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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Philipp, Patrick Veloso, Julia Appenzeller, Arno Hartz, Tobias Beyerer, Juergen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Iosb Fraunhofer Center for Machine Learning Karlsruhe Germany Klinisches Krebsregister Niedersachen Hannover Germany Karlsruhe Institute of Technology Kit Vision and Fusion Laboratory Ies Karlsruhe Germany

ISBN: (纸本)9798350320282

The amount of data in the medical field is constantly increasing. But it is not only the sheer amount of information that is important, but also its quality and type of representation. While nomenclatures such as SNOMED CT (systematized Nomenclature of Medicine and Clinical Term) are suited for finegrained documentation and modern analysis, much information is also bound in classifications. This fact is often historical, as billing systems are typically based on classifications such as ICD-10 and then also had been used for documentation. Leveraging this information automatically is subject of this paper - i.e. enabling an automatic mapping from ICD-10 to SNOMED CT. Because this mapping provides a large set of SNOMED codes for each ICD-10 concept, the approach is non-trivial. In order to pick the best possible code, we propose to take advantage of the hierarchical structure of the SNOMED system to find the concept which lies closer to all candidates in the target system. In other words, our algorithm searches the lowest common ancestor (LCA) of all candidates. For evaluation, we studied 1692 codes from a real-world dataset. The results are promising and show that the proposed approach achieves good results in the majority of cases. © 2022 IEEE.

关键词： Classification (of information)

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LiDAR-based localization and automatic control of UAVs for mobile remote reconnaissance 18

LiDAR-based localization and automatic control of UAVs for m...

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Electro-Optical and Infrared systems: Technology and Applications XVIII and Electro-Optical Remote Sensing XV 2021

作者： Hammer, Marcus Borgmann, Björn Hebel, Marcus Arens, Michael Fraunhofer IOSB Ettlingen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Gutleuthausstr. 1 Ettlingen76275 Germany

ISBN: (数字)9781510645776

ISBN: (纸本)9781510645769

Sensor-based monitoring of the surroundings of civilian vehicles is primarily relevant for driver assistance in road traffic, whereas in military vehicles, far-reaching reconnaissance of the environment is crucial for accomplishing the respective mission. Modern military vehicles are typically equipped with electro-optical sensor systems for such observation or surveillance purposes. However, especially when the line-of-sight to the onward route is obscured or visibility conditions are generally limited, more enhanced methods for reconnaissance are needed. The obvious benefit of micro-drones (UAVs) for remote reconnaissance is well known. The spatial mobility of UAVs can provide additional information that cannot be obtained on the vehicle itself. For example, the UAV could keep a fixed position in front and above the vehicle to gather information about the area ahead, or it could fly above or around obstacles to clear hidden areas. In a military context, this is usually referred to as manned-unmanned teaming (MUM-T). In this paper, we propose the use of vehicle-based electro-optical sensors as an alternative way for automatic control of (cooperative) UAVs in the vehicle's vicinity. In its most automated form, the external control of the UAV only requires a 3D nominal position relative to the vehicle or in absolute geocoordinates. The flight path there and the maintaining of this position including obstacle avoidance are automatically calculated on-board the vehicle and permanently communicated to the UAV as control commands. We show first results of an implementation of this approach using 360° scanning LiDAR sensors mounted on a mobile sensor unit. The control loop of detection, tracking and guidance of a cooperative UAV in the local environment is demonstrated by two experiments. We show the automatic LiDAR-controlled navigation of a UAV from a starting point A to a destination point B. with and without an obstacle between A and B. The obstacle in the direct path

关键词： Unmanned aerial vehicles (UAV)

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Best-of-Breed: Service-Oriented Integration of Artificial Intelligence in Interoperable Educational Ecosystems 11th

Best-of-Breed: Service-Oriented Integration of Artificial In...

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11th International Workshop on Learning Technology for Education Challenges, LTEC 2023

作者： Krauss, Christopher Streicher, Alexander Poxleitner, Eva Altun, Daniela Mueller, Joanna An, Truong-Sinh Mueller, Christoph Fraunhofer Institute for Open Communication Systems FOKUS Kaiserin-Augusta-Allee 31 Berlin10589 Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Fraunhoferstraße 1 Karlsruhe76131 Germany Fraunhofer Academy Hansastraße 27c Munich80686 Germany Fraunhofer Institute for Communication Information Processing and Ergonomics FKIE Fraunhoferstraße 20 Wachtberg53343 Germany

