Aktuelle Publikationen

Juni 2024

Explainable AI for Time Series via Virtual Inspection Layers

Johanna Vielhaben, Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin

For time series data, where the input itself is often not interpretable, dedicated XAI research is scarce. In this work, we put forward a virtual inspection layer for transforming the time series to an interpretable representation and allows to...

Mai 2024

Dynamic Exposure Visualization of Air Quality Data with Augmented Reality

Sylvain Renault, Oliver Schreer, Ingo Feldmann, Lieven Raes, Jurgen Silence

This paper introduces a new concept and outlines the implementation of an AR application designed for mobile devices. It can visualize real-time environmental data from various Open Data platforms. The scientific contribution lies in diverse...

Mai 2024

Time-bin entanglement at telecom wavelengths from a hybrid photonic integrated circuit

Hannah Thiel, Moritz Kleinert, Norbert Keil, Hauke Conradi, Lennart Jehle, Robert Chapman, Stefan Frick, Gregor Weihs, Holger Suchomel, Martin Kamp, Sven Hofling, Christian Schneider

We present a fiber-pigtailed hybrid photonic circuit comprising nonlinear waveguides for photon-pair generation and a polymer interposer reaching 68 dB of pump suppression and photon separation based on a polarizing beam splitter with > 25 dB...

April 2024

Channel Charting for Beam Management in Sub-THz Systems

Patrick Agostini, Slawomir Stanczak, Zoran Utkovski

Sub-THz communication, vital for 6G, requires densification and multi-connectivity to overcome signal blockage. Efficient beam and user scheduling are crucial. We propose a low-complexity scheduling scheme using machine learning (ML), employing a...

April 2024

BiSPARCs for Unsourced Random Access in Massive MIMO

Patrick Agostini, Slawomir Stanczak, Zoran Utkovski

This paper addresses the massive MIMO unsourced random access problem in quasi-static Rayleigh fading. Our proposed coding scheme combines an outer LDPC code with an inner SPARC code, integrating channel estimation, single-user decoding, and...

April 2024

Animatable Virtual Humans: Learning pose-dependent human representations in UV space for interactive performance synthesis

Wieland Morgenstern, Peter Eisert, Anna Hilsmann, Milena Bagdasarian

We propose a novel representation of virtual humans for highly realistic real-time animation and rendering in 3D applications. We learn pose dependent appearance and geometry from highly accurate dynamic mesh sequences obtained from...

April 2024

Realness of face images can be decoded from non-linear modulation of EEG responses

Yonghao Chen, Peter Eisert, Anna Hilsmann, Sebastian Bosse, Arno Villringer, Vadim V. Nikulin, Milena Bagdasarian, Michael Gaebler, Tilman Stephani

We utilized an EEG dataset of steady-state visual evoked potentials in which participants were presented with human face images of different stylization levels. Assessing neuronal responses, we found a non-linear relationship between SSVEP...

April 2024

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

Clare A Primiero, Frederik Pahde, Brigid Betz-Stablein, Nathan Ascott, Brian D'Alessandro, Seraphin Gaborit, Paul Fricker, Abigail Goldsteen, Sandra Gonzalez-Villa, Katie Lee, Sana Nazari, Hang Nguyen, Valsamis Ntsoukos, Balazs E. Pataki, Josep Quintana, Susana Puig, Gisele G. Rezze, Rafael Garcia, H. Peter Soyer, Josep Malvehy

AI has proven effective in classifying skin cancers using dermoscopy images. However, clinical application is limited when algorithms are not well-trained, or there is a lack of clinical context (e.g., medical history). The increasing use of...

April 2024

UL-DL Duality for Cell-Free Massive MIMO With Per-AP Power and Information Constraints

Lorenzo Miretti, Slawomir Stanczak, Renato L. G. Cavalcante, Emil Björnson

This article advances the theoretical foundations of user-centric cell-free massive MIMO networks. In particular, by means of a novel UL-DL duality principle for fading channels, it settles the optimality of the recently developed “team MMSE”...

