Aktuelle Publikationen

This paper addresses the self-interference problem in full duplex operation by leveraging an intelligent reflecting surface (IRS) to provide spatial separation of the transmitted and the received (i.e. reflected) signal in a monostatic sensing...

In recent years, immersive applications such as Cloud XR have emerged, which require very low latency to ensure a high quality of experience. This paper presents a congestion control algorithm combined with L4S to achieve stable and low latency...

We present a guard-ring free InGaAs/InP single photon avalanche diode with 20 µm diameter for the optical C-band. At 225 K, 25.6 µs dead time and 17% detection efficiency, the dark count rate is 3 kcps with 0.5% afterpulsing probability. This...

Due to the lack of a feedback channel in the C-V2X sidelink, finding a suitable MCS level is a difficult task. In this paper, we propose an ML approach that uses quantile prediction to predict the MCS level with the highest achievable data rate....

This paper presents a hybrid photonic integration concept based on the use of a polymer motherboard, InP EML arrays and InP PD arrays to realize WDM and SDM Terabit optical engines operating at 100-Gb/s or even at 20! 0-Gb/s per lane. The optical...

One of cost-effective ways to increase the transmission capacity of current standard wavelength division multiplexing (WDM) transmission systems is to use a wavelength band other than the C-band to transmit in multi-band. We proposed the concept...

While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing is often overestimated and regarded...

Rule-based and modified backpropagation XAI methods struggle with innovative layer building blocks and implementation-invariance issues. In this work we propose canonizations for popular deep neural network architectures and introduce an XAI...

Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input level typically resemble the models' predicted segmentation mask or...

State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To...

Due to their perfect periodic autocorrelation property, Zadoff–Chu sequences are often used as stimulus signals in the measurement of radio channel responses. In this letter, the cross-correlation of a linear shift-invariant system's response to...

In this paper, we show that many of such unsuccessful samples in image-to-image translation lie at the fringe—relatively low-density areas of data distribution, where the DNN was not trained very well. To tackle this problem we propose to perform...

We propose a system to automatically generate enriched digital models from this data, consisting of two AI modules: one for 3D model reconstruction from 2D plans and one for 6D localization of images taken within a building in the corresponding...

Fraunhofer HHI Researchers investigate in this publication a cost-efficient capacity upgrade of optical transmission systems by the reuse of already deployed single mode fiber. This is enabled by the benefits of other transmission bands, to...

Current image quality estimation relies on data-driven approaches, however the scarcity of annotated data poses a bottleneck. This paper introduces a novel pre-training approach utilizing synthetic fractal images. The proposed method is tested on...

A novel 16x4 crossing-free waveguide routing network on four layers of polymer-based stacked waveguides is presented. The design and fabricated device combine in-plane passive waveguide structures with vertical multimode interference couplers to...

We introduce a GMM Layer for gradient-based prototype learning. It is used to cluster feature vectors by computing their probabilities for each gaussian and using the soft cluster assignment for prediction. Hence prototypical image regions can be...

Over-the-Air Multi-Party Communication for scalable, private, secure and dependable data aggregation: This novel approach combines lattice coding, Over-the-Air computation and secure Multi-Party Communication to confidentially aggregate analog...

We introduce the Concept Relevance Propagation (CRP) approach, which combines the local and global perspectives and thus allows answering both the ‘where’ and ‘what’ questions for individual predictions. We demonstrate the capability of our...

Given the number of in situ and remote (e.g. radiosonde/satellite) monitoring devices, there is a common perception that there are no limits to the availability of EO for immediate use in such AI-based models. However, a mere fraction of EO is...

In this paper, we present an analysis and registration pipeline for confined point clouds acquired by stereo endoscopes into a fused representation. For a coarse registration, TEASER is applied, while a refinement is conducted utilizing...

In this work, we propose FEDAUX, an extension to Federated Distillation, which, under the same set of assumptions, drastically improves the performance by deriving maximum utility from the unlabeled auxiliary data. Our proposed method achieves...

In this work, we present an artificial intelligence (AI)-based semantic segmentation approach for three-dimensional (3D) point clouds which were generated from 2D images with a structure from motion (SfM) pipeline. We utilize state-of-the-art...

Hyperspectral imaging is an emerging technology that has gained significant attention in the medical field due to its ability to provide precise and accurate imaging of biological tissues. The current methods of hyperspectral imaging, such as...

Surgical phase recognition is an important aspect of surgical workflow analysis, as it allows an automatic analysis of the performance and efficiency of surgical procedures. A big challenge for training a neural network for surgical phase...

We present a new approach for video-driven animation of high-quality neural 3D head models, addressing the challenge of person-independent animation from video input.In order to achieve person-independent animation from video input, we introduce...

This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Training a VAE for geometry and texture yields a parametric model for...

Spectral imaging allows to analyze optical tissue properties that are invisible to the naked eye. We present a novel approach using two multispectral snapshot cameras covering different spectral ranges as a stereo-system. The proposed method...

A lab-on-a-chip multichannel sensing platform for biomedical analysis based on optical silicon nitride (SiNx) microring-resonators (MRR) was established. The resonators were surface functionalized and finally combined with a microfluidic chamber...

This paper validates an in-house three-dimensional ray-launching (3D-RL) algorithm with a channel sounder measurement campaign that has been performed in a typical indoor environment at 300 GHz.