Label synchronization for Hybrid Federated Learning in manufacturing and predictive maintenance

Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information....

Efficient Bayesian Uncertainty Estimation for nnU-Net

The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its extraordinary performance, nnU-Net does not supply a measure of uncertainty to indicate its possible...

Design of an Aberration Correction System for a Deployable Space Telescope

Launch costs for high-resolution space telescopes for Earth observation can be reduced when the telescope mirrors are made deployable. However, such a system is subject to optical aberrations that decreases image quality. To counter these aberrations, an Aberration Correction System (ACS) is proposed that uses a deformable mirror (DM) which is...

GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications

Currently, non-rigid image registration algorithms are too computationally intensive to use in time-critical applications. Existing implementations that focus on speed typically address this by either parallelization on GPU-hardware, or by introducing methodically novel techniques into CPU-oriented algorithms. Stochastic gradient descent (SGD...

Stochastic optimization with randomized smoothing for image registration

Image registration is typically formulated as an optimization process, which aims to find the optimal transformation parameters of a given transformation model by minimizing a cost function. Local minima may exist in the optimization landscape, which could hamper the optimization process. To eliminate local minima, smoothing the cost function...

An Online Learning Approach to Eliminate Bus Bunching in Real-time

Recent advances in telecommunications created new opportunities for monitoring public transport operations in real-time. This paper presents an automatic control framework to mitigate the Bus Bunching phenomenon in real-time. The framework depicts a powerful combination of distinct Machine Learning principles and methods to extract valuable...