Aktuelle Publikationen

November 2020

Experimental Demonstrations of High-Capacity THz-Wireless Transmission Systems for Beyond 5G

Carlos Moises Castro Posada, Robert Elschner, Thomas Merkle, Colja Schubert, Ronald Freund

Using the concept of a “THz-Wireless Fiber Extender” it is possible to combine the flexibility of wireless networks with the high capacity of fiber-optical networks. In this article, we report on a real-time short-range demonstration of a 100...


Oktober 2020

Hybrid data and model driven algorithms for angular power spectrum estimation

Renato L. G. Cavalcante, Slawomir Stanczak

We propose two algorithms that use both models and datasets to estimate angular power spectra from channel covariance matrices in massive MIMO systems. The first algorithm is an iterative fixed-point method that solves a hierarchical problem. It...


Oktober 2020

Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements

Felix Sattler, Jackie Ma, Patrick Wagner, David Neumann, Markus Wenzel, Ralf Schäfer, Wojciech Samek, Klaus-Robert Müller, Thomas Wiegand

Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV- 2 pandemic. In this work we propose an approach to reliably...


September 2020

637 μW emitted terahertz power from photoconductive antennas

Robert Kohlhaas, Steffen Breuer, Lars Liebermeister, Simon Nellen, Milan Deumer, Mykhaylo P. Semtsiv, William Ted Masselink, Björn Globisch

We present photoconductive terahertz (THz) emitters based on rhodium (Rh) doped InGaAs for time-domain spectroscopy (TDS). The emitters feature a record high THz power of 637 µW. In combination with InGaAs:Rh receivers, a 6.5 THz bandwidth and a...


September 2020

Robust and Communication-Efficient Federated Learning from Non-IID Data

Felix Sattler, Klaus-Robert Müller, Wojciech Samek, Simon Wiedemann

Federated Learning comes at the cost of a significant communication overhead during training. In this work, we propose Sparse Ternary Compression (STC), a new compression framework that is specifically designed to meet the requirements of the...


September 2020

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

Nils Strodthoff, Wojciech Samek, Tobias Schaeffter, Patrick Wagner

This paper puts forward first benchmarking results for the PTB-XL dataset, covering a variety of tasks from different ECG statement prediction tasks over age and gender prediction to signal quality assessment. We find that convolutional neural...


August 2020

Effect of Optical Feedback on the Wavelength Tuning in DBR Lasers

Magnus Happach, David de Felipe Mesquida, Victor Nicolai Friedhoff, Gelani Irmscher, Martin Kresse, Moritz Kleinert, Crispin Zawadzki, Walter Brinker, Martin Möhrle, Norbert Keil, Werner Hofmann, Martin Schell

Optical feedback has an impact on the tunability of lasers. We created a model of a tunable distributed Bragg reflector (DBR) laser describing the effect of optical feedback from a constant reflector distance on the wavelength tuning. Theoretical...


August 2020

Accurate and Robust Neural Networks for Face Morphing Attack Detection

Clemens Peter Seibold, Peter Eisert, Anna Hilsmann, Wojciech Samek

A morphed face image is a fusion of two face images and represents biometrics of two different subjects. Embedded in an official document, it can cause immense damage, since both subjects can claim its ownership and thus share an identity. In...


August 2020

Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints

Felix Sattler, Klaus-Robert Müller, Wojciech Samek

Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. However, FL yields suboptimal results if the local clients’ data distributions...


Juli 2020

Monolithically Integrated InP-Based Polarization Rotator-Splitter with Simplified Fabrication Process

Hendrik Boerma, Patrick Runge, Martin Schell, Felix Ganzer, Shahram Keyvaninia

Polarization division multiplexing doubles the transmission capacity of optical communication systems. For such systems, splitters separating the TE from the TM mode are indispensable components. We design and manufacture an integrated InP-based...


Mai 2020

PTB-XL, A Large Publicly Available Electrocardiography Dataset

Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I. Lunze, Wojciech Samek, Tobias Schaeffter

Electrocardiography (ECG) is increasingly supported by algorithms based on machine learning. We put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10...


April 2020

Artificial Intelligence in Dentistry: Chances and Challenges

Falk Schwendicke, Wojciech Samek, Joachim Krois

AI solutions have not by large entered routine dental practice, mainly due to (1) limited data availability, accessibility, structure and comprehensiveness, (2) lacking methodological rigor and standards in their development, (3) and practical...


April 2020

Going beyond Free Viewpoint: Creating Animatable Volumetric Video of Human Performances

Anna Hilsmann, Oliver Schreer, Peter Eisert, Ingo Feldmann, Philipp Fechteler, Wolfgang Paier, Wieland Morgenstern

We present an end-to-end pipeline for the creation of high-quality animatable volumetric video content of human performances. Going beyond the application of free-viewpoint volumetric video, we allow re-animation of an actor’s performance...


April 2020

Resolving Challanges in Deep Learning-Based Analyses of Histopathological Images using Explanation Methods

Miriam Hägele, Klaus-Robert Müller, Wojciech Samek, Alexander Binder, Frederick Klauschen, Sebastian Lapuschkin, Philipp Seegerer, Michael Bockmayr

This work shows the application of explainable AI (XIA) methods to resolve common challenges encountered in deep learning-based digital histopathology analyses. We investigate three types of biases and show that XAI techniques are helpful and...


März 2020

High performance BH InAs/InP QD and InGaAsP/InP QW mode-locked lasers as comb and pulse sources

Marlene Zander, Martin Schell, Martin Moehrle, Wolfgang Rehbein, Jan C. Balzer, Steffen Breuer, Dieter Franke, Kevin Kolpatzeck

Coherent comb lasers may serve as a source for multiwavelength modulators in short reach transmission, or for phase controlled OFDM channels in long reach. We explore and compare quantum dot (QD) and quantum well (QW) lasers with more than 33...


März 2020

Trends and Advancements in Deep Neural Network Communication

Felix Sattler, Thomas Wiegand, Wojciech Samek

Deep models are also being increasingly applied in distributed settings, where the data are separated by limited communication channels and privacy constraints. To address the challenges, a wide range of training and evaluation schemes have been...


Februar 2020

Determination of the optical properties of cholesteatoma in the spectral range of 250 to 800 nm

Eric Wisotzky, Peter Eisert, Anna Hilsmann, Florian Uecker, Philipp Arens, Steffen Dommerich

We determine the absorption and scattering coefficients of cholesteatoma and bone. In the near-UV and visual spectrum, clear differences exist between both tissues. These differences reveal the future possibility to detect and identify,...


Februar 2020

Hybrid Human Modeling: Making Volumetric Video Animatable

Peter Eisert, Anna Hilsmann

Photo-realistic modeling and rendering of humans is extremely important for VR environments. While purely computer graphics modeling can achieve highly realistic human models, achieving real photo-realism with these models is computationally...


Januar 2020

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks

Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Tung Nguyen, Ahmed Osman, Heiko Schwarz, Detlev Marpe, Thomas Wiegand, Wojciech Samek

This paper presents DeepCABAC, a universal compression algorithm for deep neural networks (DNNs) that through its adaptive, context-based rate modeling, allows an optimal quantization and coding of parameters. It compresses DNNs up to 5% of...


Januar 2020

UDSMProt: Universal Deep Sequence Models for Protein Classification

Nils Strodthoff, Wojciech Samek, Patrick Wagner, Markus Wenzel

Inferring the properties of protein from its amino acid sequence is a key problem in bioinformatics. We put forward UDSMProt, a universal deep sequence model that is pretrained on a language modeling task and finetuned on protein classification...



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