Countermeasures against Fault Injection Attacks in Neural Networks and Processors

Machine learning has gained a lot of recognition recently and is now being used in many important applications. However, this recognition was limited in the hardware security area. Especially, very few approaches depend on this powerful tool to detect attacks during operation. This thesis reduces this gap in the field of fault injection attack...

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network...

First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt

Future Greenland ice sheet (GrIS) melt projections are limited by the lack of explicit melt calculations within most global climate models and the high computational cost of dynamical downscaling with regional climate models (RCMs). Here, we train artificial neural networks (ANNs) to obtain relationships between quantities consistently...

A learning Dempster-Shafer model for automated building detection

This paper presents a learning Dempster-Shafer model for the detection of buildings in aerial image and range data. The process of evidence assignment in the Dempster-Shafer method is implemented through membership functions in an adaptive network-based fuzzy inference system, where a back propagation learning rule is employed to tune the...