9 citations · 13 across the 10 of their papers we have counts for
10 papers
zea: A Toolbox for Cognitive Ultrasound Imaging
Tristan S. W. Stevens, Wessel L. van Nierop, Ben Luijten +7
We present zea (pronounced ze-yah), a Python package for cognitive ultrasound imaging that offers a flexible, modular, and differentiable pipeline for ultrasound data processing. A…
SVD-NO: Learning PDE Solution Operators with SVD Integral Kernels
Noam Koren, Ralf J. J. Mackenbach, Ruud J. G. van Sloun +2
Neural operators have emerged as a promising paradigm for learning solution operators of partial differential equa- tions (PDEs) directly from data. Existing methods, such as those…
Adaptive Bayesian Single-Shot Quantum Sensing
Ivana Nikoloska, Ruud Van Sloun, Osvaldo Simeone
Quantum sensing harnesses the unique properties of quantum systems to enable precision measurements of physical quantities such as time, magnetic and electric fields, acceleration,…
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
Zhixing Wang, Le Zheng, Shi Yan +3
Extended object tracking methods based on random matrices, founded on Bayesian filters, have been able to achieve efficient recursive processes while jointly estimating the kinemat…
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging
Tristan S. W. Stevens, Jeroen Overdevest, Oisín Nolan +3
Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that u…
Active inference and deep generative modeling for cognitive ultrasound
Ruud JG van Sloun
Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultra-portable and cost-effective, akin to the stethoscope. N…