ISBN: (纸本)9783031347535

Artificial Intelligence (AI) offers great potential for optimizing learning processes, teaching methods, learning content, or organizational procedures. However, the success of AI components in educational environments is by no means guaranteed and depends on several conditions in their respective learning settings. In this article, we analyze requirements that are often addressed prior to introducing AI features. We address organizational, methodological, didactical, content-related, and technical challenges. The research question of this work is how AI features can best be incorporated into modern educational system landscapes to create sustainable system architectures that are accepted and perceived as added value by users. Thereby, the article discusses two approaches to software architecture: Best-of-Suite (for monolithic architectures) and Best-of-Breed (for service-oriented architectures). Monolithic systems offer a wide range of functions, can be offered by a single provider but can become difficult to manage and create dependencies. Specialized and service-oriented systems, in turn, consist of modular functions handled by specialized services, are more flexible and scalable, and can be integrated with a wide range of tools and services, but require more effort to set up and manage. We explain why the Best-of-Breed strategy is a sensible approach to the use of AI components, how this can be implemented sustainably with the help of a middleware component, and we report on the user experiences from a field test. While in this work we evaluate the implemented system with a cybersecurity training as an on-the-job course, the middleware has been successfully used in other educational contexts, as well. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Interoperability

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Efficient Global Occupancy Mapping for Mobile Robots using OpenVDB

arXiv

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arXiv 2022年

作者： Hagmanns, Raphael Emter, Thomas Besselmann, Marvin Grosse Beyerer, Jürgen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Karlsruhe Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation Karlsruhe Germany FZI Forschungszentrum Informatik Karlsruhe Germany

In this work we present a fast occupancy map building approach based on the VDB datastructure. Existing log-odds based occupancy mapping systems are often not able to keep up with the high point densities and framerates of modern sensors. Therefore, we suggest a highly optimized approach based on a modern datastructure coming from a computer graphic background. A multithreaded insertion scheme allows occupancy map building at unprecedented speed. Multiple optimizations allow for a customizable tradeoff between runtime and map quality. We first demonstrate the effectiveness of the approach quantitatively on a set of ablation studies and typical benchmark sets, before we practically demonstrate the system using a legged robot and a UAV. © 2022, CC BY.

关键词： Mapping

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Fundamental Limitation Caused by Optical Propagation: Issues Relevant to Implement “All-Optical” technologies for Global Connectivity Using Space, Terrestrial and Underwater Communications

Fundamental Limitation Caused by Optical Propagation: Issues...

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International Symposium on Communication systems, Networks and Digital Signal Processing, CNSDSP

作者： Arun K. Majumdar Italo Toseli Consulting Research Scientist “Former” Adjunct Professor CSU_Pueblo San diego USA System Technologies and Image Exploitation Fraunhofer Institute of Optronics Fraunhofer IOSB Ettlingen Germany

ISBN: (数字)9798350348743

ISBN: (纸本)9798350348750

This paper presents a new architecture and novel technology for 3D optical networks in order to establish global communications and connectivity incorporating both entirely atmosphere and underwater terminals. The method is based on using adaptive optics (AO)-based transceivers for mitigating turbulence, and serial relay communications between small satellites, cluster of HAPs space terminals, and the ground (as well as underwater) terminal. Fundamental limitations caused by optical propagation through long slant paths associated with anisotropic and non-Kolmogorov turbulence, and their effects on optical communication design and performance are also addressed.

关键词： Underwater communication Three-dimensional displays Optical propagation Terrestrial atmosphere Small satellites Optical fiber networks Transceivers

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Human-Machine Interactions for on the Fly Free Text Input Processing 3rd

Human-Machine Interactions for on the Fly Free Text Input Pr...

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3rd International Conference on Human systems Engineering and Design: Future Trends and Applications, IHSED 2020

作者： Dyck, Sergius Hoffmann, Almuth Fraunhofer Institute of Optronics System Technologies Image Exploitation IOSB Fraunhoferstr. 1 Karlsruhe76131 Germany

ISBN: (纸本)9783030582814

Information in machine-readable form has to be entered by users of information systems in various situations. In the input process, forms with a number of individual fields are commonly used. For the creator, this input can be cumbersome and potentially counterintuitive. For this reason, free text components are becoming increasingly important. Information in the form of free text is not machine-readable as such, and extracting information is complex. The accuracy of existing approaches used to extract information from free text components depends directly on the quality of the used internal model. These models have to be created by experts or trained in advance using the appropriate machine learning methods. The main objective presented in this paper is extracting information as well as enhancing the underlying model on the fly. This implies that the information extraction algorithms apply directly to the user’s input and that the user is enabled to correct possible deficiencies in the model as they arise. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Text processing

