Fusion of radar data from multiple MIMO systems to achieve higher angular resolution

In today's automotive applications, radar is widely used to estimate the target position and velocity with respect to the radar position. Estimating the position of the target in terms of range and velocity is fairly advanced and accurate. However, resolving two closely spaced targets in the azimuth domain which have the same range and relative...

Evaluating the Robustness of the Recurrent Inference Machine Based on MRI Lesion Data from Multiple Sclerosis Patients

The Recurrent Inference Machine (RIM) has been developed as an alternative to the clinically used Compressed Sensing (CS) algorithm, using Deep Learning (DL). A common issue with DL networks is the generalization of the network to features that have not been trained for. In this study we evaluate the robustness of the RIM to white matter lesions...

Accelerating (Compressed) SENSE Scans in MRI

Magnetic Resonance Imaging is a painless procedure to produce high-resolution diagnostic images. Today, it is one of the essential clinical imaging modalities. One of the major challenges involved with this imaging modality is its long scanning time. Parallel imaging in combination with compressed sensing has overcome this challenge to a great...

Active Semi-Supervised Learning For Diffusions on Graphs

In statistical learning over large data-sets, labeling all points is expensive and time-consuming. Semi-supervised classification allows learning with very few labels. Naturally, selecting a few points to label becomes crucial as the performance relies heavily on the labeled points. The motivation behind active learning is to build an optimal...

Accelerating Diffusion-Weighted Chemical Shift Imaging using Compressed Sensing with Parameter Mapping

Diffusion-weighted chemical shift imaging (DW-CSI) is a recently developed MRI modality that enables radiologists to reveal the diffusion properties of small molecules that act in metabolic reactions in-vivo. In order to extract this diffusion information from a patient, DW-CSI requires approximately one hour of scan time. This extensive scan...

Dantzig Selector: Het schatten van een sparse parametervector met weinig metingen - Dantzig Selector: Statistical estimation of a sparse vector with undersampled data

Bij lineaire regressie geldt in het algemeen, dat het aantal metingen (n) groter is dan het aantal parameters (p). In praktische situaties kan het echter voorkomen, dat het aantal metingen veel kleiner is dan het aantal parameters. De kleinste kwadratenschatter kan in dat geval niet gebruikt worden om de parametervector te schatten, er is geen...