Hyperspectral image analysis has been gaining research attention thanks to the current
advances in sensor design which have made acquiring such imagery much more affordable …

Safety-critical applications are frequently based on deep learning algorithms. In particular,
Convolutional Neural Networks (CNNs) are commonly deployed in autonomous driving …

This paper introduces a universal federated learning framework that enables over-the-air
computation via digital communications, using a new joint source-channel coding scheme …

This paper introduces a federated learning framework that enables over-the-air computation
via digital communications, using a new joint source-channel coding scheme. Without …

Pathology slides of lung malignancies are classified using resource-frugal convolution
neural networks (CNNs) that may be deployed on mobile devices. In particular, the …

This paper introduces a federated learning framework that enables over-the-air computation
via digital communications, using a new joint source-channel coding scheme. Without …

Deep neural networks have shown incredible performance for inference tasks in a variety of
domains, but require significant storage space, which limits scaling and use for on-device …

Classical quantizer design approaches using the Lloyd-Max algorithm (or-means) have
served signal processing applications for more than three decades. With the advent of …

Image segmentation is a pivotal task in hyperspectral image processing. Usually, it is
performed in a setting where neither the environment nor the hardware pose an obstacle. If …

Kurzfassung Im Folgenden werden die Motivation, das Ziel und die Aufgabe der Dissertation
beschrieben. Der herausragende Sieg von AlexNet bei der ImageNet Large Scale …