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The GOOSE Dataset for Perception in Unstructured Environments

The GOOSE Dataset for Perception in Unstructured Environment...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Peter Mortimer Raphael Hagmanns Miguel Granero Thorsten Luettel Janko Petereit Hans-Joachim Wuensche Institute for Autonomous Systems Technology University of the Bundeswehr Munich Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation Karlsruhe Germany Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The potential for deploying autonomous systems can be significantly increased by improving the perception and interpretation of the environment. However, the development of deep learning-based techniques for autonomous systems in unstructured outdoor environments poses challenges due to limited data availability for training and testing. To address this gap, we present the German Outdoor and Offroad Dataset (GOOSE), a comprehensive dataset specifically designed for unstructured outdoor environments. The GOOSE dataset incorporates 10000 labeled pairs of images and point clouds, which are utilized to train a range of state-of-the-art segmentation models on both image and point cloud data. We open source the dataset, along with an ontology for unstructured terrain, as well as dataset standards and guidelines. This initiative aims to establish a common framework, enabling the seamless inclusion of existing datasets and a fast way to enhance the perception capabilities of various robots operating in unstructured environments. This framework also makes it possible to query data for specific weather conditions or sensor setups from a database in future. The dataset, pre-trained models for offroad perception, and additional documentation can be found at https://***/.

关键词： Point cloud compression Training Autonomous systems Navigation Semantic segmentation Ontologies Robot sensing systems

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Quantum-inspired exoplanet detection in the presence of experimental imperfections

arXiv

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arXiv 2025年

作者： Linowski, Tomasz Schlichtholz, Konrad Sorelli, Giacomo International Centre for Theory of Quantum Technologies University of Gdansk Gdańsk80-308 Poland Fraunhofer IOSB Ettlingen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Gutleuthausstr. 1 Ettlingen76275 Germany

Ideal spatial demultiplexing (SPADE) is proven to be a quantum-optimal tool for exoplanet detection, i.e., asymmetric source discrimination. However, recent investigations into the related problems of separation estimation and symmetric source discrimination showed its efficiency to be limited in the presence of noise. In this work, we use analytical tools to scrutinize the practical applicability of SPADE and derive the associated optimal decision strategy for exoplanet detection in the presence of experimental imperfections. On the one hand, we find that the probability of detection of noisy SPADE has the same scaling with planet-star separation and relative brightness as conventional techniques, such as direct imaging and coronagraphs. On the other hand, we prove that, due to a superior scaling coefficient under realistic noise conditions, SPADE remains the most efficient method for practical exoplanet detection in the sub-Rayleigh regime. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimal detection

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Concept and Design of a Prototype Autonomous, Modular Subsea Bottom Station

Concept and Design of a Prototype Autonomous, Modular Subsea...

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OCEANS

作者： Louis Leon Rautmann Jan Witte Thomas Emter Minhaz Ahmed Burkhard Rieck Helge Renkewitz Sascha Kosleck Dept. of Ocean Engineering University of Rostock Rostock Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Karlsruhe Germany Fraunhofer Institute for Silicon Technology ISIT Itzehoe Germany Evologics GmbH Berlin Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB-AST Ilmenau Germany

This paper deals with the introduction of a novel concept of a customisable, modular and autonomous subsea bottom station (BASE). The subsea station will be supported by a floating surface unit ensuring cloud data connection as well as including MEMS-based energy harvesting and spaces for additional sensors. The concept not only serves as a long-term survey station or lander in the classical sense. It shall also operate as the main hub for a future underwater communications network and as a service station for underwater vehicles, such as AUVs, operating in the area. This paper aims to introduce the overall system and to showcase concepts of selected subsystems.

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Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input

arXiv

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arXiv 2023年

作者： Wu, Chengzhi Pfrommer, Julius Zhou, Mingyuan Beyerer, Jürgen Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Germany Fraunhofer Center for Machine Learning Germany

We propose a combined generative and contrastive neural architecture for learning latent representations of 3D volumetric shapes. The architecture uses two encoder branches for voxel grids and multi-view images from the same underlying shape. The main idea is to combine a contrastive loss between the resulting latent representations with an additional reconstruction loss. That helps to avoid collapsing the latent representations as a trivial solution for minimizing the contrastive loss. A novel switching scheme is used to cross-train two encoders with a shared decoder. The switching scheme also enables the stop gradient operation on a random branch. Further classification experiments show that the latent representations learned with our self-supervised method integrate more useful information from the additional input data implicitly, thus leading to better reconstruction and classification performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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