April 2024

Towards an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets

Rodrigo Hernangómez, Slawomir Stanczak, Martin Kasparick, Gerhard Fettweis, Daniel Schäufele, Rafail Ismayilov, Oscar Dario Ramos-Cantor, Hugues Tchouankem, Philipp Geuer, Alexandros Palaois, Cara Watermann, Mohammad Parvini, Anton Krause, Thomas Neugebauer, Jose Leon Calvo, Bo Chen

We present iV2V and iV2i+, two machine-learning datasets for industrial wireless communication. The datasets cover sidelink and cellular communication involving autonomous robots together with localization and sensing data, which can be used to...

April 2024

Signal separation in radio spectrum using self-attention mechanism

Fadli Damara, Slawomir Stanczak, Zoran Utkovski

Traditional signal processing methods often fall short compared to data-driven approaches in a signal separation problem that involves co-channel signals, where the energy content of the interference component overlaps with the transmitted signal...

März 2024

Comparison of Sub-THz Radio Channel Characteristics at 158 GHz and 300 GHz in a Shopping Mall Scenario

Alper Schultze, Wilhelm Keusgen, Michael Peter, Ramez Askar, Taro Eichler, Mathis Schmieder

This paper compares the sub-THz radio channel characteristics at 158 GHz and 300 GHz in a shopping mall scenario by extracting three different path loss models and various channel parameters.

März 2024

From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space

Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin, Christopher J. Anders, Frederik Pahde

We present a novel method ensuring the right reasons on the concept level by reducing the model's sensitivity towards biases through the gradient. When modeling biases via Concept Activation Vectors, we highlight the importance of choosing robust...

März 2024

Sparse Aperiodic Optical Phased Arrays on Polymer Integration Platform

Adam Raptakis, Christos Kouloumentas, Norbert Keil, Moritz Kleinert, Hercules Avramopoulos, Panos Groumas, Lefteris Gounaridis, Christos Tsokos, Madeleine Weigel, Efstathios Andrianopoulos, Georgios Lymperakis

Solid-state optical beam-steering utilizing polymer waveguides as edge emitters to form optical phased arrays (OPAs) with aperiodic spacing for operation at 1550 nm is demonstrated for the first time. Power consumption of 1.28 mW/? per channel is...

Februar 2024

Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction

Eric Wisotzky, Peter Eisert, Anna Hilsmann, Jost Triller

We present a novel approach combining two calibrated multispectral real-time capable snapshot cameras, covering different spectral ranges, into a stereo-system. Therefore, a hyperspectral data-cube can be continuously captured. The combined use...

Februar 2024

Towards Better Morphed Face Images Without Ghosting Artifacts

Clemens Peter Seibold, Anna Hilsmann

We propose a method for automatic prevention of ghosting artifacts based on a pixel-wise alignment during morph generation. We evaluate our proposed method on state-of-the-art detectors and show that our morphs are harder to detect, particularly,...

Februar 2024

Generative Texture Super-Resolution via Differential Rendering

Milena Teresa Bagdasarian, Peter Eisert, Anna Hilsmann

We propose a generative deep learning network for texture map super-resolution using a differentiable renderer and calibrated reference images. Combining a super-resolution generative adversarial network (GAN) with differentiable rendering, we...

Februar 2024

Multi-View Inversion for 3D-aware Generative Adversarial Networks

Florian Tim Barthel, Peter Eisert, Anna Hilsmann

Our method builds on existing state-of-the-art 3D GAN inversion techniques to allow for consistent and simultaneous inversion of multiple views of the same subject. We employ a multi-latent extension to handle inconsistencies present in dynamic...

Februar 2024

Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures

Eric Wisotzky, Peter Eisert, Anna Hilsmann, Thomas Wittenberg, Lara Wallburg, Stefan Göb

This study investigates the effectiveness of neural network architectures in hyperspectral image demosaicing. We introduce a range of network models and modifications, and compare them with classical interpolation methods and existing reference...

Februar 2024

Animating NeRFs from Texture Space: A Framework for Pose-Dependent Rendering of Human Performances

Paul Knoll, Peter Eisert, Anna Hilsmann, Wieland Morgenstern

We introduce a novel NeRF-based framework for pose-dependent rendering of human performances where the radiance field is warped around an SMPL body mesh, thereby creating a new surface-aligned representation. Our representation can be animated...

Ergebnisse pro Seite10ǀ20ǀ30
Ergebnisse 1-20 von 280
<< < 1 2 3 4 5 6 7 8 > >>