Publications

Time-multiplexed reservoir computing is a machine learning concept which can be realised in photonic hardware systems using only one physical node. The concept can be used for various problems, ranging from classification problems to time-series prediction tasks, while being fast and energy efficient. Here, a theoretical analysis of a reservoir computer realised via delay-coupled semiconductor lasers is presented and the role of the internal system time-scales and the bifurcation structure is discussed. It is further shown that optimal performance can be reached by tailoring the coupling delays to the specific memory requirements of the given task.

Physiological direct current (DC) potential shifts in electroencephalography (EEG) can be masked by artifacts such as slow electrode drifts. To reduce the influence of these artifacts, linear detrending has been proposed as a pre-processing step. We considered quadratic detrending, which has hardly been addressed for ultralow frequency components in EEG. We compared the performance of linear and quadratic detrending in simultaneously acquired DC-EEG and transcutaneous partial pressure of carbon dioxide during two activation methods: hyperventilation (HV) and apnea (AP). Quadratic detrending performed significantly better than linear detrending in HV, while for AP, our analysis was inconclusive with no statistical significance. We conclude that quadratic detrending should be considered for DC-EEG preprocessing.

We study output reference tracking for unknown continuous-time systems with arbitrary relative degree. The control objective is to keep the tracking error within predefined time-varying bounds while measurement data is only available at discrete sampling times. To achieve the control objective, we propose a two-component controller. One part is a recently developed sampled-data zero-order hold controller, which achieves reference tracking within prescribed error bounds. To further improve the control signal, we explore the system dynamics via input-output data, and include as the second component a data-driven MPC scheme based on Willems et al.’s fundamental lemma. This combination yields significantly improved input signals as illustrated by a numerical example.

Nanoporous gold is a three-dimensional bulk material that is percolated with a random network of nanometer-sized ligaments and made by selective corrosion of bimetallic alloys. It has intriguing geometric, catalytic, and optical properties that have fascinated scientists for many decades. When such a material is made into the form of small, 100-nm-sized particles, so-called nanosponges emerge that offer much flexibility in controlling their geometric, electronic, and optical properties. Importantly, these particles act as an antenna for light that can efficiently localize optical fields on a deep subwavelength scale in certain hotspots at the particle surface. This makes such nanosponges an interesting platform for plasmonic sensing, photocatalysis, and surface-enhanced Raman spectroscopy. Since the optical properties of these nanosponges can be controlled to a large degree by tuning their geometry and/or composition, they have attracted increasing attention in recent years. Here, we provide a concise overview of the current state of the art in this field, covering their fabrication, computational modeling, and specifically the linear and nonlinear optical properties of individual and hybrid nanosponges, for example, plasmon localization in randomly disordered hotspots with a size <10 nm and a long lifetime with an exceptionally high Purcell factor. The resulting nonlinear optical and photoemission properties are discussed for individual and hybrid nanosponges. The results presented have strong implications for further applications of such nanosponges in photonics and photocatalysis.