RunDAE model: Running denoising autoencoder models for denoising ECG signals.

Background: The denoising autoencoder (DAE) is commonly used to denoise bio-signals such as electrocardiogram (ECG) signals through dimensional reduction. Typically, the DAE model needs to be trained using correlated input segments such as QRS-aligned segments or long ECG segments. However, using long ECG segments as an input can result in a complex deep DAE model that requires many hidden layers to achieve a low-dimensional representation, which is a major drawback.

DualSort: online spike sorting with a running neural network.

Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain.

Multiple ECG segments denoising autoencoder model.

Objectives: Denoising autoencoder (DAE) with a single hidden layer of neurons can recode a signal, i.e., converting the original signal into a noise-reduced signal.

[The G-BA innovation fund's project "Avenue-Pal" - analysis and improvement of cross-sectoral and cross-departmental interface and relocation management in palliative care].

Background: About 80% of all people in Germany die in inpatient care. Around every fifth person in inpatient care is relocated to another care area in the last phase of their life. That is more than 150,000 people being relocated, often without indication.

High speed external cavity diode laser concept based on a resonantly driven MEMS scanner for the mid-infrared region.

The rapid detection of trace gases is of great relevance for various spectroscopy applications. In this regard, the technology of external cavity diode lasers (ECDLs) has firmly established itself due to its excellent properties. Outside of the laboratory environment, however, these still have some restrictions, especially with regard to high acquisition rates for sensitive spectroscopy applications and mode-hop-free tuning.