Aktuelle Publikationen

August 2021

Zero on Shape: A Generic 2D-3D Instance Similarity Metric learned from Synthetic Data

Maciej Janik, Peter Eisert, Anna Hilsmann, Niklas Gard

We present a network architecture which compares RGB images and untextured 3D models by the similarity of the represented shape. Our system is optimised for Zero-Shot retrieval, meaning it can recognise shapes never shown in training.


August 2021

Enabling S-C-L-Band Systems with Standard C-Band Modulator and Coherent Receiver using Nonlinear Predistortion

Robert Emmerich, Colja Schubert, Carsten Schmidt-Langhorst, Ronald Freund, Robert Elschner, Isaac Sackey, Matheus Ribeiro Sena

To counteract the forthcoming capacity crunch in optical networks by increasing the throughput over already existing fiber infrastructures, innovative approaches are required. Multiband systems are an interesting approach to address this issue....


August 2021

S-Band Transmission with Off-the-Shelf C-Band Components Enabled by Nonlinear Predistortion based on Coherent System Identification

Robert Emmerich, Colja Schubert, Carsten Schmidt-Langhorst, Ronald Freund, Robert Elschner, Isaac Sackey, Mustafa Sezer Erkilinc, Matheus Ribeiro Sena

Nonlinear Predistortion based on Coherent System Identification Text: In order to cope with the rapid traffic growth and as well reducing the cost-per-bit, reuse of the legacy optical fiber infrastructure is one of the main objectives for...


August 2021

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

Felix Sattler, Klaus-Robert Müller, Wojciech Samek

Federated learning (FL) is the most widely adopted framework for collaborative training of deep learning models under privacy constraints. Albeit its popularity, it has been observed that FL yields suboptimal results if the clients’ data...


August 2021

FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons

Simon Wiedemann, Thomas Wiegand, Wojciech Samek, Friedel Gerfers, Daniel Becking, Suhas Shivapakashy, Pablo Wiedemann

With the growing demand for deploying Deep Learning models to the “edge”, it is paramount to develop techniques that allow to execute models within very tight and limited resource constraints. In this work we propose a software-hardware...


August 2021

Explain and Improve: LRP-Inference Fine Tuning for Image Captioning Models

Jiamei Sun, Wojciech Samek, Alexander Binder, Sebastian Lapuschkin

This paper analyzes the predictions of image captioning models with attention mechanisms beyond visualizing the attention itself. We compare the interpretability of attention heatmaps systematically against the explanations. We demonstrate that...


August 2021

LiFi Positioning for Industry 4.0

Sepideh Mohammadi Kouhini, Volker Jungnickel, Ronald Freund, Christoph Kottke, Ziyan Ma, Marcel Müller, Daniel Behnke, Marcos Martinez Vazquez, Jean-Paul Linnartz

Precise  position  information  is  considered  as  the main  enabler  for  the  implementation  of  smart  manufacturing systems  in  Industry  4.0.  In ...


August 2021

Benefits of MIMO Mode Switching, Angular Diversity and Multiuser Multiplexing for LiFi

Sepideh Mohammadi Kouhini, Volker Jungnickel, Ronald Freund, Dominic Schulz, Peter Hellwig

We report on the first real-time experiments with distributed MIMO and multiple users for LiFi. MIMO mode switching and angular diversity are beneficial for robust-ness. Multiuser multiplexing helps in scenarios where users have complementary...


August 2021

Distributed MIMO Experiment Using LiFi Over Plastic Optical Fiber

Sepideh Mohammadi Kouhini, Volker Jungnickel, Ronald Freund, Sreelal Maravanchery Mana, Sepideh Mohammadi Kouhini, Jean-Paul Linnartz , Carina Ribeiro Barbio Corrêa, Eduward Tangdiongga, Thiago Cunha, Xiong Den

This  paper  shows  the  feasibility  of  a  networked LiFi  system  using  a  distributed  multiple-input  multiple-output(MIMO)  link  for  optical ...


August 2021

Deep Convolutional Neural Network for Network-wide QoT Estimation

Pooyan Safari, Johannes Fischer, Mohammad Behnam Shariati, Geronimo Bergk

We propose a novel Deep Convolutional Neural Network formulation for network-wide QoT classification tasks and show its effectiveness for networks with significant topological differences. Our formulation achieves ~99% accuracy on large and...



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