying effects

Quantifying the level of nonlinear distortions and time-varying effects in frequency response function (FRF) measurements is a first step towards the selection of an appropriate parametric model structure. In this project we tackle this problem in the presence of missing data, which is an...

### Nonparametric identification of linear dynamic errors-in-variables systems

The present work handles the nonparametric identification of linear dynamic systems within an errors-in-variables framework, where the input is arbitrary, and both the input and output disturbing noises are white with unknown variances. Using the property that the frequency response function and...

### Carrier aggregation intermodulation distortions in Advanced Systems

Achieving greater throughput requires increasing bandwidth. However, due to frequency’s scarcity, the necessary bandwidth may not be available in one contiguous band. LTE Advanced use multiple bands for one user: that is carrier aggregation [1]. However power amplifier’s efficiency is only...

### Local bending stiffness identification of beams using simultaneous Fourier-series fitting and shearography

In this project, we present a novel method for the identification of the local bending stiffness of beams. We use shearography to capture measurements of vibrating beams, so the input data for the identification is the modal slope – the differential of the modal shape. The modal slope is fitted...

### Identification of time-varying biomechanical systems

This project aims at extracting models of human joints in a data driven manner. This is done in collaboration with the biomechanics group at the TU Delft, where experimental setups have been instrumented to apply force and position disturbances to joints (ankle, wrist and shoulder). Based on...