Aktuelle Publikationen

With the goal of enabling optical wireless communications for mobile devices, we assess a physical layer based on high-bandwidth on-off keying modulation. This allows for amplifier designs that avoid operation in a resistive mode, reducing their energy usage to a fraction. Link-level simulations show that the investigated physical layer can deal with typical frontend limitations and can operate in challenging non-line-of-sight channels.

We present a hybrid animation approach that combines example-based and neural animation methods to create a simple, yet powerful animation regime for human faces. We introduce a light-weight auto-regressive network to transform our animation-database into a parametric model. During training, our network learns the dynamics of facial expressions, which enables the replay of annotated sequences from our animation database as well as their seamless concatenation in new order.

This paper provides a timely overview of the field of explainable artificial intelligence (XAI). It explains the theoretical foundations of interpretability algorithms, outlines best practice aspects, demonstrates successful usage of XAI in selected application scenarios, and discusses challenges and possible future directions of this active emerging field.

This paper proposes a novel approach for coherent detection with double-side vertically illuminated photodetectors. Signal and local oscillator are injected collinearly from opposite sides of the photodetector chip. The concept inherently provides angular selective detection and can be used for developing compact, solid-state receiver modules for coherent light detection and ranging (LiDAR).

This paper gives a comprehensive overview over classic shallow and novel deep approaches to anomaly detection. We identify the common underlying principles and provide an empirical assessment of major existing methods that are enriched by the use of recent explainability techniques. We present specific worked-through examples together with practical advice and discuss open challenges.

This paper proposes a novel criterion for CNN pruning inspired by neural network interpretability: The most relevant units, i.e. weights or filters, are automatically found using their relevance scores obtained from concepts of explainable AI (XAI). By exploring this idea, we connect the lines of interpretability and model compression research.

Time-domain spectroscopy with terahertz frequencies typically requires complex and bulky systems. Here, the authors present an optoelectronics-based, frequency-domain terahertz sensing technique which offers competitive measurement performance in a much simpler system.

Externally modulated DFB lasers (EML) and vertically illuminated photodetectors are presented. Because of their excellent high-speed behavior and operation wavelength of 1310 nm, the devices are of interest for intra-data center communication. Since the EML and the photodetector chips are compatible with current systems, these devices are candidates for upgrading existing transceivers to higher baud rates. Therefore, a proof of concept for 100 GBaud data transmission with the presented components is demonstrated. Even without predistor! tion, the experiments show clearly open eye diagrams.

This paper proposes a novel, simple yet effective defense strategy for adversarial attacks on deep learning models. Our algorithm, called MALA for DEfense (MALADE), is applicable to any existing classifier, providing robust defense as well as off-manifold sample detection. In our experiments, MALADE exhibited state-of-the-art performance against various elaborate attacking strategies.

Inverse design is rapidly gaining popularity for automated design of photonic components. Two methods to improve it for large passive waveguide structures are developed: Adaptive Threshold Binarization and Hybrid Optimization. To demonstrate their capability, inverse design is applied to an InP waveguide platform for the first time. As an example, a polarizer with a PER of -19.4 dB is presented.