AUSPEX: An integrated open-source decision-making framework for UAVs in rescue missions.

Unmanned aerial vehicles (UAVs) have become paramount for search and rescue (SAR) missions due to their ability to access hazardous and challenging environments and to rapidly provide cost-effective aerial situational awareness. Nevertheless, current UAV systems are designed for specific tasks, often focusing on benchmarking use cases. Therefore, they offer limited adaptability for the diverse decision-making demands of SAR missions.

Investigating the Sim-to-Real Generalizability of Deep Learning Object Detection Models.

State-of-the-art object detection models need large and diverse datasets for training. As these are hard to acquire for many practical applications, training images from simulation environments gain more and more attention. A problem arises as deep learning models trained on simulation images usually have problems generalizing to real-world images shown by a sharp performance drop.

Association of Unc-51-like Kinase 4 () with the reactivity of the extended reward system in response to conditioned stimuli.

Objectives: is an established candidate gene for mental disorders and antipsychotic treatment response. We investigated the association of functional genetic variation at the locus with the human extended dopaminergic reward system using fMRI during the performance of a well-established reward paradigm.

Methods: Two hundred and thirty-four patients were included in this study.

Filling the Gaps: Using Synthetic Low-Altitude Aerial Images to Increase Operational Design Domain Coverage.

A key necessity for the safe and autonomous flight of Unmanned Aircraft Systems (UAS) is their reliable perception of the environment, for example, to assess the safety of a landing site. For visual perception, Machine Learning (ML) provides state-of-the-art results in terms of performance, but the path to aviation certification has yet to be determined as current regulation and standard documents are not applicable to ML-based components due to their data-defined properties. However, the European Union Aviation Safety Agency (EASA) published the first usable guidance documents that take ML-specific challenges, such as data management and learning assurance, into account.

Measuring and Predicting Sensor Performance for Camouflage Detection in Multispectral Imagery.

To improve the management of multispectral sensor systems on small reconnaissance drones, this paper proposes an approach to predict the performance of a sensor band with respect to its ability to expose camouflaged targets under a given environmental context. As a reference for sensor performance, a new metric is introduced that quantifies the visibility of camouflaged targets in a particular sensor band: the Target Visibility Index (TVI). For the sensor performance prediction, several machine learning models are trained to learn the relationship between the TVI for a specific sensor band and an environmental context state extracted from the visual band by multiple image descriptors.

Deep Learning Based Vehicle Detection on Real and Synthetic Aerial Images: Training Data Composition and Statistical Influence Analysis.

The performance of deep learning based algorithms is significantly influenced by the quantity and quality of the available training and test datasets. Since data acquisition is complex and expensive, especially in the field of airborne sensor data evaluation, the use of virtual simulation environments for generating synthetic data are increasingly sought. In this article, the complete process chain is evaluated regarding the use of synthetic data based on vehicle detection.

Ball bearing vibration data for detecting and quantifying spall faults.

Ball bearings are essential components of electromechanical systems, and their failures significantly affect the service lifetime of these systems. For highly reliable and safety-critical electromechanical systems in energy and aerospace sectors, early bearing fault detection and quantification are crucial. The vibration measurements of bearing fatigue faults, i.

Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results.

UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection.

A combined optimization-simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities.

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated for potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations.

Aspects of yaw control design of an aircraft with distributed electric propulsion.

Distributed electric propulsion (DEP) offers new options in aircraft design. Besides the optimization of the wing, another area of optimization is the vertical tail plane (VTP) and yaw control. The large number of engines significantly relaxes the one-engine-inoperative (OEI) case during take-off, which is mostly the sizing case for the VTP.

Multipath-Assisted Radio Sensing and State Detection for the Connected Aircraft Cabin.

Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger localization and occupancy detection can be monitored using location-aware communication systems, also known as wireless sensor networks.

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites.

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning.